Browse AI vs Apify: No-Code Robots or a Cloud Scraping Platform (2026)

Apify's Store banner advertised 53,284 published scrapers when we read it on 2026-07-27. Browse AI lists around 250 prebuilt robots and expects you to record the rest yourself by clicking through the page you want. Both sit in the same no-code scrapers category on this site, and that gap is the first sign they are not the same kind of product: one is a recorder that trains a robot on your clicks and then watches the page for changes, the other is a marketplace of ready-made scrapers plus a cloud runtime you can deploy your own code into. Teams end up comparing them when the requirement is recurring monitoring rather than a single pull, and the comparison usually gets decided by something neither marketing page talks about: what happens in month six.

Proxyway lists both under no-code scrapers. That framing is accurate and incomplete. It puts a recorder and a platform in the same bucket without addressing the platform question, which is who fixes the thing when the target site ships a redesign.

Two different bets

Browse AI's bet is that the person who needs the data should be the person who sets up the extraction. You open the target page inside the recorder, click the fields you want, and the robot replays those interactions on a schedule. The product's centre of gravity is monitoring: the site markets AI change detection with alerts, schedules down to hourly on the lower plans, and a catalogue of what it calls 250+ prebuilt robots for common targets like Amazon, eBay and Airbnb, plus 7,000+ integrations covering Google Sheets, Airtable, Zapier, Make and plain webhooks. Those counts are Browse AI's own, read from its homepage on 2026-07-27.

Apify's bet is that someone has already written the scraper you need. Its Store banner advertised 53,284 Actors when read on 2026-07-27. An Actor is a containerised program with an input schema, published by Apify or by a third-party developer, that you run on Apify's infrastructure. The company describes itself as "the largest marketplace of tools for AI" and the platform underneath handles scheduling with cron expressions, run monitoring, alerting, storage and proxies. Apify also maintains Crawlee, the open-source crawling library, which tells you where the platform's roots are.

The practical consequence: Browse AI is a tool a marketing analyst can drive alone. Apify is a platform an analyst can use and an engineer can extend, and the second half of that sentence is most of its value.

Head to head

All figures in this table come from each vendor's own pricing, docs or marketing pages, read on 2026-07-27. Neither is a measurement we made.

Setup effort in the first hour

Browse AI wins the first hour, and it is not close. If your target is a list page with a predictable structure, you record the extraction and have rows within minutes. There is no account plumbing, no schema, no deciding which of several similar Actors is the maintained one. For a non-engineer this is the entire proposition, and the product delivers it.

Apify's first hour splits into two cases. If a good Actor already exists for your target, you fill in a form and you are done nearly as fast as with Browse AI. If it does not, you fall back to configuring a generic crawler Actor with selectors, which is closer to writing code than the no-code label implies. The Store's quality also varies. Popularity and recent update dates are the signals worth reading, because a two-year-old Actor with thirty users is not the same purchase as one the publisher patches weekly.

Our guide on where no-code scrapers stop working covers the general shape of that ceiling. The short version for this pairing: when you run out of road on Browse AI you leave the product, and when you run out of road on Apify you write an Actor and stay.

Month six

Recording a robot takes an afternoon. Keeping a fleet of them alive is the standing cost nobody quotes you.

Browse AI markets change detection as a feature and its site says a robot "automatically adapts so the code doesn't break". Treat that as a vendor claim, because it is one, and we have no third-party measurement of how often the adaptation succeeds. Structurally, a robot trained by clicking is bound to what you clicked. A layout change that moves your fields inside a new container is exactly the failure mode a recorder is worst at, and the recovery is you, in the recorder, doing it again. That work does not scale linearly: ten robots is a chore you absorb, forty is somebody's part-time job.

Apify moves that work off your desk when you are running a popular third-party Actor. The publisher has an incentive to patch quickly, because their revenue depends on the Actor working, and a widely used Actor breaks for hundreds of users at once. You often find the fix already shipped. The cost of that arrangement is dependency: you have no control over how fast a maintainer responds, and an abandoned Actor is worse than a robot you can re-record, because you cannot fix it without forking it. Forking is possible, which is the point, but forking requires an engineer, and at that moment you have left the no-code comparison entirely.

Neither answer is better in the abstract. If your targets are five stable sites, Browse AI's re-record cost is trivial and Apify's maintainer risk is unnecessary. If your targets are forty sites of varying volatility, Apify's distribution of maintenance across publishers is worth real money.

Monitoring and alerting

This is where Browse AI is clearly the stronger product, because it is what the product is for. Change detection, screenshot capture and alerting are first-class, and the extraction exists partly to feed them. If your requirement is "tell me when this competitor's price, job listing or policy page changes", Browse AI is built around that sentence. Schedule granularity is tied to plan: Browse AI's pricing page lists an hourly minimum on Free and Personal and a five-minute minimum on Professional, read 2026-07-27.

Apify has monitoring too, but it is monitoring of a different noun. Its platform watches your runs: whether the job succeeded, whether output volume looks wrong, whether performance degraded. That is operational monitoring, and it is the right kind for a data pipeline. It is not change detection on a page. You can build page-level change detection on Apify by diffing dataset output between runs, and plenty of Actors do exactly that, but you are assembling it rather than buying it.

Our guide to no-code price monitoring walks the recurring-monitoring use case in more detail.

Pricing shape, not price

The pricing models are not comparable at the rate level, and anyone who puts them in the same column is doing you a disservice. What is comparable is the shape.

Browse AI bills credits against output. Its pricing page, read 2026-07-27, defines a credit as extracting 10 rows of data from a page or capturing a screenshot, and lists Free at $0 with 50 monthly credits, Personal at $19 per month billed annually, Professional at $69 per month billed annually with a credit allowance that scales, and Premium starting at $500 per month. It also notes that premium sites carry a higher credit minimum per run, which is how the harder targets get priced in. Because cost tracks rows, a spreadsheet of expected volume gives you a usable forecast.

