Question 1

What is ApifyForge?

Accepted Answer

ApifyForge is a free, independent platform for discovering, comparing, and managing Apify web scraping actors. It provides quality scores, cost estimates, side-by-side comparisons, and 14 developer tools — all at no charge. ApifyForge is not affiliated with Apify; it is built by an independent developer who manages 250+ actors on the Apify Store.

Question 2

Does ApifyForge cost money?

Accepted Answer

No. ApifyForge itself is completely free to use. The actors you run use standard Apify pay-per-event pricing charged through your own Apify account. Most actors cost $0.05–$0.50 per result. Apify offers a free tier with $5 of monthly credits, so you can try actors without paying anything.

Question 3

Is ApifyForge safe to use?

Accepted Answer

Yes. ApifyForge uses OAuth to connect to your Apify account — your API token is stored in an encrypted server-side session and is never exposed to the browser. ApifyForge can only read your actor data and trigger runs; it cannot delete actors or modify your account settings. You can disconnect at any time.

Question 4

How is ApifyForge different from the Apify Store?

Accepted Answer

The Apify Store is where actors are published and run. ApifyForge adds a layer on top: quality scores, cost calculators, side-by-side comparisons, developer tools (schema validator, test runner, regression suite), and portfolio analytics. Think of ApifyForge as a dashboard and toolkit for getting more out of the Apify ecosystem.

Question 5

Can I use ApifyForge without an Apify account?

Accepted Answer

Yes. You can browse the actor directory, read comparisons, use the glossary, and explore learn guides without an account. You only need to connect an Apify account to use the dashboard, run actors through developer tools, or see your portfolio analytics.

Question 6

How many actors does ApifyForge track?

Accepted Answer

ApifyForge currently indexes over 250 Apify actors across categories including lead generation, SEO tools, compliance screening, ecommerce intelligence, cybersecurity, financial intelligence, and social media. New actors are added regularly.

Question 7

What is the best Apify actor for lead generation?

Accepted Answer

It depends on your use case. For local business leads from Google Maps, use Google Maps Email Extractor. For enriched leads with quality scores, use B2B Lead Gen Suite. For contact enrichment from company websites, use Website Contact Scraper. Compare them side-by-side on our comparison page at apifyforge.com/compare/lead-generation.

Question 8

How much does web scraping cost with Apify?

Accepted Answer

Most Apify actors use pay-per-event pricing, typically $0.05–$0.50 per result. For example, scraping 100 business contacts from Google Maps costs about $15. Apify includes a free tier with $5/month of credits. Use our cost calculator at apifyforge.com/tools/cost-calculator to estimate costs before running any actor.

Question 9

Is web scraping legal?

Accepted Answer

Web scraping of publicly available data is generally legal in the US and EU, based on the HiQ v. LinkedIn ruling and similar precedents. However, you must respect robots.txt directives, terms of service, and data protection laws like GDPR and CCPA. Do not scrape personal data without a lawful basis, and never use scraped data for spam or harassment.

Question 10

What are the developer tools on ApifyForge?

Accepted Answer

ApifyForge provides 14 free developer tools: Cost Calculator, Schema Validator, Test Runner, Cloud Staging, Schema Diff, Regression Suite, MCP Debugger, LLM Optimizer, Compliance Scanner, Input Tester, Proxy Analyzer, Plan Advisor, Pipeline Builder, and Schema Registry. All tools run on your Apify account using standard pay-per-event pricing.

Question 11

Can I compare Apify actors side by side?

Accepted Answer

Yes. ApifyForge has 20 comparison pages covering lead generation, contact scrapers, review scrapers, compliance screening, SEO tools, ecommerce intelligence, security tools, and more. Each comparison shows feature matrices, pricing, best-for recommendations, and a recommender link. Browse all comparisons at apifyforge.com/compare.

Question 12

How do I choose the right Apify actor?

Accepted Answer

Start with the actor recommender at apifyforge.com/recommend — describe your use case and get personalized suggestions. You can also browse by category in the actor directory, use comparison pages to evaluate similar actors, and check quality scores and maintenance pulse data on each actor detail page.

Question 13

What is pay-per-event pricing on Apify?

Accepted Answer

Pay-per-event (PPE) is Apify's pricing model where you pay a fixed amount per result — for example, $0.15 per business contact extracted. Platform compute costs are included in the price. You can set spending limits per run, and unused credits carry over. This is different from time-based pricing where you pay for compute time regardless of results.

Question 14

Can I schedule Apify actors to run automatically?

Accepted Answer

Yes. The Apify platform supports scheduled runs at any interval — hourly, daily, weekly, or custom cron expressions. You can also trigger runs via the API from Python, JavaScript, or any HTTP client. ApifyForge does not manage schedules directly, but actors run on your Apify account where scheduling is built in.

Question 15

Does ApifyForge work with AI agents and MCP?

Accepted Answer

Yes. ApifyForge tracks 80+ MCP (Model Context Protocol) servers that expose Apify actors as tools for AI agents. The MCP Debugger tool lets you test MCP server tool calls. Actors can be integrated with LangChain, LlamaIndex, and other AI frameworks via the Apify integration libraries.

Question 16

How do I export data from Apify actors?

Accepted Answer

Actor results are stored in Apify Datasets. You can download data as JSON, CSV, Excel, or XML from the Apify Console. You can also access results programmatically via the Apify API, or push data directly to Google Sheets, HubSpot, Slack, or any webhook endpoint using Apify integrations.

Question 17

What happens if an Apify actor fails?

Accepted Answer

When an actor fails, Apify logs the error in the run details. Common causes include website blocks (use proxy configuration), timeouts (increase memory or timeout settings), and input validation errors. ApifyForge's health monitor tracks failure rates across your portfolio. Check our debugging guide at apifyforge.com/help/how-do-i-debug-a-failed-apify-actor-run.

Question 18

Can I use Apify actors with Python?

Accepted Answer

Yes. The official apify-client Python package lets you trigger actor runs, read datasets, and manage your account from Python. Install with pip install apify-client. ApifyForge's API code examples show Python, JavaScript, and cURL for every actor.

Question 19

How does ApifyForge work?

Accepted Answer

ApifyForge is a free developer dashboard that connects to your Apify account via API token and gives you fleet-level management, analytics, and developer tools for all your actors in one place. When you use any feature — whether that is fleet analytics, revenue tracking, schema validation, or regression testing — ApifyForge calls the Apify API on your behalf using the token you provide during setup. Any actor runs triggered through ApifyForge execute on your own Apify account, and you pay Apify directly for those runs through their standard Pay Per Event (PPE) pricing model. ApifyForge itself charges nothing.

Here is how the typical workflow looks in practice. First, you connect your Apify account by pasting your API token in the Settings page. Within a few minutes, your dashboard populates with data about every actor in your account: run history, success rates, revenue figures, quality scores, and Store rankings. From there, you can use the Schema Validator to check your actor output against its declared dataset schema, run regression tests across your entire fleet with the Test Runner, track PPE revenue trends over time, and perform bulk metadata updates that would take hours in the Apify Console.

The reason ApifyForge exists is that the standard Apify Console is designed to manage actors one at a time. That works fine when you have five actors, but becomes unsustainable at scale. Developers managing portfolios of 20, 50, or 200+ actors need fleet-level visibility: which actors are earning the most, which ones are failing, which ones have schema violations that could trigger maintenance flags, and where the growth opportunities are. ApifyForge provides that visibility in a single dashboard. To get started, visit apifyforge.com and connect your Apify account — setup takes under two minutes. If you are wondering about costs, see the related question about whether ApifyForge is free, or check our PPE pricing breakdown at apifyforge.com/glossary/ppe for details on how actor run costs work.

Question 20

Is ApifyForge free?

Accepted Answer

Yes, ApifyForge is completely free to use with no subscription fees, no premium tiers, and no feature gates. Every feature — the dashboard, fleet analytics, schema validator, test runner, regression testing, revenue tracking, quality score monitoring, and bulk metadata tools — is available to every user at no cost. You will never be asked to upgrade or pay ApifyForge for anything.

The only costs you incur are standard Apify platform charges when actor runs are triggered. For example, if you use the cloud staging feature to test an actor in Apify's production environment, that run consumes credits on your Apify account just like any other actor run. These costs are typically very small — under $0.01 per test run in most cases. Each tool page in ApifyForge displays the exact cost breakdown so you always know what a particular action will cost before you run it. Local tools like the Schema Validator and the local Test Runner make zero API calls and cost nothing at all.

ApifyForge's business model is straightforward: we help Apify developers build better actors, earn more PPE revenue, and manage larger portfolios. When developers succeed on the Apify platform, the ecosystem grows, and that benefits everyone. We do not monetize your data, display ads, or sell access to premium features. The tool you see on day one is the same tool available to developers managing 200+ actor fleets.

