What 100 Apify Actors Reveal About AI and Developer Tool Demand

If you are deciding what to build on the Apify Store, the hardest question is usually not whether an idea sounds useful. It is whether people are already looking for it, whether the existing supply is good enough, and whether you can make a narrower version that is meaningfully better.

I ran the Apify Store Market Analyzer to get a current, reproducible snapshot of that problem. The run analyzed 100 public Actors from the AI and developer-tools categories, then generated a separate opportunity report from recent usage and marketplace metadata.

The short version: lead generation had the strongest niche score in this run, AI had the largest recent user volume, and the keyword signals pointed toward practical workflows around Google, Reddit, posts, and transcripts. None of those signals is a business case by itself. They are useful starting points for deciding what to inspect next.

Key takeaways

• Lead generation ranked first with an opportunity score of 46.33 and a reported demand-to-supply ratio of 1.572.
• AI had the largest recent demand signal: 72,915 users in the report's 30-day window.
• Developer tools and SEO tools had smaller supply signals than their recent demand shares, with reported ratios of 1.75 and 2.14.
• The highest-scoring keyword was google, followed by reddit and posts.
• The result is a directional market snapshot, not a revenue forecast or a complete Store census.

How the analysis was run

The source Actor supports several modes. I used findNewIdeas because the goal was to identify areas worth researching before building an Actor, rather than to produce a directory of every Store listing.

{
  "mode": "findNewIdeas",
  "categories": ["AI", "DEVELOPER_TOOLS"],
  "maxActors": 100,
  "minTotalUsers": 0,
  "includeAnalytics": true,
  "includeUnrunnableActors": true,
  "minOpportunityScore": 0
}

The run completed on 9 August 2026 at 17:40 UTC. It returned 100 unique Actor records and stored the derived SEO_REPORT separately in the Actor's key-value store. The run finished successfully in about five seconds and recorded 101 result events, including the report event.

You can inspect the run in Apify Console. The dataset and report are linked from the run, so the analysis can be checked rather than accepted as an unexplained list of claims.

Which niches showed the strongest signals?

The report combines recent user demand with a supply comparison. Its opportunity score is a bounded heuristic. A higher score means that the selected demand and supply signals looked more favorable in this snapshot. It does not mean that a new Actor will rank, earn money, or avoid competition.

Three patterns stand out.

Lead generation was the best first place to investigate

Lead generation ranked first even though AI had more than three times its 30-day users. That is the difference between total demand and relative opportunity. The report saw 23,206 recent users in lead generation and a stronger demand-to-supply relationship than in the larger AI category.

That does not mean "build a lead scraper" is a good product brief. It means a builder should look for a specific workflow inside the category: a source, role, geography, or verification step that users care about and current Actors handle poorly.

AI is large, but it is not automatically underserved

AI produced the largest demand signal in the report, with 72,915 users in the 30-day window and 668,757 total users across the analyzed records. Its ratio was close to one at 1.009, which suggests that supply was broadly keeping pace with demand in this view.

For a new AI Actor, a generic label is probably not enough. A better question is which recurring task is still awkward: extracting a specific kind of document, turning a known source into structured data, or adding a useful validation step around an existing model workflow.

Developer tools and SEO tools look smaller but more concentrated

Developer tools showed 10,559 recent users and a reported ratio of 1.75. SEO tools showed 15,266 recent users and a ratio of 2.14. These are smaller categories than AI, but the relative gap between demand and supply was stronger in this run.

That is a useful signal for a focused builder. A small category with a clear job can be easier to understand and serve than a broad category with much higher traffic. The next step would be to inspect the individual Actors, their inputs, output schemas, pricing, and reviews before choosing a project.

What the top Actor records look like

The dataset also shows why category scores need to be paired with product-level research. Several high-usage Actors already serve broad, familiar workflows.