Apify bills platform usage. Its pricing page, read 2026-07-27, lists Free at $0, Starter at $29 per month, Scale at $199 and Business at $999, with usage billed in compute units defined as 1 GB of RAM running for 1 hour, priced at $0.2 per CU on Free and Starter, $0.16 on Scale and $0.13 on Business. Store Actors add their own charges on top of, or bundled into, that usage. Apify's own documentation for running Store Actors is candid that pay-per-usage Actors are "quite difficult to estimate" beforehand and recommends testing on a limited scope first, which is honest and also a warning.

One wrinkle worth flagging, because the brief we started from assumed otherwise. Apify's documentation page on running Store Actors, read 2026-07-27, describes the monthly Actor rental model as being retired, stating that new rentals are prohibited after April 2026 with full retirement by October 2026. The publisher-facing monetisation documentation read the same day still lists rental among the available models without mentioning any sunset. Apify has not, as far as we can see from its docs alone, published a single consolidated statement resolving the two, so treat the rental timeline as in flux and confirm it with Apify before you build a budget on it. What is clear either way is that the platform is moving toward pay-per-event and pay-per-usage, where charges attach to results generated or resources consumed rather than to a flat monthly rent per Actor.

The general mechanics of credit-metered scraping billing are covered in our guide to scraping API credit pricing, and the same arithmetic applies here.

Proxies and blocking

Apify sells proxy access as an explicit part of the platform. Its documentation describes datacenter, residential and Google SERP proxy types, with rotation behaviour spelled out: a different IP per browser or per HTTP request, and an optional session parameter that holds an IP across connections, persisting roughly 26 hours on datacenter and around 30 minutes on residential. Its pricing page includes datacenter IPs on every plan, from 5 on Free to 500 on Business, and prices residential traffic per gigabyte with no included allowance. If you already understand residential proxies and want to control exit country or session stickiness, that lever exists.

Browse AI does not expose that lever. Its pricing signals the same underlying problem differently, by charging a higher credit minimum on premium sites. That is a defensible product decision for the audience it serves, since most non-engineers do not want to think about IP pools, but it means you cannot tune your way out of a blocking problem. You can only pay the higher credit rate and hope the vendor's infrastructure is enough.

For genuinely hostile targets, neither tool is the endpoint. A team fighting a serious anti-bot system usually ends up buying from the proxy networks category directly, whether that is Bright Data or Oxylabs, or moving to a scraping API that specialises in unblocking. Our comparison of Bright Data and Oxylabs covers that tier.

The escape hatch

Requirements harden over time. A monitoring job that started as "watch these twelve pages" becomes "watch four thousand, join them against our catalogue and land them in the warehouse". At that point the question is whether your tool can come with you.

Apify can. You write an Actor in JavaScript or Python, push it to the same platform, and it inherits the scheduling, storage, proxies and monitoring you were already using. The Store Actor you were renting becomes a fork you maintain. Nothing about your orchestration changes. That continuity is the strongest argument for Apify over any pure no-code option, and it is the reason engineering teams tolerate its rougher first hour.

Browse AI has no equivalent. It has an API for triggering robots and pulling results, which is useful for wiring output into other systems, but there is no path from a recorded robot to custom extraction logic. When you outgrow it, you migrate: to Apify, to a framework like Scrapy, or to an AI extraction service that takes a schema instead of selectors. Our note on the AI squeeze on no-code scrapers covers where that third option is heading, and the Scrapy vs Crawlee comparison covers the framework tier if you land there.

What each one is bad at

Browse AI is bad at breadth and bad at control. It gives you no proxy configuration, no code path, and a maintenance burden that lands entirely on you and grows with your robot count. Its credit model, while easy to forecast, punishes high-row extraction: a job producing hundreds of thousands of rows is not what the pricing was designed around. And the self-healing claim is unverified by anyone outside the company.

Apify is bad at the first hour for anyone who is not technical, and bad at cost predictability. Compute-unit billing plus per-Actor charges means two variables move at once, and the vendor's own docs admit estimation is hard. Store quality is uneven, and choosing between six Actors that scrape the same site is a judgement call with no obvious signal beyond user counts and update dates. Its monitoring watches jobs, not pages, so the change-alert use case needs assembly. The rental-model transition adds a second layer of uncertainty to any budget built this quarter.

Pick Browse AI when, pick Apify when

Pick Browse AI when non-engineers own the workflow and the deliverable is an alert rather than a dataset. A small number of stable targets, a recurring schedule, and a Slack message when something changes: that is the shape the product was built for, and paying for it beats building it.

Pick Apify when you want prebuilt scrapers you can fork, real proxies underneath, and room to drop into code as requirements harden. If there is an engineer within reach of this project, even part-time, Apify's ceiling is high enough that you are unlikely to migrate off it, and that is worth more than a faster first hour.

Who each is wrong for

Browse AI is the wrong buy for an engineering team that already knows it will need custom extraction logic within a year. You will pay for a recorder, hit the wall, and migrate anyway, having built no reusable infrastructure. It is also wrong for anyone whose targets fight back hard, because you cannot reach the layer where that fight is won.

Apify is the wrong buy for a solo analyst with no engineering support and a monitoring requirement. You will spend the evaluation choosing between Actors, the first month learning compute-unit billing, and you will still not have change alerts on a page. Browse AI or a peer like Octoparse gets you there in an afternoon.

If neither fits, the problem is probably not no-code at all, and our guide to choosing web scraping tools is the wider shortlist.