To get started, all you need is an Apify account with an API token. The Apify free tier includes $5 of monthly platform credits, which is more than enough to explore ApifyForge's features and run your first tests. Visit apifyforge.com to connect your account. If you are curious about how Apify's own pricing works separately from ApifyForge, see the related question about how much Apify costs, or visit apifyforge.com/glossary/ppe for a detailed breakdown of Pay Per Event pricing.

Question 21

Do I need an Apify account?

Accepted Answer

Yes, you need an Apify account with an API token to use ApifyForge. ApifyForge is purpose-built for Apify developers who build, publish, and manage actors on the Apify platform, so a connected Apify account is required for every feature to function.

If you do not already have an Apify account, signing up is quick and free. Visit apify.com and create an account — the free tier includes $5 of monthly platform credits, 10 GB of data transfer, and full access to the Apify Store, API, and CLI. That is more than enough to start developing actors, running tests, and exploring the platform. Once your account is created, generate an API token from console.apify.com/account/integrations. We recommend creating a dedicated token specifically for ApifyForge rather than reusing your primary token, so you can revoke it independently if needed without affecting other integrations.

Here is how to connect step by step: First, sign up or log in at apify.com. Second, go to console.apify.com/account/integrations and click Create Token. Third, copy the token. Fourth, open apifyforge.com, go to Settings, paste the token, and click Save. Your dashboard will begin populating within a few minutes as ApifyForge pulls in your actor data, run history, and analytics.

Once you start publishing actors and users begin running them, you earn revenue through Apify's Pay Per Event (PPE) pricing system. For many active developers, this PPE revenue covers or exceeds their Apify subscription costs, making the platform effectively self-funding. ApifyForge helps you maximize that revenue by tracking earnings across your entire fleet, identifying your top performers, and flagging actors with quality issues that reduce Store visibility. If you are new to Apify and want to understand the cost structure before committing, see the related questions about how much Apify costs and whether Apify has a free tier.

Question 22

What makes this different from the Apify Console?

Accepted Answer

The fundamental difference is scope: the Apify Console manages individual actors one at a time, while ApifyForge manages your entire actor portfolio as a fleet with cross-cutting analytics, bulk operations, and developer tooling that does not exist in the Console.

The Apify Console is excellent for what it does. You can view an actor's settings, check its run history, browse its dataset output, and configure builds. But every action targets a single actor. If you want to check the success rate across 50 actors, you need to open 50 separate pages. If you want to update metadata on 20 actors, that is 20 sets of form submissions. If you want to know which of your actors earned the most PPE revenue last month, there is no aggregated view.

ApifyForge solves these fleet-scale problems with several key features. Fleet Analytics gives you a single dashboard showing revenue, success rates, run counts, and quality scores across every actor in your account. You can sort, filter, and drill into any metric. Revenue Tracking shows PPE earnings per actor over time with trend lines, so you can identify which actors are growing and which are declining. Quality Score Monitoring tracks the factors that affect Store visibility — schema compliance, success rate, metadata completeness, build freshness — and alerts you when any actor drops below threshold. Bulk Metadata Operations let you update titles, descriptions, categories, and SEO fields across multiple actors in a single action. The Schema Validator checks your actor output against its declared schema locally before you push, preventing the maintenance flags that reduce Store visibility. The Test Runner and Regression Tests give you pre-deploy confidence with automated test suites.

Think of it as the difference between managing one server with SSH and managing a fleet with a control plane like Kubernetes. Both have their place, but at scale you need the fleet tool. Most developers start using ApifyForge when they hit 10-15 actors and realize the Console workflow does not scale. Visit apifyforge.com to see the dashboard in action. For more on the specific tools available, see the related questions about the Schema Validator, Test Runner, and Cloud Staging.

Question 23

How much does Apify cost?

Accepted Answer

Apify uses a credit-based pricing model where you pay for the compute resources your actors consume, with plans starting from a free tier that includes $5 of monthly platform credits all the way up to enterprise plans for high-volume users.

Here is how the pricing tiers break down. The Free plan gives you $5 of platform credits per month, 10 GB of data transfer, and access to all core platform features including the Store, API, CLI, and web scraping tools. The Personal plan at $49 per month includes $49 of platform credits, 30-day data retention, higher concurrency, and priority support. The Team plan at $499 per month is designed for organizations with multiple users and includes $499 of credits, team management features, 90-day data retention, and dedicated support. Enterprise plans are custom-priced for large-scale operations.

Platform credits are consumed by compute time (CPU and memory usage during actor runs), data transfer, proxy bandwidth, and storage. The exact credit consumption depends on your actor's resource requirements. A lightweight Cheerio-based scraper processing 1,000 pages might cost $0.10 in credits, while a Playwright-based scraper rendering JavaScript on the same number of pages might cost $0.50 or more.

On top of platform credits, there is Pay Per Event (PPE) pricing. When users run actors that have PPE enabled, they pay per event — for example, per result returned, per page scraped, or per search performed. As an actor developer, you set the PPE price and earn revenue from every event your actors generate. This is where the economics get interesting: developers with 10 or more published actors often earn more in PPE revenue than they spend on their Apify subscription, making the platform effectively self-funding.

ApifyForge helps you track both sides of this equation with its revenue dashboard, showing your PPE earnings alongside your platform costs so you can see your net position at a glance. Visit apifyforge.com/glossary/ppe for a detailed breakdown of how Pay Per Event pricing works. For related information, see the questions about whether Apify has a free tier and how much you can earn with Apify actors.

Question 24

Is Apify free?

Accepted Answer

Yes, Apify offers a free tier that lets you develop actors, run scraping jobs, and explore the platform without paying anything. The free plan includes $5 of monthly platform credits, 10 GB of data transfer, and full access to the Apify Store, API, CLI, and all core platform features.

Here is what you can realistically do with the free tier. The $5 of monthly credits is enough to develop and test actors locally using the Apify CLI, run small to medium scraping jobs (a few thousand pages per month depending on complexity), experiment with different actor templates and frameworks (Cheerio, Playwright, Puppeteer), publish actors to the Apify Store, and access datasets and key-value stores for data management. You also get access to the full Apify API, which means ApifyForge works perfectly with a free account.

There are some limitations to be aware of. The free tier does not include residential proxies, which are essential for scraping websites with strong anti-bot protection like Amazon, LinkedIn, Google, and most social media platforms. If your actors target these sites, you will need a paid plan for proxy access. Data retention on the free tier is 7 days, meaning your datasets and run logs are automatically deleted after a week. Concurrency is also limited, so you cannot run many actors simultaneously.

For many developers, the free tier serves as a development and testing environment. You build and debug your actors on the free plan, then upgrade when you are ready to publish and scale. The Personal plan at $49 per month includes $49 of credits, 30-day data retention, residential proxy access, and higher concurrency limits — which is what most independent developers use for production.

One important note for actor developers: once you start publishing actors with Pay Per Event pricing, users who run your actors generate PPE revenue for you. This means the platform can become self-funding as your actor portfolio grows. Visit apifyforge.com/glossary/ppe for a detailed explanation of how PPE revenue works. For a full comparison of all pricing tiers, see the related question about how much Apify costs.

Question 25

What is the Apify Store?

Accepted Answer

The Apify Store is a public marketplace where developers publish actors (pre-built automation tools) and users discover, run, and pay for them. Think of it as an app store specifically for web scraping, data extraction, browser automation, and AI-powered data processing tools.

The Store contains thousands of actors organized by category — including scraping tools for specific websites (Amazon, LinkedIn, Google Maps, TikTok), general-purpose crawlers, data processing pipelines, AI integrations, and automation workflows. Users find actors through keyword search, category browsing, curated collections, and popularity rankings. Each actor has a dedicated Store page with a description, README documentation, pricing information, input schema, example output, and user reviews.

For developers, the Store is a monetization platform. When you publish an actor with Pay Per Event (PPE) pricing enabled, every user who runs your actor pays per event — per result returned, per page scraped, per search performed, or whatever unit you define. You set the price per event and earn revenue automatically. Apify handles all billing, payment processing, and user management. Your job is to build a useful actor, price it appropriately, and keep it running reliably.

Store visibility is determined by several ranking factors. Quality score is a composite metric based on your actor's success rate, schema compliance, metadata completeness, and build freshness. Usage volume measures how many users run your actor and how often. Metadata relevance determines how well your actor matches search queries based on its title, description, and README content. Actors with higher quality scores and more usage appear higher in search results and category pages.

ApifyForge helps you optimize for these ranking factors with quality score tracking, schema validation, metadata management, and Store analytics. You can see exactly which factors are dragging down your visibility and fix them before they affect your revenue. Visit apifyforge.com/glossary/apify-store for more details on how Store rankings work. For related topics, see the questions about how to get more users for your actor and how much you can earn with Apify actors.

