Apify and Anakin both give developers a way to pull structured data from the web without maintaining scraping infrastructure themselves. Apify does it through a marketplace of over 43,000 pre-built "actors." Anakin does it through a set of eight purpose-built products under one API key.
The products overlap on the surface: both scrape pages, crawl domains, and run browsers at scale. The difference that actually matters shows up on the invoice, and in what happens when something quietly breaks.
What Apify does well
Apify has been around since 2015, longer than almost anyone else in web scraping, and it shows. The Apify Store has a pre-built actor for nearly every major site: Amazon listings, LinkedIn profiles, Google Maps results, Instagram posts. If a common scraping task exists, someone on Apify has probably already built and published a working actor for it.
Apify also maintains Crawlee, an open-source scraping library used well beyond the Apify platform itself. Developers who build locally with Crawlee tend to land on Apify's cloud when they need to deploy at scale, which gives the company a strong funnel independent of its own marketing.
For teams that want a pre-built solution and don't mind managing runtime costs, that marketplace depth is a real advantage. It's also the reason Apify shows up first in most "web scraping platform" searches.
Where the billing gets complicated
Apify charges for compute units: one CU equals one GB of RAM running for one hour. A lightweight HTTP scrape might cost a fraction of a CU. A browser-heavy actor running for ten minutes on 2GB of RAM burns roughly 0.33 CU, somewhere around $0.07 to $0.10 per run before any other fees apply. (Apify pricing 2026)
That "before any other fees" part is where it gets messy. Many of the most popular Store actors, including the Google Maps and LinkedIn scrapers people reach for most, charge an additional $3 to $5 per 1,000 results on top of the compute units they consume. A $29 Starter plan can run out fast if you're using actors with per-result pricing layered on compute costs. (use-apify.com pricing docs)
The bigger issue for anyone trying to forecast a budget: you don't know what a job will cost until it finishes. Compute units accrue as the actor runs, and if the actor fails partway through, you've still paid for the RAM-hours it used getting there. There's no way to know the final number before you hit run.
How Anakin's pricing works instead
Anakin charges credits per successful outcome, not per unit of compute time. You know the cost of an action before you call it, because the price is tied to what the action does, not how long a server happened to run it. If a job fails, it costs nothing. There's no partial charge for compute burned on a run that didn't complete.
This isn't a minor accounting difference. It changes how you plan. A team running thousands of scraping jobs a day can budget against a known per-call rate instead of reconciling a monthly compute bill that swings with how many jobs failed, retried, or ran longer than expected.
Anakin's products work asynchronously too: you submit a job, get a job_id back immediately, and poll for the result. Apify's actors run to completion and hold the platform's compute the whole time, which is a fundamentally different execution model for anyone building a high-throughput pipeline rather than running scheduled batch jobs.
Who catches a broken actor first
Apify runs automated tests on every Store actor daily, checking whether it still completes a default run within five minutes. Fail three days in a row and the actor gets flagged "Under Maintenance." Keep failing, and Apify deprecates it automatically, but not until it's been broken for more than a month. (Apify help center)
That's the safety net working as designed, and it still leaves a wide gap. A site restructures its layout, a community actor starts returning empty fields or malformed rows, and your pipeline can run on degraded data for weeks before Apify's system even flags it, let alone before anyone fixes it. The job still reports success. Compute units still get billed. The data just isn't right anymore, and nothing in that flow tells you so.
Wire takes a different approach to the same problem: Anakin maintains the integrations it builds. When a target site changes, the endpoint your agent calls stays the same, because the fix happens on Anakin's side, not in a queue waiting on whichever community developer built the actor you happened to pick. Wire's actions are also built to self-heal: most breaks from a site change get detected and repaired automatically, and an engineer only steps in for the cases the automated fix can't resolve on its own.
Beyond scraping: what the marketplace model can't cover
Apify's actor marketplace is built for reading the web: extracting listings, prices, posts, and profiles. It has no equivalent for running pre-built automation actions against a site on an agent's behalf.
Anakin covers that gap through Wire, a catalogue of pre-built actions across hundreds of popular websites. Each action handles the browser rendering, authentication, and structured extraction behind it, so an agent calls an action_id instead of building and maintaining its own actor. Apify has nothing comparable in its product line, since actors are single-purpose scrapers you write or adopt from the Store, not a managed, pre-built action catalogue.
