Fleet Health Report — Consolidated Fleet Intelligence

Get a complete, data-driven view of your entire Apify actor portfolio plus a prioritized action plan in a single API call. ApifyForge Fleet Analytics scans every actor in your account, pulls run statistics, computes failure rates, estimates monthly revenue, and (optionally) orchestrates 7 specialist actors in parallel to produce a composite Fleet Health Report with a Fleet Health Score, structured Action Plan, and narrative summary. Whether you manage 5 actors or 500, this tool tells you not just what is happening across your fleet but what to do about it first.

New in v3 -- operational work queue

• Fleet Health Score -- single 0-100 composite metric across reliability, cost, quality, compliance, and revenue, with per-dimension drivers and change vs last run
• Action Plan -- prioritized roadmap in four buckets (fixNow, optimizeNext, buildNext, considerArchiving) with impact estimates, confidence explanations, and a "why now" per item
• Lifecycle tracking -- acknowledge items as pending / in_progress / done; regression detection flags items that reappear after being marked done
• Trend + resolved items -- every item is tagged new/worsening/unchanged/improving/stalled vs the previous run, and resolved items surface their realized impact
• Modes -- default | growth | cost | stability | weekly reweight priorities without changing what runs
• Narrative summary -- human-readable headline, key focus, and trend line at the top of every report
• Guard-pipeline bridge -- actors with chronic low release confidence from guard-pipeline automatically surface in the action plan

Why use ApifyForge Fleet Analytics?

• One call, full picture. Instead of clicking through dozens of actor dashboards, get aggregated stats for your entire fleet in a single structured output.
• Revenue estimation. Automatically calculates estimated monthly revenue by multiplying each actor's 30-day run volume by its Pay-Per-Event price.
• Failure detection. Identifies actors with failure rates above 10% by sampling the last 100 runs and filtering to the 30-day window.
• Stale actor identification. Flags actors with zero runs in the last 30 days so you can decide whether to update or deprecate them.
• Category breakdown. Shows how your actors are distributed across Apify Store categories so you can spot gaps or overconcentration.
• Top performers. Ranks your actors by run volume and user count so you know which ones are carrying your portfolio.
• Dashboard-ready output. Designed to feed directly into the ApifyForge dashboard for visualization and trend tracking.

Key Features

• Fetches all actors from your account via paginated API calls (handles accounts with 100+ actors)
• Enriches data with Apify Store statistics (users, runs, builds)
• Pulls actor detail endpoint for PPE pricing configuration
• Samples last 100 runs per actor to compute actual success/failure rates over 30 days
• Computes weighted fleet-wide success rate (weighted by run volume so high-traffic actors count more)
• Identifies top 10 actors by runs and by users
• Estimates monthly revenue from PPE pricing multiplied by 30-day run counts
• Outputs a single comprehensive JSON object to the default dataset

How to Use

1. Go to ApifyForge Fleet Analytics on the Apify Store.
2. Click Try for free.
3. Enter your Apify API Token (find it at Settings > Integrations).
4. Click Start.
5. Wait for the run to complete (typically 30-120 seconds depending on fleet size).
6. View the results in the Dataset tab, or download as JSON.

Input Parameters

Parameter	Type	Required	Default	Description
apifyToken	string	No	(from platform)	Your Apify API token. Leave empty when running on Apify -- the token is injected automatically. Required only for local testing.
includeSpecialistReports	boolean	No	false	When true, orchestrate 7 fleet-wide specialists in parallel (quality-monitor, cost-watchdog, pricing-advisor PPE mode, pricing-advisor plan mode, competitor-scanner, compliance-scanner, market-gap-finder) and synthesize their signals into the Action Plan. Adds 3-10 minutes to run time but upgrades the output from a metadata rollup to a full Fleet Health Report.
includeRevenueBreakdown	boolean	No	false	When true, produce a per-actor revenue breakdown with top earners, pricing tier classification, and missed-monetization opportunities. Zero extra API cost (reuses already-fetched data).
mode	string	No	default	One of default, growth, cost, stability, weekly. Reweights action plan priorities without changing which specialists run.
acknowledgements	array	No	[]	Update the status of action items from a previous run. Each entry is {id, status, note?} where status is pending, acknowledged, in_progress, or done. Acks are persisted across runs so in-progress items don't re-surface at full urgency. Items marked done that reappear are flagged as regressions.