Question 26

How do I connect my Apify account?

Accepted Answer

To connect your Apify account, go to the ApifyForge Settings page, paste your Apify API token into the token field, and click Save. The connection is validated immediately, and your dashboard begins populating with actor data within a few minutes.

Here is the complete step-by-step process. First, log in to your Apify account and navigate to console.apify.com/account/integrations. Click the Create Token button to generate a new API token. Give it a descriptive name like 'ApifyForge Dashboard' so you can identify it later. Copy the token — you will only see the full token once, so make sure you copy it before closing the dialog. Next, open apifyforge.com and go to the Settings page. Paste the token into the API Token field and click Save.

We strongly recommend creating a dedicated token specifically for ApifyForge rather than reusing your primary API token or a token shared with other integrations. The reason is simple: if you ever need to revoke ApifyForge's access, you can delete just that token without breaking your CI/CD pipelines, monitoring scripts, or other tools that use different tokens. Apify allows you to create multiple tokens per account at no extra cost.

Once connected, ApifyForge begins pulling data from the Apify API: your actor list, run history, build information, dataset metadata, and usage statistics. The initial data population typically takes two to five minutes depending on how many actors you have. After that, data refreshes automatically at regular intervals. You will see your fleet dashboard populate with success rates, revenue figures, quality scores, and Store ranking data.

If the connection fails, double-check that you copied the full token (they are long strings) and that the token has not expired. You can verify your token works by visiting console.apify.com/account/integrations and checking its status. For more details on what data ApifyForge can access with your token, see the related question about what data ApifyForge can access. If you run into issues, see the troubleshooting question about dashboards showing no data.

Question 27

What data can ApifyForge access?

Accepted Answer

ApifyForge can only access the data that the Apify API exposes for your account using your API token. This includes your actor list, run history and logs, dataset metadata, key-value store metadata, build information, and usage statistics. ApifyForge cannot access anything outside the Apify API scope.

Here is a specific breakdown of what is and is not accessible. ApifyForge CAN access: the list of actors in your account with their settings and metadata, run history including status, duration, and resource consumption for each run, dataset metadata such as item counts and schema information, key-value store listings, build logs and version history, and your account usage statistics and credit consumption. ApifyForge CANNOT access: your actor source code or repository contents, environment variables and secrets stored in your actor configuration, the actual content of your dataset items (we read metadata only, not your scraped data), proxy passwords or proxy configuration details, billing information or payment methods, or any data from other Apify users' accounts.

This distinction matters for security. Your source code, environment variables, and scraped data never leave Apify's servers. ApifyForge computes analytics — revenue trends, success rates, quality scores, fleet health metrics — from the metadata available through the API. These computed analytics are cached temporarily in our database to keep the dashboard responsive, but the raw data is not stored permanently. Cache is refreshed at regular intervals and automatically cleared when you disconnect your account.

The Apify API token you provide determines the exact permission scope. Standard tokens provide read and write access to your account resources. If Apify introduces more granular token scopes in the future, you could further restrict what ApifyForge can do. For now, we recommend using a dedicated token as described in the related question about how to connect your Apify account. For information about how your token is stored and protected, see the question about API token storage.

Question 28

Do you store my API token?

Accepted Answer

Yes, your API token is stored in our database, but it is encrypted at rest using AES-256 encryption — the same encryption standard used by banks, government agencies, and major cloud providers. The token is never stored in plaintext, never logged in application logs, never shared with third parties, and never transmitted anywhere except to the Apify API over HTTPS.

Here is how the security architecture works. When you enter your API token in ApifyForge Settings, it is encrypted using AES-256 before being written to the database. The encryption key is stored separately from the database in a secure key management system, meaning that even if someone gained access to the database directly, they could not decrypt your token without the separate encryption key. When ApifyForge needs to make an Apify API call on your behalf, the token is decrypted in memory, used for the API request, and never written to disk in decrypted form.

You maintain full control over your token at all times. If you want to revoke ApifyForge's access, you have two options. First, you can go to ApifyForge Settings and click Disconnect, which deletes the encrypted token from our database immediately. Second, you can go to your Apify account at console.apify.com/account/integrations and delete the token directly, which invalidates it everywhere instantly — ApifyForge will no longer be able to make API calls with that token.

As a security best practice, we recommend several measures. Create a dedicated API token specifically for ApifyForge rather than reusing tokens from other integrations. Rotate your tokens periodically — every 90 days is a reasonable cadence. If you suspect any security issue, revoke the token immediately from your Apify account settings and generate a new one. Monitor your Apify account's API call logs for any unexpected activity.

For more details on the security boundaries, see the related question about what data ApifyForge can access. To learn how to disconnect and remove your token entirely, see the question about how to disconnect your account.

Question 29

How do I disconnect my account?

Accepted Answer

To disconnect your Apify account from ApifyForge, go to the Settings page and click the Disconnect button. This immediately deletes your encrypted API token from our database and clears all cached analytics data associated with your account. The disconnection takes effect instantly — no waiting period, no data retention, no lingering access.

Here is exactly what happens when you disconnect. Your encrypted API token is permanently deleted from our database. All cached analytics data — revenue figures, success rates, quality scores, fleet metrics — is purged. Your ApifyForge dashboard resets to its initial empty state. No API calls can be made to your Apify account from ApifyForge after this point. Your actors, runs, datasets, and everything on the Apify platform itself are completely unaffected — disconnecting from ApifyForge has zero impact on your Apify account.

There is an alternative approach if you prefer. Instead of disconnecting through ApifyForge, you can revoke the API token directly from your Apify account at console.apify.com/account/integrations. Simply find the token you created for ApifyForge and delete it. This invalidates the token everywhere instantly, which means ApifyForge can no longer make API calls even though the encrypted (now-useless) token remains in our database until you formally disconnect. For complete cleanup, we recommend doing both: revoke the token on Apify's side first, then disconnect in ApifyForge Settings.

Reconnecting later is straightforward. Go to Settings, enter a new API token, and click Save. Your dashboard will repopulate within a few minutes with fresh data from the Apify API. No previous cached data is restored — everything is pulled fresh. This means there is no penalty for disconnecting and reconnecting; you get a clean start every time.

If you are disconnecting because of a security concern, revoke the token on Apify's side immediately at console.apify.com/account/integrations rather than relying solely on the ApifyForge disconnect button. For more on token security, see the related question about how your API token is stored.

Question 30

What does the Revenue dashboard show?

Accepted Answer

The Revenue dashboard shows PPE earnings across your entire actor portfolio with per-actor breakdown and trend lines. It is powered by the Revenue Tracker actor ($0.40 per sync) and the Cost Watchdog actor ($0.25 per check).

When you click Run Revenue Sync, ApifyForge runs the Revenue Tracker actor on your Apify account. It pulls PPE revenue data for every actor in your portfolio, calculates per-actor earnings, identifies your top performers, and shows trend lines so you can see which actors are growing and which are declining. The Cost Watchdog section shows compute spending — what you pay Apify for platform credits — alongside your revenue so you can see your net position.

Key metrics include total PPE revenue, revenue per actor, revenue trend (growing/declining/stable), compute costs, and revenue-to-cost ratio. The dashboard caches results so you can view your last revenue report without paying again. Most developers run a revenue sync weekly or after significant changes to their portfolio.

For strategies on maximizing revenue, see the related questions about how much you can earn with Apify actors and how to get more users. Visit the Learn section at apifyforge.com/learn/monetization for detailed monetization guides.

Question 31

What does the Health dashboard show?

Accepted Answer

The Health dashboard monitors actor failure rates, success trends, and deprecation signals across your fleet. It is powered by two actors: the Health Monitor ($0.25 per check) and the Deprecation Monitor ($0.25 per scan).

The Health Monitor checks every actor in your account and reports: overall fleet health as a percentage, per-actor success rates with color-coded status (healthy above 99%, warning 90-99%, critical below 90%), failed run counts with trend indicators, and specific recommendations for actors with issues. The Deprecation Monitor scans for actors approaching maintenance flags — stale builds, declining success rates, schema violations — and warns you before Apify's automated system flags them.

Actors are sorted by failure rate so the most critical issues appear first. Each actor row shows a success rate bar, failed/total run counts, status badge, trend indicator, and actionable recommendations. This view replaces manually checking 50+ actors in the Apify Console one at a time.

Run a health check whenever you suspect issues, or schedule weekly checks to catch problems before they trigger maintenance flags. For debugging specific failures, see the related questions about what happens when an actor fails and how to debug failed runs.

Question 32

What does the Quality dashboard show?

Accepted Answer

The Quality dashboard scores your actors on the factors that affect Apify Store visibility and ranks. It is powered by three actors: the Quality Monitor ($0.25 per scan), the Output Completeness Monitor ($0.25 per scan), and the A/B Tester ($0.50 per test).