Where the catalogues actually overlap
This isn't just an abstract difference. Wire's catalogue and Apify's Store target a lot of the same platforms. As of this writing, Wire covers 909 sites and 4,934 actions, and a meaningful chunk overlaps directly with Apify's most-used Store actors: Amazon (15 actions), Walmart, eBay, Zillow, Airbnb, Reddit, YouTube, GitHub, Etsy, Yelp, Tripadvisor, Booking.com, and Indeed all show up in both catalogues. (Wire catalogue)
The mechanism is where the two diverge. An Apify actor targeting Amazon opens a Chromium instance, navigates the page, and parses the DOM, which is why it burns compute units per run and can break the moment Amazon changes a CSS selector. Wire's Amazon actions call the same background API Amazon's own frontend uses, so there's no browser involved and nothing to break when the page gets redesigned, only the underlying data contract, which changes far less often than the markup does.
Apify still wins outright on a specific, important list. LinkedIn, Google Maps, Instagram, Facebook, Pinterest, Twitter/X, Glassdoor, and Crunchbase aren't in Wire's catalogue yet. These happen to be the platforms with the heaviest anti-bot defenses and social-graph logins, and they're also some of Apify's most-used Store actors. If your pipeline depends specifically on one of those, Apify is the more complete option today.
That gap is also where the catalogue is moving. Wire's coverage isn't static: it went from 812 sites and 4,361 actions in June 2026 to 909 sites and 4,934 actions as of this writing, about 12% growth in six weeks. (Wire catalogue) Any site can be requested directly through the catalogue page, and new actions typically ship within days rather than quarters, so the list of what Apify still covers alone gets shorter over time, not longer.
The same gap shows up in how each platform handles logged-in access. Apify has no native session management. The closest equivalent is community actors like Login Session, which automate a login and save the resulting cookies, localStorage, and sessionStorage to a named pool for reuse elsewhere. It works, but sessions cap out at 24 hours because of proxy constraints, and the actor can't get through two-factor authentication or a captcha on its own. (pocesar/login-session on Apify)
Anakin handles this natively instead of through a bolted-on actor. You save an account once as a named identity, and Wire resolves it inside an encrypted vault at request time. Credentials are AES-256 encrypted at rest and injected at runtime; the raw secret is never returned by the API. Browser Sessions extend the same idea to plain scraping, keeping authenticated state alive between calls instead of forcing every run to log in again.
Anakin vs Apify at a glance
| Anakin | Apify | |
|---|---|---|
| Billing model | Credits per successful outcome | Compute units (RAM x time) plus per-actor fees |
| Cost known before running | Yes | No, calculated after the job completes |
| Cost of a failed job | Zero | Compute units already consumed are still billed |
| Execution pattern | Async: submit, get job ID, poll | Actor runs to completion, holds compute the whole time |
| Site coverage overlap | 909 sites, incl. Amazon, Walmart, eBay, Zillow, Airbnb | 43,000+ actors, incl. the same sites plus LinkedIn, Google Maps, Instagram |
| Persistent authenticated sessions | Yes, Browser Sessions | No native support, community actors cap sessions at 24 hours |
| Credential storage | Named identity vault, AES-256 encrypted at rest | Cookies/localStorage in a named key-value store (community actor) |
| Broken integration detection | Managed, endpoint stays stable when a site changes | Up to 30 days of continuous failure before automatic deprecation |
| Pre-built action catalogue | Yes, through Wire (hundreds of sites) | No, actors are built per-task from the Store or by hand |
| Compliance | SOC 2 Type II, ISO 27001:2022 | SOC 2 Type II (Apify 2026) |
| Entry pricing | $79/mo (Scale) | $29/mo (Starter) |
Values are editorial assessments based on available documentation, not independently benchmarked figures.
When Apify is the better fit
If you need a scraper for a specific, well-known site and don't want to write any code, or if that site is one of the ones Wire doesn't cover yet, like LinkedIn or Google Maps, the Store's breadth is hard to beat. For teams comfortable managing a compute-unit budget and running scheduled batch jobs rather than high-throughput pipelines, Apify's model works fine too.
When Anakin is the better fit
If you're building a production pipeline where cost predictability matters, where jobs run continuously rather than on a schedule, or where your target sites overlap with Wire's catalogue, Anakin's model is built for that. Credits per outcome mean your budget scales with what actually succeeds, not with how much compute a job happened to consume trying. And when your agents eventually need to take actions on a platform instead of just extracting from it, that capability is already there.
Get started with Anakin and see the pricing model that charges for what worked, not for how long it ran trying.