Input Examples

Fast mode -- metadata rollup only (~30 seconds):

{}

Full Fleet Health Report -- orchestrates 7 specialists, produces Action Plan + Fleet Health Score (~3-10 minutes):

{
    "includeSpecialistReports": true,
    "includeRevenueBreakdown": true,
    "mode": "default"
}

Growth-focused weekly review:

{
    "includeSpecialistReports": true,
    "includeRevenueBreakdown": true,
    "mode": "growth"
}

Acknowledging progress on items from a previous run:

{
    "includeSpecialistReports": true,
    "acknowledgements": [
        {"id": "google-maps-scraper:cost-watchdog:cost", "status": "in_progress", "note": "patching the retry logic"},
        {"id": "amazon-scraper:pricing-advisor:revenue", "status": "done"}
    ]
}

Output Example

{
    "totalActors": 211,
    "publicActors": 198,
    "privateActors": 13,
    "totalRuns30d": 48523,
    "totalUsers30d": 1247,
    "totalUsers7d": 389,
    "successRate": 97.3,
    "topActorsByRuns": [
        { "name": "google-maps-scraper", "id": "abc123", "runs30d": 8421 },
        { "name": "linkedin-scraper", "id": "def456", "runs30d": 5102 },
        { "name": "amazon-product-api", "id": "ghi789", "runs30d": 3890 },
        { "name": "twitter-scraper", "id": "jkl012", "runs30d": 2744 },
        { "name": "youtube-channel-scraper", "id": "mno345", "runs30d": 2301 }
    ],
    "topActorsByUsers": [
        { "name": "google-maps-scraper", "id": "abc123", "users30d": 312 },
        { "name": "linkedin-scraper", "id": "def456", "users30d": 198 },
        { "name": "amazon-product-api", "id": "ghi789", "users30d": 145 },
        { "name": "twitter-scraper", "id": "jkl012", "users30d": 89 },
        { "name": "youtube-channel-scraper", "id": "mno345", "users30d": 76 }
    ],
    "categoryBreakdown": {
        "DEVELOPER_TOOLS": 85,
        "AI": 72,
        "SOCIAL_MEDIA": 28,
        "SEO_TOOLS": 15,
        "ECOMMERCE": 11
    },
    "actorsWithNoRuns30d": [
        { "name": "legacy-email-validator", "id": "xyz001", "lastRunAt": "2025-11-15T10:30:00.000Z" },
        { "name": "old-rss-parser", "id": "xyz002", "lastRunAt": "2025-12-01T08:00:00.000Z" }
    ],
    "actorsWithHighFailRate": [
        { "name": "unstable-scraper", "id": "fail01", "failRate": 34.5, "runs30d": 29 },
        { "name": "broken-api-wrapper", "id": "fail02", "failRate": 18.2, "runs30d": 11 }
    ],
    "estimatedMonthlyRevenue": 4821.50,
    "analyzedAt": "2026-03-16T14:30:00.000Z"
}

Output Fields

Field	Type	Description
totalActors	number	Total number of actors in your account
publicActors	number	Number of actors published publicly on the Apify Store
privateActors	number	Number of private (unlisted) actors
totalRuns30d	number	Aggregate run count across all actors in the last 30 days
totalUsers30d	number	Aggregate unique user count across all actors in the last 30 days
totalUsers7d	number	Aggregate unique user count across all actors in the last 7 days
successRate	number	Fleet-wide weighted success rate (percentage, weighted by run volume)
topActorsByRuns	array	Top 10 actors ranked by 30-day run count. Each entry has name, id, runs30d.
topActorsByUsers	array	Top 10 actors ranked by 30-day unique user count. Each entry has name, id, users30d.
categoryBreakdown	object	Map of Apify Store category names to the number of your actors in each category
actorsWithNoRuns30d	array	Actors with zero runs in the last 30 days. Each entry has name, id, and optional lastRunAt timestamp.
actorsWithHighFailRate	array	Actors with >10% failure rate and at least 5 recent runs. Each entry has name, id, failRate (percentage), and runs30d.
estimatedMonthlyRevenue	number	Estimated total monthly revenue in USD, calculated as SUM(runs30d * ppePrice) for all actors with PPE pricing
revenueBreakdown	object	Present when includeRevenueBreakdown: true. Contains revenueByActor, topEarners, actorsWithNoPricing (missed monetization), and pricingTierBreakdown (budget/standard/premium).
specialistReports	object	Present when includeSpecialistReports: true. Raw signal summaries from each of the 7 orchestrated specialists (qualityAudit, costWatchdog, pricingAdvisor, planAdvice, competitorScanner, complianceScanner, marketGapFinder), each with ok, durationSeconds, itemCount, and signals[].
highlights	object	High-level topPriorities[] and alerts[] distilled from cross-specialist signals.
actionPlan	object	The prioritized work queue -- see dedicated section below. Contains fixNow, optimizeNext, buildNext, considerArchiving, resolved, recommendedSequence, and a narrative summary.
fleetHealthScore	object	Single 0-100 composite metric with score, band, change vs previous run, and drivers[] showing which dimensions moved the score.
analyzedAt	string	ISO 8601 timestamp of when the analysis was performed