The Quality Monitor evaluates each actor on: README completeness, pricing configuration, dataset schema compliance, input schema quality, build freshness, and overall reliability. Each factor gets a score and the dashboard shows which specific issues are dragging down your Store ranking. The Output Completeness Monitor tracks output volume over time — if an actor that normally returns 50 results starts returning 10, it flags the decline before users notice. The A/B Tester lets you compare two actors side by side with identical inputs to see which produces better output.

The quality score directly affects how high your actors appear in Apify Store search results. A score of 90+ puts you on page one. Below 70 means your actor is effectively invisible. The Quality dashboard shows exactly which factors to fix for the biggest ranking improvement.

For strategies on improving your Store ranking, see the related questions about getting more users for your actor and why actors get flagged for maintenance. Visit apifyforge.com/learn/store-seo for the full SEO optimization guide.

Question 33

What does the Competitors dashboard show?

Accepted Answer

The Competitors dashboard analyzes rival actors in your categories and identifies market opportunities. It is powered by the Market Gap Finder actor ($0.40 per scan).

The Market Gap Finder scans the Apify Store for actors in your categories and reports: which niches are underserved (few actors, high demand), which actors compete with yours (same keywords, same categories), pricing comparison (how your PPE prices compare to competitors), and growth opportunities (categories with growing user demand but few quality actors).

This intelligence helps you make strategic decisions: should you lower prices to compete, raise prices because you offer better quality, build a new actor in an underserved niche, or improve metadata to outrank competitors in search. The dashboard presents findings as actionable insights rather than raw data.

For strategies on pricing competitively, see the related question about how much you can earn with Apify actors. For improving your Store ranking, see the question about getting more users. Visit apifyforge.com/learn/store-seo for Store SEO strategies.

Question 34

What does the Failures dashboard show?

Accepted Answer

The Failures dashboard tracks which actors gained new failures since your last check. It is powered by the Failure Tracker actor ($0.10 per report).

Unlike the Health dashboard which shows 30-day aggregate success rates, the Failures dashboard answers a specific question: what broke since yesterday? It works by taking snapshots of every actor's public run statistics (which include all users' runs, not just yours) and comparing them between runs. The first run creates a baseline. Every run after that shows the delta — exactly which actors gained new FAILED, ABORTED, or TIMED-OUT runs.

This solves a real problem: the Apify Console shows a daily bar chart with total failures, but it does not tell you which actors failed. The Apify API does not expose individual runs by other users on your actors. The Failure Tracker works around this limitation by diffing the 30-day aggregate stats, which do include all users.

The dashboard shows four key metrics: new failures since last snapshot, actors affected, new runs since last snapshot, and healthy actors with zero new failures. Below that, each affected actor is listed with color-coded badges showing how many failures, aborts, and timeouts it gained, plus its 30-day success rate for context.

For best results, run the Failure Tracker daily. You can also configure a webhook URL in the input to receive Slack or Discord alerts when failures are detected. For diagnosing specific failures, use the Health dashboard which provides per-actor recommendations. For preventing failures, see the related questions about what happens when actors fail and how to debug failed runs.

Question 35

What are Bulk Operations?

Accepted Answer

Bulk Operations let you update metadata, pricing, categories, or trigger builds across multiple actors in a single action. This is the fleet-management feature that makes ApifyForge essential for developers with 10+ actors.

Without bulk operations, updating the description on 30 actors means opening 30 actor pages in the Apify Console, editing the field, and saving — a process that takes hours. With ApifyForge, you select the actors, enter the new values, and apply the change across all of them in seconds.

Common bulk operations include: updating categories when Apify adds new options (like when MCP_TOOLS was introduced), adjusting PPE pricing across a category of actors, setting consistent SEO metadata (seoTitle, seoDescription) across your fleet, updating Dockerfile base images when Apify releases new versions, and triggering rebuilds after dependency updates.

Bulk operations include a preview mode that shows what would change before applying. This is especially important for pricing changes, which trigger Apify's 14-day notice period for increases. Always preview before applying bulk pricing changes.

For strategies on managing large actor portfolios, visit apifyforge.com/learn/managing-multiple-actors.

Question 36

What are Alerts?

Accepted Answer

Alerts notify you when important events happen across your actor fleet — failures, revenue milestones, new users, cost spikes, and maintenance flags — so you know what happened without checking the dashboard.

ApifyForge can send alerts for several event types. Failure alerts trigger when an actor's success rate drops below a threshold you set (default: 90%). Revenue alerts notify you of significant changes — a daily revenue spike or drop of more than 20% compared to the previous period. Maintenance alerts fire when Apify flags any of your actors for maintenance. Cost alerts trigger when your daily compute spending exceeds a threshold. New user alerts notify you when a new user runs one of your actors for the first time.

Alerts help you catch problems before they compound. A failure alert on Monday means you fix the issue before the success rate drops enough to trigger a maintenance flag on Thursday. A revenue drop alert means you investigate immediately rather than discovering the problem at the end of the month.

Configure alerts in the Alerts page under the Operations section of the dashboard sidebar. You can set thresholds per alert type and choose your notification channel. For proactive monitoring strategies, see the related questions about what happens when actors fail and why actors get flagged for maintenance.

Question 37

What does the Schema Validator do?

Accepted Answer

The Schema Validator checks your actor's actual dataset output against the schema you declared in your dataset_schema.json file, catching mismatches before they trigger maintenance flags on the Apify Store. It is one of the most important pre-deploy tools available to Apify developers because schema violations are the number one cause of actors being flagged for maintenance, which directly reduces your Store visibility and revenue.

Here is what the Schema Validator checks for. Empty schemas — if your dataset_schema.json exists but defines no fields, the validator flags it immediately. Type mismatches — if your schema declares a field as a string but your actor outputs a number or array for that field, you get a detailed error showing the exact JSON path, the expected type, and the actual type. Undeclared fields — if your actor outputs fields that are not declared in your schema, these are flagged because they can cause issues for users who rely on the schema for data pipeline integration. Missing required fields — if your schema marks fields as required but your actor sometimes omits them, the validator catches this inconsistency.

The validation process works by comparing a sample of your actor's actual output data against the declared schema, field by field and type by type. The output report lists every violation with enough detail to fix it immediately: the field path (e.g., results[0].price.currency), what the schema expected (string), and what the actor actually produced (null). This makes fixing schema issues straightforward rather than requiring you to manually diff JSON structures.

To use the Schema Validator, run it locally before every push to your Apify actor repository. It executes in seconds and requires no API calls, so it is completely free. Integrate it into your development workflow as a pre-push check — many developers add it to a Git pre-push hook so schema violations are caught automatically before code reaches production. For related tools, see the questions about the Test Runner and Regression Tests, which provide additional pre-deploy validation. Visit apifyforge.com/tools/schema-validator for the full documentation and usage examples.

Question 38

What is the Test Runner?

Accepted Answer

The Test Runner is a testing tool that runs your Apify actor with predefined test inputs and automatically validates the output against assertions you define, giving you confidence that your actor works correctly before you deploy it to the Apify Store.

Each test case in the Test Runner consists of three parts: input parameters that define what to feed your actor, output assertions that define what you expect to get back, and optional metadata like test name and timeout. Input parameters can be any valid JSON that matches your actor's input schema — for example, a specific search query, a list of URLs, or configuration options. Output assertions let you specify expected minimum result counts (e.g., at least 10 results), required fields that must be present in every output item (e.g., every result must have a title, url, and price), field type constraints (e.g., price must be a number, not a string), and value range checks.

Here is a practical example. Say you have a Google Maps scraper. You might define three test cases: one that searches for 'coffee shops in London' and expects at least 5 results with name, address, and rating fields; one that searches for a specific place by URL and expects exactly 1 result with all detail fields populated; and one that uses an empty search to verify your actor handles edge cases gracefully without crashing.

The Test Runner supports two execution modes. Local mode runs your actor on your machine using the Apify CLI, which is free and fast — ideal for development iteration. Cloud mode triggers an actual actor run on the Apify platform, which costs standard PPE credits but tests in the real production environment where your actor will ultimately run. Cloud mode catches issues that local testing misses, such as Docker build problems, missing dependencies, and network-level differences.

Run all your test cases with a single command to get a complete pass/fail report. Integrate it with Regression Tests for automated pre-deploy validation across your entire test suite. For more on testing workflows, see the related questions about Cloud Staging and Regression Tests. Visit apifyforge.com/tools/test-runner for documentation and examples.

Question 39

What is Cloud Staging?

Accepted Answer

Cloud Staging runs your actor in Apify's actual production environment before you make it public on the Store, catching issues that local testing cannot detect. Think of it as a staging server specifically designed for Apify actors, integrated directly into your deployment pipeline.

