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What the 11 Actors do—and what Turov says they cost
The list below reflects prices and descriptions in Turov’s September 29, 2026 article, not a verified current Apify Store price check. His account describes a mix of one-time data extraction and recurring monitoring tools.
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| Actor | What it returns or monitors | Price reported by Turov |
|---|---|---|
| Google Maps Business Scraper | Business names, phones, websites, ratings, reviews, and GPS | $1.00 per run plus $0.03 per business |
| TikTok Profile & Video Scraper | Followers, likes, bio, and per-video statistics | $0.01 per profile plus $0.002 per video |
| Instagram Profile Scraper | Followers, bio, verified status, and engagement | $0.01 per profile |
| YouTube Video & Channel Scraper | Views, likes, subscribers, and search results | $0.002 per video |
| LinkedIn Profile Scraper | Headlines, companies, skills, and experience | $0.02 per profile |
| RAG Web Browser | Clean Markdown from a URL, plus Google search | $0.003 per page |
| Fuel Prices France API | Prices, station coverage reported as 9,800 stations, and GPS | $0.20 per run plus $0.01 per 1,000 stations |
| Hotel Rate Monitoring | Competitor rates and parity checks | Fractions of a cent per item; no exact rate stated |
| API Breaking-Change Radar | OpenAPI spec diffs, changelog classification, and alerts | $0.50 per run plus $0.50 per breaking change |
| Review Radar | New Google reviews for a business and Slack alerts | $0.25 per run plus $0.01 per new review |
| Review Pitch Generator | Turns poor reviews into a sales report | $0.25 per run plus $0.10 per pitch |
These figures are the author’s reported September 2026 prices and examples; they should not be treated as current marketplace rates. [Turov’s article]
The Tool Desk
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In Turov’s described workflow, an agent searches the Apify Store through MCP, reads an Actor’s README and input schema, sends structured JSON, and receives structured output. He also describes a synchronous HTTP API call as an alternative. The key is that the agent needs enough context to choose the right tool and use it correctly—not merely an endpoint it can reach.
#1 Best Overall
- Explain when to use it. A clear “Use this tool when…” section helps an agent match a user’s request to the Actor.
- Describe every input field. Field-level guidance helps the model construct valid input rather than guessing at names, formats, or options.
- Document every output field. The agent needs to know what returned values mean in order to answer the user or pass data to another step.
Turov’s shorthand is, “Your README is written for an LLM, not just humans.” That does not make human-readable documentation irrelevant: both people and models benefit when the tool’s purpose, input contract, and output are explicit.
Price for the run, not only the results
A per-result charge is easy to explain, but Turov says it left fixed browser and proxy costs uncovered when a run returned few results. His example was one Google Maps run that found 16 businesses: at $0.005 per business, he reports earning $0.08 against $1.23 in Playwright and proxy costs. After adding a run-started event and a $1.00 run fee, he says the 16 businesses were billed at $1.48.
Rank #2
- Used Book in Good Condition
That incident points to a basic pricing distinction: some costs arise as soon as a run starts, while others grow with the amount of useful data returned. A run fee can address the first category; per-result charges can scale with the second. The right balance depends on the Actor’s actual cost structure and what customers consider a fair charge.
Turov also reports a 20% Apify fee and several platform-specific billing behaviors. Those are author-reported details from 2026, not a current general fee schedule or guarantee of present platform rules. He says new Actors must use PAY_PER_EVENT rather than PRICE_PER_DATASET_ITEM, recommends charging immediately before pushing each result from the event handler, and advises checking chargedEventCounts after a run. He warns that charging after a crawler finishes may fail if the event loop has closed.
- Test billing with a dry-run guard: Turov says running your own pay-per-event Actor can charge its owner.
- Check charged-event counts against expected results rather than assuming a successful run charged correctly.
- Account for fixed execution costs as well as marginal costs per result.
- Confirm current Apify billing rules and fees before adopting these implementation details; platform behavior can change.
Scrapers and monitors solve different problems
Most of the listed tools extract a dataset on request. Turov calls the final three—API Breaking-Change Radar, Review Radar, and Review Pitch Generator—monitors because they retain state between runs and charge when they find something. That changes the product’s value proposition: a scraper answers a one-time question, while a monitor can keep checking for a recurring change or opportunity.
| Design question | One-time scraper | Stateful monitor |
|---|---|---|
| What happens between calls? | Typically returns data collected for that run | Retains state across runs, according to Turov’s description |
| What triggers recurring value? | A user asks for another extraction | A new review, breaking change, or other detected event |
| What pricing basis may fit? | Per run, per item, or both | Per run and/or per detected event |
| When is it useful? | For a bounded lookup or list-building task | When a user needs ongoing alerts or checks |
Recurring demand is not automatic: the monitor still needs a meaningful signal, sensible alerting, and a clear explanation of what is billed. Turov’s examples illustrate the model; they do not establish that monitors generally earn more than scrapers.
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Turov reports that Apify Store automatically runs Actors with prefilled inputs and that three failures can lead to an “Under maintenance” status and eventual deprecation. Because platform QA rules may change, developers should check current official guidance rather than relying on this 2026 account alone.
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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsHis practical recommendations focus on making automated checks inexpensive and predictable:
Best Value
- Handle empty input gracefully so a test invocation does not crash immediately.
- Keep prefilled QA inputs small to avoid unnecessary runtime and costs.
- Use dry-run behavior for demos and protect charge calls with a dry-run flag.
- Inspect item counts and sample output even when the run reports success; a green status does not prove that useful data was returned.
- Pin dependencies so an upstream release is less likely to break a working Actor unexpectedly.
He also describes several implementation problems in his own setup: Google Maps navigation hanging until heavy resources were aborted, French decimal and thousands separators requiring careful parsing, residential proxies helping his Maps Actor, small batches, and retries after failed runs. These are experience-specific troubleshooting notes, not universal requirements for every scraper. His reported dependency ranges—apify>=3.2,<4 and crawlee>=1.7,<2—are version-sensitive and should be checked against current package and platform documentation before reuse. [Turov’s article]
Choose a narrow use case and make the tool legible
Turov recommends specific niche titles, where he believes competition may be lower, and documentation written for agent comprehension. He also describes using his Maps Actor himself to build lead lists. These are his recommendations and example, not measured evidence about marketplace competition or typical commercial returns.
For a new tool idea, the practical questions are whether the user’s task can be expressed as structured input, whether the returned data is clear enough for an agent to act on, and whether the likely run pattern supports the costs. A narrow request such as finding plumbers in a named city with phone numbers is easier to specify than a vague request to “find leads.” A well-documented schema lets the model turn the first request into a reproducible tool call.
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Turov’s article is a first-person build-and-monetization account, so its prices, platform incidents, earnings, and technical observations should be read as reported experience. It does not establish a general market benchmark, the current price or availability of any Actor, or the present state of Apify’s fees, QA policy, MCP behavior, or dependency compatibility. For anyone building an agent-callable data product, its strongest transferable lesson is about product design: document the tool as carefully as its endpoint, and price the work the system performs even when the output is small.
Quick Recap
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
