By the fastCRW team · Features and endpoint surface verified 2026-05-18 · Verify independently before buying.
Disclosure: we build fastCRW. This is a vendor-authored comparison, so weight it accordingly, and verify the endpoint details independently before you build against any of the three.
Apify vs Firecrawl vs fastCRW: three philosophies, not three clones
If you are comparing Apify, Firecrawl, and fastCRW on features, the first thing to internalize is that they are not the same kind of product. They sit at three different altitudes. Apify is a platform — a marketplace of prebuilt scrapers plus a compute layer to run them. Firecrawl is a broad managed API — a wide surface of endpoints for AI web data. fastCRW is a focused primitive — a single Firecrawl-compatible Rust binary that does scrape, crawl, map, and search and intentionally stops there. Matching the tool to your use case matters more than counting endpoints, so this guide is organized around scope, not a checkbox race.
Apify: an Actor marketplace and compute platform
Apify's defining feature is the Actor marketplace: thousands of prebuilt, community- and vendor-published scrapers for specific sites and tasks (e-commerce listings, social profiles, SERPs, maps). You are not just buying an API — you are renting a runtime that schedules, scales, and stores the output of those Actors, plus the Crawlee SDK to build your own. The breadth is the point. If a maintained Actor already exists for your exact target, that is genuine leverage no generic API gives you.
Firecrawl: a broad managed API surface
Firecrawl is the category reference for AI-oriented web data. Its surface is wide: scrape, crawl, map, search, extract, plus cloud-only specialties like agentic endpoints and page interaction. It defines the request and response shapes that the rest of the ecosystem (fastCRW included) implements against. You trade self-host weight for a managed surface that covers most jobs without you assembling anything.
fastCRW: a focused Firecrawl-compatible primitive
fastCRW deliberately implements the overlap surface that most pipelines actually use — /v1/scrape, /v1/crawl, /v1/map, /v1/search — as a single statically-linked Rust binary (roughly an 8 MB Docker image, one container) under AGPL-3.0. The API is Firecrawl-compatible, so switching is a base-URL swap. The thesis is narrow and honest: a lean primitive you can own and self-host beats feature-stacking you cannot. See Firecrawl vs fastCRW for the 1:1 version of this argument and Apify vs fastCRW for the platform angle.
Core endpoint scope side by side
Here is the honest feature surface. Note the asymmetry: Apify's "endpoints" are really Actors and a run API; the scrape/crawl semantics live inside whichever Actor you pick. Firecrawl and fastCRW expose named REST endpoints directly.
| Capability | Apify | Firecrawl | fastCRW |
|---|---|---|---|
| Shape | Marketplace + compute | Broad managed API | Focused open-core API |
| Scrape one URL | Via an Actor | /v1/scrape | /v1/scrape |
| Crawl a site | Via a crawler Actor | /v1/crawl | /v1/crawl (async job) |
| Map/discover URLs | Via an Actor | /v1/map | /v1/map |
| Web search | Via a SERP Actor | /v1/search | /v1/search |
| JSON / schema extraction | Actor-dependent | /v1/extract + scrape | formats: ["json"] + jsonSchema, or /v1/extract across up to 50 URLs |
| Self-host the whole engine | Partial (Crawlee SDK open; platform closed) | AGPL-3.0, multi-service stack | AGPL-3.0, single ~8 MB binary |
Scrape, crawl, map, search across all three
All three can scrape one page, crawl a site, enumerate URLs, and run a web search — the difference is how. On fastCRW these are four named POST endpoints with a consistent request shape. On Firecrawl they are the same four (plus more) under the API it pioneered. On Apify each is an Actor you select, configure, and run; the platform then schedules and stores the result. For a developer who wants "give me clean content for these URLs," the named-endpoint model is less to assemble; for "scrape this specific awkward site that already has a maintained Actor," Apify's marketplace can be the shortest path.
LLM extraction: formats json plus jsonSchema
fastCRW does structured extraction by passing formats: ["json"] with a jsonSchema to /v1/scrape — a request costs the 1-credit scrape plus the LLM token cost, billed as usage-metered LLM credits, and returns typed output. /v1/extract wraps the same path across up to 50 URLs in a single call, so multi-page structured extraction is one request rather than a loop. One note: fastCRW's LLM-based JSON extraction is a managed feature available on paid plans, alongside the managed search answer mode. Apify's extraction quality depends entirely on the Actor you chose.
Where the endpoint shapes line up
Because fastCRW targets Firecrawl's request/response shapes, a client written for Firecrawl's scrape/crawl/map/search usually works against fastCRW after a base-URL swap. That is the compatibility wedge: you are not learning a third API. The caveat is that response field names and error envelopes have minor divergence, so validate the short known list before cutover — details in Firecrawl API compatibility.
What fastCRW adds beyond the shared surface
Beyond the four shared endpoints, fastCRW ships a few things a plain endpoint list doesn't capture. /v1/extract batches structured extraction across up to 50 URLs in one call, so multi-page extraction is a single request rather than a loop. The chrome renderer's CDP screenshot capture is a first-class output alongside markdown and JSON. And the Research API (/v1/search/research/papers, plus /papers/{id}, /papers/{id}/similar, and /v1/search/research/github) composes search and scrape into a citation-aware research primitive, so you are not wiring that loop from scratch on the flat-credit primitives.
The open-core advantage in feature terms
Where fastCRW's narrow surface turns into an actual advantage is ownership. The features it does have are not gated, metered behind a marketplace, or locked to a hosted plan.
Self-host the whole engine, no feature gating by tier
fastCRW ships as one static Rust binary — roughly an 8 MB Docker image in one container (plus an optional sidecar), versus Firecrawl's multi-service self-host stack measured in the ~2–3 GB range across five containers. Crucially, every endpoint works on every deployment: there is no "this feature is Cloud-only" tier wall on the open-core surface, and self-hosting the AGPL-3.0 engine costs $0 in license — you pay only for your own server. Apify's Crawlee SDK is open source, but the scheduling/marketplace platform that gives Apify its value is closed and hosted.
Drop-in after a base-URL swap from Firecrawl
Because the API is Firecrawl-compatible, the feature surface you adopt is reversible. Write your client against the shared scrape/crawl/map/search shape and the choice of backend becomes a runtime config value rather than a rewrite. That portability is itself a feature: you are not locked into one vendor's endpoint dialect. Compare that to migrating off an Apify Actor, where your integration is shaped around one Actor's specific input/output contract.
Picking fastCRW for scrape, crawl, map, and search
fastCRW is the right call when scrape/crawl/map/search is the actual job: you want a hard cost ceiling via self-host, you need scraped content and target URLs to stay on your own infrastructure, and you want a Firecrawl-compatible surface that keeps the decision reversible. Structured extraction across up to 50 URLs, CDP screenshots, and the search-plus-scrape Research API are included in that same focused surface, not held back for a separate product tier.
Sources
- fastCRW endpoint surface, footprint, and honest gaps: github.com/us/crw (open-core README) · canonical fact sheet verified 2026-05-18
- Firecrawl features and docs: docs.firecrawl.dev (verified 2026-05-18)
- Apify platform and Actor marketplace: apify.com (verified 2026-05-18)
Related: Firecrawl vs fastCRW · Apify vs fastCRW · Firecrawl API compatibility · Apify alternatives