The core problem Cloud Staging solves is the gap between your local development environment and Apify's production infrastructure. When you develop and test locally using the Apify CLI, your actor runs on your machine with your Node.js version, your installed packages, your network configuration, and your file system. When the same actor runs on Apify's cloud platform, it runs inside a Docker container with potentially different dependency versions, different network routing, different proxy configurations, and different memory and CPU constraints. Cloud Staging bridges this gap by running your actor in the real environment before users see it.

Here is what Cloud Staging validates. Docker build success — your Dockerfile actually builds without errors on Apify's infrastructure. Dependency resolution — all your npm or pip packages install correctly in the container environment. Schema compliance — your actor's actual output matches your declared dataset_schema.json when running in production. Success rate — your actor completes without crashing on Apify's infrastructure. Output quality — the results your actor produces in the cloud match the quality and completeness you see locally. Network behavior — your actor can reach its target websites from Apify's IP ranges and through its proxy infrastructure.

The staging process is straightforward. You push your code to Apify, then trigger a Cloud Staging run through ApifyForge. The tool runs your actor with your test inputs in the production environment and reports back on every validation check. The whole process typically takes a few minutes and costs one or two PPE events — usually under $0.01 per staging run.

Cloud Staging is most valuable when combined with the Schema Validator and Test Runner as a three-layer testing approach: validate schema locally (free), run test cases locally (free), then verify everything works in production with Cloud Staging (minimal cost). For more on this testing workflow, see the related questions about the Schema Validator, Test Runner, and Regression Tests. Visit apifyforge.com/tools/cloud-staging for setup instructions.

Question 40

What are Regression Tests?

Accepted Answer

Regression tests are automated test suites that run before every publish to verify that new code changes have not broken existing functionality in your Apify actors. They are your safety net against deploying breaking changes that cause failures, empty outputs, or maintenance flags on the Store.

The concept is simple but powerful. For each actor, you define multiple test cases that cover different input combinations, edge cases, and expected behaviors. When you push new code, the regression suite runs every test case and reports any failures. If a test that was passing before now fails, you have a regression — something in your new code broke previously working functionality. The suite catches this before the broken code reaches production and your users.

Here are the types of regressions these tests catch that simple smoke tests miss. Broken pagination — your actor used to return 100 results but now returns only 10 because a pagination handler broke. Changed field names — a refactor renamed 'productPrice' to 'price' but your schema and downstream integrations expected the old name. New anti-bot measures — the target website added Cloudflare protection and your actor no longer gets past the landing page. Rate limit changes — the target API reduced its rate limit and your actor now gets throttled, returning partial results. Dependency updates — a new version of Cheerio or Playwright changed an API method signature, causing silent failures.

To set up regression tests, define your test cases in the ApifyForge Test Runner with specific inputs and expected output assertions. Then integrate the regression suite with your Git workflow by adding a pre-push hook that runs all test cases before allowing a push to proceed. If any test fails, the push is blocked and you see exactly which test case failed and why. This prevents broken code from ever reaching your Apify builds.

The ApifyForge Test Runner stores your regression test history, so you can track when specific tests started failing, correlate failures with code changes, and identify patterns over time. This historical data is invaluable for debugging intermittent failures. For related testing tools, see the questions about the Test Runner, Schema Validator, and Cloud Staging. Visit apifyforge.com/tools/regression-tests for setup guides and best practices.

Question 41

How do I validate my actor's output schema?

Accepted Answer

Use the ApifyForge Schema Validator to compare your actor's actual dataset output against the schema declared in your dataset_schema.json file. The validator runs locally on your machine, takes just seconds, and catches the exact issues that trigger maintenance flags on the Apify Store.

Here is the step-by-step process to validate your schema. First, make sure your actor has a dataset_schema.json file in its root directory. This file declares the structure and types of every field your actor outputs. If you do not have one yet, create it based on your actor's actual output structure — every field your actor produces should be declared with its correct type (string, number, boolean, array, object). Second, run your actor locally or in the cloud to produce a sample dataset. Third, run the ApifyForge Schema Validator, pointing it at your actor's output data and the schema file.

The validator performs four categories of checks. Type mismatches — if your schema says a field is a string but your actor outputs a number, you get an error with the exact JSON path (e.g., results[0].price), the expected type (string), and the actual type (number). Undeclared fields — if your actor outputs fields not listed in your schema, these are flagged because they indicate your schema is incomplete. Missing required fields — if your schema marks a field as required but it is absent from some output items, the validator catches this. Empty schemas — if your dataset_schema.json exists but defines no fields, this is immediately flagged as invalid.

Schema violations matter because they directly affect your actor's visibility on the Apify Store. When Apify's automated quality checks detect schema mismatches over a period of several days, your actor gets flagged with a maintenance warning. This reduces its ranking in Store search results and category pages, which means fewer users find and run your actor, which means less PPE revenue. The damage from a maintenance flag can take weeks to recover from even after the underlying issue is fixed.

The prevention strategy is simple: run the Schema Validator before every push. Many developers add it to a Git pre-commit or pre-push hook so validation happens automatically. For a complete testing workflow, combine the Schema Validator with the Test Runner and Cloud Staging for three layers of pre-deploy validation. See the related questions about the Schema Validator tool and Regression Tests for more details. Visit apifyforge.com/tools/schema-validator for full documentation.

Question 42

What is the MCP Debugger?

Accepted Answer

The MCP Debugger tests and diagnoses MCP (Model Context Protocol) server connections on Apify standby URLs. It sends a real protocol handshake — the same initialize request that Claude Desktop and other MCP clients send — and reports exactly what works, what fails, and how to fix it.

MCP servers on Apify run as standby actors: always-on HTTP endpoints that accept JSON-RPC requests at a /mcp path. When they stop working, the error messages from MCP clients are frustratingly vague: 'connection failed', 'transport error', or just silence. The MCP Debugger replaces guesswork with diagnostics.

Here is what the MCP Debugger checks. Connection success — can it reach the standby URL and get a response? Latency — how long does the initialize handshake take? Under 2 seconds is healthy, 2-5 seconds suggests a cold start, over 5 seconds is a warning. Protocol compliance — does the server respond with valid JSON-RPC? Tool discovery — how many tools does the server expose, and what are their names and descriptions? Authentication — does the server require a token, and does the provided token work?

The debugger diagnoses common issues and maps them to actionable fixes. A 404 means your webServerMcpPath is wrong in actor.json. A timeout means standby mode is not enabled or the actor has not been warmed up. A 401 means the authentication token is missing or invalid. Zero tools returned means your server.tool() calls happen after the transport connects instead of before.

The MCP Debugger costs $0.15 per debug run. To use it, go to the MCP Debugger page in the ApifyForge dashboard, enter your standby URL (e.g., https://username--actor-name.apify.actor/mcp), optionally provide an API token, and click Debug Connection. The report shows connection status, latency, available tools, detected issues, and fix suggestions.

This tool is especially valuable when setting up a new MCP server, troubleshooting user-reported connection failures, and monitoring MCP server health over time. For related testing tools, see the questions about the Schema Validator, Test Runner, and Cloud Staging. Visit apifyforge.com/tools/mcp-debugger for documentation.

Question 43

What is the Compliance Scanner?

Accepted Answer

The Compliance Scanner assesses legal and regulatory risk for any Apify actor before you scrape. It analyzes the actor's metadata — name, description, categories, and README — to detect PII collection indicators, Terms of Service exposure against 13 major platforms, and applicable regulations across 6 jurisdictions.

The scan checks three risk categories. PII risk scans for 18 keywords that indicate personal data collection: email, phone, name, address, salary, resume, identity, and more. ToS risk matches against known platform policies: LinkedIn and Facebook are HIGH risk (active litigation), Amazon and Google are MEDIUM, Reddit and Yelp are LOW. Authentication risk flags actors accessing content behind login walls, relevant to CFAA compliance.

The scan produces an overall risk level (LOW/MEDIUM/HIGH), lists applicable regulations (GDPR, CCPA, CFAA, CAN-SPAM, PIPEDA, ePrivacy Directive), and provides actionable recommendations like 'document your lawful basis for processing personal data under GDPR' or 'add opt-out mechanism for email collection.'

Compliance Scanner costs $0.15 per scan and does NOT run the target actor — it reads metadata only. Visit apifyforge.com/tools/compliance-scanner for documentation.

Question 44

What is the Cost Calculator?

Accepted Answer

The Cost Calculator estimates what any actor will cost at various volumes before you spend credits. Enter an actor ID and get a breakdown of PPE pricing, average compute cost per run, average events per run, and volume projections from 1x to 100x.