Fleet Health Score

When the actor runs with includeSpecialistReports: true, it produces a single 0-100 Fleet Health Score composed of 5 weighted dimensions:

Dimension	Budget	What it measures
Reliability	25 pts	Weighted fleet success rate plus high-failure actor count
Cost	20 pts	Cost anomalies, budget breaches, runaway spending
Quality	20 pts	Fleet-wide quality score gap vs target (90+)
Compliance	15 pts	PII / ToS / regulatory risk signals per actor
Revenue	20 pts	Unmonetized high-volume actors + underpriced actors

The score comes with a band (critical | poor | fair | good | excellent), a change delta vs the previous run (tracked automatically via KV store), and a drivers[] array explaining what contributed to or deducted from the score. This is the single number to track week over week.

Action Plan

The Action Plan is the operational work queue produced from cross-specialist signal synthesis. Every item has:

• bucket -- fixNow / optimizeNext / buildNext / considerArchiving
• urgency -- critical / high / medium / low
• status -- pending / acknowledged / in_progress / done (persisted across runs via acknowledgements)
• trend -- new / worsening / unchanged / improving / stalled (vs previous run)
• sources[] -- which specialists agreed on this item (deduped across runs)
• estimatedImpact -- { monthlyUsd, confidence } where available
• whyNow[] -- list of reasons this is urgent right now (new, trend worsening, stalled N cycles, affects top actor, $N impact)
• confidenceExplanation[] -- why the recommendation should be trusted (how many specialist signals agreed, longitudinal evidence, regression history)
• isRegression -- true when a previously resolved item has reappeared
• unresolvedCycles -- number of consecutive runs this item has been unresolved

Plus a top-level recommendedSequence[] (dependency-ordered top 7 items the user should tackle in order), a resolved[] section showing items that disappeared since the previous run with their realizedImpact.previousMonthlyUsd, and a narrative summary with a headline, key focus, and trend line.

Acknowledgement lifecycle

The action plan becomes an actual work queue through acknowledgements. Passing acknowledgements: [{id, status, note?}] on input persists the status in the actor's KV store and applies it to the computed plan:

• acknowledged / in_progress -- urgency downgraded one level so the item stays visible but doesn't dominate
• done -- item expected to be resolved. If it reappears on a later run, it is flagged with isRegression: true and forced to at least high urgency

This is what turns the plan from a report into something users work from instead of just read.

Modes

Mode	Effect
default	Balanced priorities (recommended)
growth	Emphasize buildNext + revenue opportunities, downgrade cost-watchdog fixNow items to medium
cost	Emphasize cost signals, downgrade buildNext opportunities
stability	Emphasize quality + compliance + risk, downgrade buildNext
weekly	Balanced output formatted for weekly review

Modes reweight the existing signals -- they don't change which specialists run.

Programmatic Access

Python

from apify_client import ApifyClient

client = ApifyClient("apify_api_xxxxxxxxxxxxxxxxxxxxxxxxxxxx")

run = client.actor("ryanclinton/apifyforge-fleet-analytics").call(
    run_input={"apifyToken": "apify_api_xxxxxxxxxxxxxxxxxxxxxxxxxxxx"}
)

dataset_items = client.dataset(run["defaultDatasetId"]).list_items().items
analytics = dataset_items[0]

print(f"Total actors: {analytics['totalActors']}")
print(f"30-day runs: {analytics['totalRuns30d']}")
print(f"Success rate: {analytics['successRate']}%")
print(f"Estimated revenue: ${analytics['estimatedMonthlyRevenue']}")

for actor in analytics["topActorsByRuns"][:5]:
    print(f"  {actor['name']}: {actor['runs30d']} runs")

JavaScript

import { ApifyClient } from "apify-client";

const client = new ApifyClient({
    token: "apify_api_xxxxxxxxxxxxxxxxxxxxxxxxxxxx",
});

const run = await client.actor("ryanclinton/apifyforge-fleet-analytics").call({
    apifyToken: "apify_api_xxxxxxxxxxxxxxxxxxxxxxxxxxxx",
});

const { items } = await client.dataset(run.defaultDatasetId).listItems();
const analytics = items[0];

console.log(`Total actors: ${analytics.totalActors}`);
console.log(`30-day runs: ${analytics.totalRuns30d}`);
console.log(`Success rate: ${analytics.successRate}%`);
console.log(`Estimated revenue: $${analytics.estimatedMonthlyRevenue}`);

cURL

# Start the actor run
curl -X POST "https://api.apify.com/v2/acts/ryanclinton~apifyforge-fleet-analytics/runs?token=YOUR_API_TOKEN" \
    -H "Content-Type: application/json" \
    -d '{"apifyToken": "apify_api_xxxxxxxxxxxxxxxxxxxxxxxxxxxx"}'

# Fetch results from the default dataset (use the defaultDatasetId from the run response)
curl "https://api.apify.com/v2/datasets/DATASET_ID/items?token=YOUR_API_TOKEN"

How It Works

ApifyForge Fleet Analytics executes a multi-stage pipeline to build your fleet overview:

1. Actor enumeration. Calls GET /v2/acts?my=true with pagination (100 per page) to retrieve every actor in your account. This returns basic metadata including name, ID, public/private status, and summary stats.