The lesson is not that these products are easy to copy. It is that demand often gathers around a recognizable workflow, while differentiation comes from reliability, output design, coverage, speed, and a clear promise. A new Actor should make one of those dimensions obvious.

The keyword list is a better starting point than a final idea

The report's highest-scoring keyword was google, with an opportunity score of 52.53. It appeared in four supply records, while the top matching Actors averaged 5,083.3 users in the recent window. The report calculated relative demand at 6.96 times the Store baseline used by its heuristic.

The next signals were:

Keywords are useful because they describe jobs people already recognize. They are dangerous when treated as complete product specifications. "Google" could mean search results, Maps, reviews, business data, or a browser workflow. "Reddit" could mean posts, comments, communities, sentiment, or monitoring.

For a real build brief, turn the keyword into a narrow sentence:

Collect Google Maps businesses in one region, return normalized contact fields, and flag records missing an email address.

That sentence gives an Actor builder something testable: a source, a scope, an output contract, and a quality check.

A practical workflow for validating an Actor idea

Use the market snapshot as the first filter, then add evidence in this order:

1. Inspect the strongest existing Actors. Read their descriptions, inputs, schemas, pricing, ratings, and recent reviews. Look for complaints about missing fields, unstable runs, poor pagination, or confusing output.
2. Choose a narrow job. A specific source and output contract are easier to test than a category-level promise.
3. Check the target directly. Confirm that the source is reachable, that the useful data is actually present, and that the collection method respects the site's rules.
4. Define the output before the scraper. Decide which fields are required, which can be null, and what makes a record valid.
5. Run a small validation set. Compare the output with the source and record failed requests, missing fields, and duplicate behavior.
6. Repeat the market snapshot. One run is a baseline. Re-running the same input later shows whether a signal is persistent or just a temporary spike.

Cosnify is built around the same handoff from an idea to a tested Actor. You can start a guided build, create an account, or review the current credit packs before running a larger experiment.

Reproducing the run

The Actor can be called from the Apify CLI with the same input:

apify call scraper_guru/apify-store-analyzer --input '{
  "mode": "findNewIdeas",
  "categories": ["AI", "DEVELOPER_TOOLS"],
  "maxActors": 100,
  "minTotalUsers": 0,
  "includeAnalytics": true,
  "includeUnrunnableActors": true,
  "minOpportunityScore": 0
}'

For a smaller and cheaper first pass, reduce maxActors to 25 or 50. If you want a different question, change the mode or use one category at a time. Preserve the input, run date, dataset, and report together so later comparisons are meaningful.

The Apify Store remains the source of the public marketplace records. The Actor documentation explains how Actors, runs, datasets, and key-value stores fit together. The analyzer's own Store page documents its pricing, fields, and scoring caveats.

Limitations

This analysis has deliberate boundaries:

• It is one snapshot from 9 August 2026, not a time series.
• It analyzed 100 records selected from AI and developer tools, not every public Actor in the Store.
• The report uses public metadata and recent usage signals. It does not expose private revenue, retention, customer interviews, or conversion rates.
• Opportunity scores and demand-to-supply ratios are comparative heuristics. They are not probabilities of success.
• A high keyword score may describe a crowded, technically difficult, or policy-sensitive workflow.
• includeUnrunnableActors was enabled, so the dataset includes public listings that may not be suitable for automatic execution.

The responsible conclusion is therefore modest: this run tells us where to look next. It does not tell us what will definitely work.

Final take

The best Actor idea is rarely hidden in a category label. It is usually a narrow workflow inside a category with visible demand, a frustrating current experience, and an output contract that can be verified.

This snapshot gives builders a useful shortlist: investigate lead generation, inspect the concentrated demand in developer and SEO tools, and use Google, Reddit, posts, and transcript workflows as research trails. Then validate one target with real output before investing in a larger Actor.

That is the kind of evidence we plan to keep publishing: a saved input, a real run, a transparent method, and practical implications for people building on Apify.