The calculator fetches the actor's current PPE configuration and analyzes the last 20 successful runs for real compute costs — duration, memory, and platform charges. Projections separate PPE charges from compute charges so you see exactly where your money goes. This is especially valuable when comparing competing actors: two scrapers might both extract the same data, but one costs 3x more at high volume.

Cost Calculator costs $0.20 per calculation and does not run the target actor. Visit apifyforge.com/tools/cost-calculator for documentation.

Question 45

What is the Input Tester?

Accepted Answer

The Input Tester validates your input JSON against an actor's declared input schema without running the actor. It checks required fields, type constraints (string vs number vs integer), enum values, and numeric ranges. If your input is invalid, you get specific error messages before wasting credits on a failed run.

Beyond validation, the Input Tester generates ready-to-use API call snippets: cURL, Python (apify-client), and JavaScript (apify-client). Paste the generated code directly into your integration instead of hand-crafting API calls.

Input Tester costs $0.15 per validation and does not run the target actor. Visit apifyforge.com/tools/input-tester for documentation.

Question 46

What is the LLM Output Optimizer?

Accepted Answer

The LLM Output Optimizer analyzes actor output and recommends how to reduce token consumption when feeding data to large language models. It scores every field by information density — high-value fields like name, url, and email are kept, while low-value fields like raw HTML, internal IDs, and timestamps are flagged for removal.

The optimizer reads sample output from the actor's most recent successful run and produces a field-by-field analysis with token counts, value classifications, and recommended actions (keep, drop, or truncate). It generates an optimized schema and reports expected token savings — typically 40-70%.

LLM Output Optimizer costs $0.35 per analysis. It does not run the target actor — it reads from the latest dataset. Visit apifyforge.com/tools/llm-optimizer for documentation.

Question 47

What is the Pipeline Builder?

Accepted Answer

The Pipeline Builder validates multi-actor data pipelines and generates TypeScript orchestration code. Define pipeline stages with actor IDs and field mappings, and the builder checks that output fields from each stage map correctly to the next stage's input fields.

The builder identifies type mismatches between stages, warns about missing field mappings, and generates complete Actor.call() chain code that you can deploy as a new orchestration actor. It also estimates total pipeline cost by summing PPE prices across all stages.

Pipeline Builder costs $0.40 per build and does not run any actors — it reads schemas from latest builds. Visit apifyforge.com/tools/pipeline-builder for documentation.

Question 48

Do the tools cost anything?

Accepted Answer

Every tool in ApifyForge runs as an Apify actor on your account and charges a small PPE fee per use. Results are cached in your dashboard, so viewing previous reports is always free.

Here is the complete pricing by category:

Testing: Schema Validator ($0.35), Test Runner ($0.35/suite), Cloud Staging ($0.50), Regression Suite ($0.35/suite), MCP Debugger ($0.15).

Schema & Data: Input Tester ($0.15), Schema Registry ($0.20), Schema Diff ($0.20), LLM Output Optimizer ($0.35).

Cost & Planning: Cost Calculator ($0.20), Plan Advisor ($0.25), Proxy Analyzer ($0.25).

Compliance: Compliance Scanner ($0.15).

Pipeline: Pipeline Builder ($0.40).

Privacy: Personal Data Exposure Report ($1.00).

Analytics: Fleet ($0.40), Revenue ($0.40), Health ($0.25), Quality ($0.25), Competitors ($0.50), Cost Watchdog ($0.25), Completeness ($0.25), Deprecation ($0.25), A/B Tester ($0.50), Market Gaps ($0.40).

A typical development cycle — schema validation, test suite, and cloud staging — costs about $1.20 total. Visit apifyforge.com/pricing for the full breakdown.

Question 49

What is the Personal Data Exposure Report?

Accepted Answer

The Personal Data Exposure Report scans 20+ data broker and people-search sites to find where your personal information is listed online. It searches sites like Spokeo, WhitePages, TruePeopleSearch, Radaris, and others, plus checks the DataBreach.com breach database and uses Brave Search to find broker listings indexed by search engines.

For each site where your data is found, the report provides a direct link to your listing (when available), what types of data are exposed (name, address, phone, email, relatives, etc.), step-by-step removal instructions specific to that site, a link to the opt-out page, what you will need to complete the removal (email, phone number, profile URL), and an estimated time to complete the removal process.

The report also generates an overall exposure score from 0 to 100, categorises removals by difficulty (easy, medium, hard), and identifies PeopleConnect family sites where one removal covers multiple brokers — Intelius, ZabaSearch, Instant Checkmate, AnyWho, and Addresses.com can all be removed with a single submission at suppression.peopleconnect.us.

The actor costs $1.00 per report and typically completes in under 10 seconds. For comparison, Incogni charges $7.49/month ($89.88/year) and DeleteMe charges $10.75/month ($129/year). Running a monthly exposure scan costs $12/year.

The report does not submit removal requests on your behalf — it provides the detection and a personalised removal guide. You follow the step-by-step instructions to remove your data from each site.

To use it, enter your first name, last name, and optionally your state and email address. Adding an email enables breach database checks. Visit apifyforge.com/tools/exposure-report to run a report.

Question 50

What happens when an actor fails?

Accepted Answer

When an Apify actor fails, the run terminates with a FAILED status and the platform records the error details, exit code, and run log. Understanding why actors fail — and how to diagnose failures quickly — is critical for maintaining high success rates and avoiding maintenance flags that reduce your Store visibility.

The most common failure categories are: runtime errors (unhandled exceptions in your code), timeouts (actor exceeds its configured timeout or the default 3600 seconds), out-of-memory crashes (actor tries to use more RAM than allocated), network errors (target website is down or blocks requests), and input validation failures (user provides invalid or missing required inputs). Each category produces different error signatures in the run log.

When a failure occurs, here is what happens on the platform side. The run status is set to FAILED. The error message and stack trace are captured in the run log. If the actor was mid-way through producing results, any data already pushed to the dataset is preserved — you do not lose partial results. The user who triggered the run is charged only for the compute time actually consumed, not the full expected duration. If the failure happened before any meaningful work, the charge is minimal.

For actor developers, failures directly impact your quality score and Store visibility. Apify tracks your actor's success rate over the past 7 days. If the rate drops below a threshold (typically around 80%), your actor may receive a maintenance flag. This is why catching failures before they reach production — using the ApifyForge Test Runner, Schema Validator, and Cloud Staging — is so valuable. A single automated test that costs $0.001 can prevent a failure that costs you weeks of reduced Store visibility.

ApifyForge monitors failure rates across your entire fleet through the dashboard. You can see which actors are failing most frequently, identify patterns in failure types, and prioritize fixes based on revenue impact. For step-by-step debugging instructions, see the related question about how to debug a failed actor run. For testing tools that prevent failures, see the questions about the Test Runner and Cloud Staging.

Question 51

How do I debug a failed Apify actor run?

Accepted Answer

Start with the run log in the Apify Console — it contains the error message, stack trace, and timeline of what happened during the run. Navigate to your actor, click the failed run, and read the log from the bottom up to find the error that caused the failure.

Here is a systematic debugging process. Step one: identify the error type from the run log. Look for these common patterns. An unhandled exception shows a JavaScript Error with a stack trace pointing to the exact file and line number — this is usually the easiest to fix because the location is explicit. A timeout error means the actor exceeded its time limit, which could indicate infinite loops, slow network requests, or the target website rate-limiting your requests. An out-of-memory error means the actor tried to use more RAM than its allocated memory (check the actor.json memory setting). A network error like ECONNREFUSED, ETIMEDOUT, or HTTP 403/429 means the target website is unreachable or blocking your requests.

Step two: reproduce the failure locally. Copy the exact input from the failed run (available in the run details), run the actor locally with apify run, and observe whether the same error occurs. If it reproduces locally, you can debug with standard Node.js debugging tools — console.log, the Node.js debugger, or VS Code's built-in debugger. If it does not reproduce locally, the issue is likely environment-specific (different Node.js version, Docker configuration, proxy behavior, or network routing). Use Cloud Staging to test in the production environment.

Step three: check for common causes specific to each error type. For timeouts — add progress logging to identify where the actor gets stuck, reduce the scope of work per run, implement request queuing with configurable concurrency. For out-of-memory — reduce batch sizes, stream data instead of loading everything into memory, increase the memory allocation in actor.json. For 403/429 blocking — add or upgrade proxies, reduce request rate, add random delays, switch from Cheerio to Playwright for JavaScript-heavy sites.

For fleet-wide debugging, the ApifyForge dashboard shows failure trends across all your actors, helping you identify whether failures are isolated incidents or systemic issues (like a proxy provider outage or a target website redesign). For prevention strategies, see the related questions about the Test Runner, Schema Validator, and what happens when an actor fails.

Question 52

Why is my Apify actor under maintenance?