2. Username resolution. Extracts your username from the first actor record, or falls back to the GET /v2/users/me endpoint if needed. The username is required to query the Store API.

3. Store enrichment. Calls GET /v2/store?username=YOUR_USERNAME with pagination to pull Apify Store statistics. Store stats include more granular user counts (7-day, 30-day) and run counts that may differ from the actor list endpoint.

4. Detail fetching. For each actor, calls GET /v2/acts/{actorId} to retrieve full details including PPE pricing configuration, categories, and tagged builds. This is the only endpoint that exposes pricingInfos.

5. Run sampling. For each actor, fetches the last 100 runs via GET /v2/acts/{actorId}/runs?limit=100&desc=true. Filters to runs started within the last 30 days and computes success/failure rates. Actors with >10% failure rate and at least 5 runs are flagged.

6. Aggregation. Computes fleet-wide totals (runs, users, categories), weighted success rate, estimated revenue, and identifies stale actors (zero runs in 30 days). Ranks actors by runs and users to surface the top 10.

7. Output. Pushes the complete analytics object to the default dataset and charges one PPE event.

How Much Does It Cost?

ApifyForge Fleet Analytics uses Pay-Per-Event pricing at $0.10 per analysis.

Scenario	Events	Cost
One-time fleet overview	1	$0.10
Weekly monitoring (4x/month)	4	$0.40
Daily monitoring (30x/month)	30	$3.00

In addition to the PPE charge, standard Apify platform compute costs apply based on memory usage and runtime. A typical analysis of 200+ actors completes in under 2 minutes using 256 MB of memory.

Tips

• Schedule regular runs. Set up a daily or weekly schedule to track trends over time. Each run creates a new dataset entry, giving you historical data for your ApifyForge dashboard.
• Investigate high-fail actors first. The actorsWithHighFailRate array is sorted by failure rate. Fix these to improve your fleet-wide success rate and prevent user churn.
• Revenue gaps. Compare actorsWithNoRuns30d against the rest. Actors with zero runs but historical traffic may need updated descriptions or bug fixes to regain users.
• Category strategy. Use categoryBreakdown to see where you are concentrated. Diversifying across categories reduces dependency on a single market segment.
• Cross-reference with Quality Monitor. Pair fleet analytics with the ApifyForge Quality Monitor to correlate quality scores with run volume and revenue.

Limitations

• Run sampling limit. Only the last 100 runs per actor are sampled. For actors with very high run volumes, the 30-day failure rate may not reflect all runs.
• Revenue is estimated. Revenue calculations use runs30d * ppePrice as a proxy. Actual revenue depends on event-level billing, which may differ from 1:1 run-to-event ratios for some actors.
• Store stats lag. Apify Store statistics may be cached and lag behind real-time data by up to 24 hours.
• Single account only. This actor analyzes one Apify account per run. Multi-account analysis requires separate runs with different tokens.
• API rate limits. For very large fleets (500+ actors), the actor makes hundreds of API calls and may take several minutes. The 5-minute timeout is usually sufficient for up to 300 actors.

Frequently Asked Questions

How long does a fleet analysis take? Typically 30-120 seconds for 50-200 actors. Each actor requires 2-3 API calls (detail + runs + optional store), so a fleet of 200 actors makes roughly 400-600 API calls. The 5-minute timeout is generous for most accounts.

Is my API token stored anywhere? No. Your API token is used only during the run to make authenticated API calls. It is not logged, stored, or transmitted to any third party. The token is marked as isSecret in the input schema, meaning Apify redacts it from logs.

Can I use this with a free Apify account? Yes. The actor works with any Apify account that has a valid API token. However, free accounts may have fewer actors and lower API rate limits.

How does the weighted success rate work? The fleet-wide success rate is weighted by run volume. An actor with 10,000 runs and 99% success rate contributes more to the fleet score than an actor with 5 runs and 50% success rate. This prevents low-traffic actors from disproportionately skewing the fleet health metric.

Integration with ApifyForge Dashboard

This actor is one of five data sources that power the ApifyForge dashboard. When connected to your ApifyForge account, fleet analytics data is automatically visualized with trend charts, actor rankings, revenue projections, and health indicators. Schedule this actor to run daily and the dashboard will show historical trends, letting you track growth and spot issues before they become problems.