Accepted Answer

Maintenance flags are applied by Apify's automated quality system when your actor consistently fails certain checks over a multi-day period. The most common causes are: dataset schema mismatches between your declared schema and actual output, low success rates (below approximately 80% over 7 days), crashes on default input, and stale builds that have not been updated in 90+ days.

Here is how to diagnose which specific issue triggered your maintenance flag. First, check the Apify Console for any automated run results — Apify periodically runs your actor with its default input to verify it works. If these automated runs are failing, that is likely the trigger. Second, use the ApifyForge Schema Validator to check for schema mismatches — this is the most common cause and the easiest to miss because your actor might work perfectly while producing output that does not match its declared schema. Third, check your success rate in the ApifyForge dashboard — even occasional failures can drag your 7-day rolling average below the threshold.

To resolve a maintenance flag, you need to fix the underlying issue and then demonstrate consistent successful operation. Here is the process. Identify the cause using the steps above. Fix the issue — update your schema, fix the bug, handle the edge case, or rebuild with updated dependencies. Push a new build to Apify. Run your actor successfully with the default input several times to rebuild your success rate. Wait for Apify's periodic re-evaluation — the flag does not clear instantly, it typically takes several days of clean runs.

Prevention is far more effective than remediation. Use the ApifyForge Schema Validator before every push to catch schema mismatches. Use the Test Runner to verify your actor works with default input. Use Cloud Staging to confirm production behavior matches local testing. Monitor your fleet health in the dashboard to catch declining success rates before they trigger flags. For more details on testing workflows, see the questions about the Schema Validator, Test Runner, and Cloud Staging.

Question 53

How do I handle Apify actor timeouts?

Accepted Answer

Actor timeouts occur when your actor exceeds its configured time limit (set in actor.json, default is 3600 seconds / 1 hour). The fix depends on whether the timeout is caused by the actor doing too much work, getting stuck on slow operations, or encountering infinite loops.

Here is how to diagnose and fix each cause. Too much work per run — if your actor tries to process thousands of pages or API calls in a single run, it may legitimately need more time. Solutions: increase the timeout in actor.json (up to 86400 seconds / 24 hours for paid plans), implement pagination in your actor input so users can process data in smaller batches, or add request queuing with configurable concurrency (maxConcurrency in Crawlee) to process requests in parallel rather than sequentially. Stuck on slow operations — if your actor hangs on specific network requests, implement request-level timeouts using AbortController or the timeout option in fetch/axios. A common pattern is setting a 30-second timeout per individual request so that a single slow target does not stall the entire run. For Crawlee-based actors, set navigationTimeoutSecs and requestHandlerTimeoutSecs in the crawler configuration. Infinite loops — these are bugs where your actor enters a cycle it never exits, such as continuously re-crawling the same URL or retrying a permanently failing request. Add logging at key decision points so you can see the actor's progress in the run log and identify where it gets stuck.

For Crawlee-based scrapers, the most effective timeout prevention measures are: set maxRequestsPerCrawl to cap the total number of pages processed, set maxRequestRetries to limit retries on failing URLs (default is 3, which is usually fine), and implement a progress callback that logs completion percentage so you can monitor whether the actor is making forward progress.

From a fleet management perspective, ApifyForge tracks timeout rates across your actors in the dashboard. If you see a spike in timeouts, it often indicates an external change — the target website became slower, added more anti-bot measures, or restructured its pages. For related debugging advice, see the questions about debugging failed runs and what happens when an actor fails.

Question 54

Can I use Apify with Python?

Accepted Answer

Yes, Apify has a full Python SDK available on PyPI (package name: apify) that supports complete actor development, API access, dataset management, and all the same capabilities as the JavaScript SDK. Python is a first-class citizen on the Apify platform.

Here is how Python development on Apify works. Install the SDK with pip install apify. Create a new actor project using the Apify CLI with apify create my-actor --template python, which generates a project scaffold with the correct Dockerfile, requirements.txt, and main.py structure. Your actor code uses the same Actor.main() async pattern as the JavaScript SDK — you define an async main function that receives the actor context, reads input, does its work, and pushes results to the dataset.

Python actors run inside Docker containers on Apify's infrastructure, using official Apify Python base images that come pre-configured with the SDK and common dependencies. Your Dockerfile extends the base image and installs your additional requirements. The build process handles pip dependency resolution, so you just list your packages in requirements.txt and they are available at runtime.

Python is particularly popular for several categories of actors. Data science and analytics actors that use pandas, numpy, scipy, or scikit-learn for processing scraped data. Machine learning actors that run inference using TensorFlow, PyTorch, or Hugging Face transformers. Natural language processing actors that use spaCy, NLTK, or custom NLP pipelines. API integration actors where Python's requests library and rich ecosystem of API client libraries make integration straightforward. Data transformation actors that leverage Python's excellent CSV, JSON, and database libraries.

For web scraping specifically, Python actors can use BeautifulSoup, Scrapy, or the Apify Python SDK's built-in HTTP client. Browser automation is available through Playwright for Python. While the Crawlee framework (Apify's recommended scraping library) is currently more mature in its JavaScript version, the Python ecosystem provides equivalent capabilities through other libraries.

All ApifyForge tools work seamlessly with Python actors because they interact through the Apify API, which is language-agnostic. The Schema Validator, Test Runner, Cloud Staging, and fleet analytics do not care whether your actor is written in Python, JavaScript, TypeScript, or any other language. For more on building actors, visit apifyforge.com/glossary/actors. For related topics, see the questions about what happens when an actor fails and how to debug failed runs.

Question 55

How much can I earn with Apify actors?

Accepted Answer

Earnings from Apify actors vary widely depending on your niche, pricing strategy, actor quality, and portfolio size, but individual actors solving high-value problems can earn $100 to $1,000 or more per month, and developers managing portfolios of 20+ actors often generate $2,000 to $10,000 per month in total PPE revenue.

The revenue model works through Pay Per Event (PPE) pricing. You set a price per event for your actor — for example, $0.50 per 1,000 results, $0.01 per page scraped, or $0.10 per search query. Every time a user runs your actor, they pay that per-event price multiplied by the number of events consumed. Apify handles all billing and payment, and you receive your earnings. The key insight is that PPE pricing should reflect the value your actor provides to the user, not just the compute cost. A B2B lead generation actor that saves a sales team hours of manual research can justifiably charge $0.05 per lead even if the compute cost is $0.001.

Here are the factors that determine how much an individual actor earns. Niche selection is the most important factor — actors in B2B lead generation, competitive intelligence, price monitoring, real estate data, and job market analysis command higher prices because the data is directly valuable to businesses. Pricing strategy matters — underpricing leads to high volume but low revenue, while overpricing reduces adoption. Study competing actors in the Store to benchmark your pricing. Quality score directly affects Store visibility — actors with high success rates, complete schemas, and good metadata rank higher in search, which means more users discover and run them. A free tier or low-cost entry point drives initial adoption, which builds usage volume and improves your Store ranking.

To maximize your earnings, consider a portfolio strategy rather than relying on a single actor. Build multiple actors that serve the same niche from different angles — for example, a Google Maps scraper, a Yelp scraper, a TripAdvisor scraper, and a review aggregator all serve the local business intelligence market. Each actor earns independently, and users in that niche often use multiple actors. Developers who build focused portfolios in high-value niches consistently earn more than those who build scattered one-off actors.

ApifyForge helps you optimize revenue with its fleet analytics dashboard, which shows PPE earnings per actor over time, identifies your top performers, flags underperforming actors, and tracks the quality scores that affect Store visibility. Visit apifyforge.com/glossary/ppe for a detailed breakdown of PPE pricing mechanics. For related strategies, see the question about how to get more users for your actor.

Question 56

How do I get more users for my actor?

Accepted Answer

Actor discoverability on the Apify Store is driven by three main factors — quality score, search relevance, and usage volume — and optimizing all three is the key to growing your user base and PPE revenue.

Start with metadata optimization, which is the fastest way to improve discoverability. Your actor title should be keyword-rich, 40-60 characters long, and clearly state what the actor does. For example, 'Google Maps Scraper - Extract Business Data and Reviews' is better than 'Maps Data Tool' because it contains the search terms users actually type. Your description should include secondary keywords and clearly explain the value proposition in the first two sentences. Your README should follow a proven structure: summary paragraph, how it works section, input parameters with examples, output format with sample data, pricing and cost information, and a FAQ section that targets long-tail search queries.

Next, optimize your quality score, which directly affects your ranking in Store search results and category pages. The quality score is a composite metric based on several factors: success rate (aim for 95% or higher), schema compliance (zero mismatches between your output and dataset_schema.json), metadata completeness (title, description, README, categories, SEO fields all filled out), build freshness (regular updates signal an active, maintained actor), and response to maintenance flags (resolve them quickly when they occur). Use the ApifyForge Schema Validator before every push and monitor your quality score trends in the ApifyForge dashboard.

Finally, drive initial usage to build momentum. The Apify Store algorithm favors actors with higher usage volume, creating a positive feedback loop — more users lead to higher rankings, which lead to more users. To bootstrap this cycle, share your actor in relevant online communities where your target users spend time (Reddit, Hacker News, industry-specific forums and Slack groups). Write blog posts or tutorials that demonstrate your actor solving a real problem, and link to it in the Apify Store. Offer a free tier or low-cost entry point that lets users try your actor with minimal risk — once they see value, they convert to regular paid usage. Reach out to YouTubers and bloggers who cover web scraping and automation topics.

ApifyForge provides Store analytics that show how your actors rank for specific search terms, which metadata fields are incomplete, and how your quality score compares to competing actors in the same category. Visit apifyforge.com/glossary/store-seo for detailed Store SEO strategies. For related topics, see the questions about what the Apify Store is and how much you can earn with actors.

Question 57

My dashboard shows no data

Accepted Answer

An empty dashboard usually means your API token is invalid, expired, or was not saved correctly. The first step is to go to the ApifyForge Settings page and verify that your token is entered and saved.

Here is a systematic troubleshooting process. Step one: check your token in Settings. If the token field is empty, you need to re-enter it. If it shows a token, try removing it, saving, then re-entering it and saving again. This forces a fresh validation. Step two: if you just connected your account for the first time, wait 3-5 minutes. The initial data population requires pulling your actor list, run history, and analytics from the Apify API, which takes a few minutes depending on how many actors you have. You should see the dashboard begin populating gradually. Step three: verify your token is valid by logging into your Apify account at console.apify.com/account/integrations and checking the token status. If the token has been revoked, expired, or had its permissions changed, it will not work with ApifyForge. Generate a fresh token and enter it in ApifyForge Settings.

Other potential causes include browser-related issues. Try clearing your browser cache and cookies for the ApifyForge domain, then reload the page. Try a different browser or incognito/private browsing mode to rule out browser extension interference. Some ad blockers and privacy extensions block API calls, which can prevent dashboard data from loading.

If you have a brand new Apify account with no actors and no run history, the dashboard will appear empty because there is no data to display. This is expected behavior — the dashboard will populate as you create actors and run them. If you have actors on Apify but they still do not appear in ApifyForge after 10 minutes with a valid token, the issue may be on our end. Check the ApifyForge status page for any ongoing service disruptions.

For persistent issues that none of the above steps resolve, open a bug report at github.com/apifyforge/apifyforge with your browser name and version, a screenshot of the empty dashboard, and confirmation that your token works when tested directly against the Apify API. For related topics, see the questions about how to connect your Apify account and how to disconnect your account.

Question 58

My actor is getting blocked or returning 403 errors

Accepted Answer

HTTP 403 (Forbidden) and 429 (Too Many Requests) errors mean the target website's anti-bot system has detected your scraper and is blocking its requests. This is one of the most common issues in web scraping, and the fix depends on what anti-bot measures the site uses and what your current setup looks like.

Here is a layered approach to resolving blocking issues, starting with the simplest fixes and escalating to more advanced techniques. Layer one — add proxies if you are not using them. Scraping without proxies means all your requests come from a single IP address, which is trivial for websites to detect and block. Datacenter proxies rotate your IP across a pool of addresses and work for most basic websites. Configure proxies in your Crawlee crawler with the proxyConfiguration option. Layer two — upgrade to residential proxies. If you are already using datacenter proxies but still getting blocked, the target site likely checks whether the IP belongs to a datacenter or a residential ISP. Residential proxies route your traffic through real residential IP addresses, which are much harder for anti-bot systems to distinguish from regular users. Sites like Amazon, LinkedIn, Google, Zillow, and most social media platforms require residential proxies. Layer three — switch from CheerioCrawler to PlaywrightCrawler or PuppeteerCrawler. Cheerio makes raw HTTP requests without executing JavaScript, which is fast but does not pass browser fingerprint checks. Modern anti-bot systems like Cloudflare, PerimeterX, and DataDome check for JavaScript execution, browser APIs, canvas fingerprints, and WebGL rendering. PlaywrightCrawler runs a real browser that passes these checks.

Additional techniques to layer on top of proxies and browser crawling include reducing your request rate with the maxRequestsPerMinute Crawlee option (start with 20-30 requests per minute and adjust based on results), adding random delays between requests using the Crawlee autoscaling configuration, rotating User-Agent headers to mimic different browsers, setting realistic viewport sizes and screen resolutions in Playwright, and handling CAPTCHAs with third-party solving services when they appear.

Some particularly aggressive websites require all of these measures simultaneously. If you are still getting blocked after implementing everything above, the target site may be using advanced bot detection that requires session management, cookie handling, or specialized scraping approaches. For related debugging advice, see the questions about how to debug failed actor runs and what happens when an actor fails.

Question 59

An actor shows as 'maintenance' but it works fine

Accepted Answer

This is a common and frustrating situation where your actor runs successfully when you test it manually, but Apify's automated quality system still flags it with a maintenance warning. The explanation is that the maintenance flag is based on automated quality checks that evaluate different criteria than your manual tests, and these checks run independently on Apify's schedule.

Here are the most likely reasons your actor shows as maintenance despite appearing to work fine. Dataset schema mismatch — your actor produces output that does not exactly match the types and fields declared in your dataset_schema.json. Your manual test might work perfectly and return useful data, but if the schema says price is a number and your actor sometimes outputs price as a string like '$19.99' instead of 19.99, the automated check flags this as a violation. This is the most common cause of 'works fine but flagged' situations. Inconsistent output on default input — Apify's quality checks run your actor with its default input. If your default input is configured differently from the input you use for manual testing, the automated runs might fail or produce different output. Make sure your actor works correctly with exactly the default input shown in your input schema. Success rate over time — even if your actor works now, the maintenance flag evaluates success rate over the past 7 days. If your actor had a bad period with multiple failures last week, the flag might still be active even though you have since fixed the issue. The flag clears once enough successful runs accumulate to bring the rolling average above the threshold. Stale builds — if your actor has not been updated in 90+ days, it may be deprioritized regardless of its current functionality.

To diagnose the specific cause, use the ApifyForge Schema Validator to check for any schema mismatches you might have missed. Check the quality score breakdown in the ApifyForge dashboard, which shows the individual factors contributing to your overall score. Review the automated run logs in the Apify Console, specifically the runs triggered by Apify's system (not your manual runs) to see if they show different behavior.

To resolve the flag, fix the identified issue, push a new build, run your actor successfully with the default input several times, and wait for Apify's periodic re-evaluation. The flag does not clear instantly — it typically takes several days of clean runs. For more details on the maintenance system, see the related question about why actors get flagged for maintenance.

Question 60

Where do I report bugs?

Accepted Answer

Report bugs by opening an issue on our GitHub repository at github.com/apifyforge/apifyforge. We use GitHub Issues as our primary bug tracking system because it allows public visibility, threaded discussions, and status tracking for every reported issue.

To help us diagnose and fix your bug quickly, include the following information in your report. The error message — copy the exact error text, including any error codes or stack traces. What you were doing — describe the steps that led to the bug, step by step, so we can reproduce it. Which tool or dashboard page was involved — specify whether the issue is in the Schema Validator, Test Runner, Cloud Staging, fleet dashboard, revenue tracker, quality score page, or another feature. Browser information — include your browser name and version (e.g., Chrome 120, Firefox 115, Safari 17) and your operating system. Screenshots — visual evidence of the bug is extremely helpful, especially for layout issues, incorrect data displays, or error dialogs. Browser console output — if applicable, open your browser's developer tools (F12), go to the Console tab, and copy any error messages shown there.

For tool-specific bugs, include additional details. For Schema Validator issues: the actor name, the content of your dataset_schema.json, a sample of your actor's output, and the full validator error output. For Test Runner issues: the actor name, the test case configuration you used, and the complete test output including any assertion failures. For Cloud Staging issues: the actor name, the build version tested, and the staging report output. For dashboard and analytics issues: whether the data appears incorrect (and what the correct value should be), whether the page fails to load entirely, or whether specific metrics are missing.

We triage new issues daily and typically respond within 24 hours. Bug priority is based on impact: issues affecting data accuracy (incorrect revenue figures, wrong success rates), tool functionality (validator crashes, test runner failures), and security concerns are addressed first. Feature requests are welcome but tracked separately from bugs.

If your issue is urgent — for example, revenue tracking shows incorrect data that is affecting business decisions — mention 'urgent' in the issue title and we will prioritize it. For general questions that are not bugs, check the other FAQ answers on this page first, as many common questions are already answered. For related information, see the questions about how to connect your account and troubleshooting empty dashboards.

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