Apify vs fastCRW: Actor Platform vs Open-Core Web-Data API (2026)

By the fastCRW team · Head-to-head · Verify Apify pricing/features independently.

Disclosure: Written by the fastCRW team; fastCRW is in the comparison. Apify's platform and pricing evolve — confirm on apify.com before deciding.

One-line difference

Apify is a scraping platform: an Actor marketplace, a cloud compute runtime, datasets, queues, and the open-source Crawlee framework. fastCRW is a single open-core API that turns URLs into clean data and can run as a ~6 MB binary on your own box. Apify sells a platform and a long tail of prebuilt site-specific scrapers; fastCRW sells a fast, predictable primitive you can self-host. Choosing between them is really choosing between "buy a platform" and "own a primitive."

Comparison table

Where Apify is genuinely better

• Prebuilt scrapers. The marketplace has scrapers for many specific sites (large e-commerce, social, search). If you need "scrape this exact well-known site" and someone already maintains an Actor for it, that is faster than writing your own logic against any general API.
• Managed orchestration. Datasets, key-value stores, request queues, scheduling, scaling — a full workflow platform. fastCRW is a stateless primitive; you bring your own scheduler and storage.
• Crawlee + Playwright depth. For complex interactive SPAs needing real browser automation, Crawlee's Playwright integration is mature. fastCRW uses a lighter fetch/render path.

Where fastCRW wins

1. Predictable, flat pricing

Apify bills on compute units plus add-ons; cost grows with Actor complexity and runtime, and is non-trivial to forecast. fastCRW is flat: 1 credit = 1 page. For continuous AI scraping (URL → clean content, repeated), flat per-page metering is far easier to budget than per-compute.

2. No platform lock-in

Actors built on Apify's storage/queue/dataset APIs are hard to migrate — you build on their platform, not your own. fastCRW is a standard REST API; the engine is open source. You can leave with a base-URL change, or run it yourself forever.

3. AI-native by default

fastCRW returns clean markdown / structured JSON and ships a built-in MCP server, so an AI agent gets scrape/crawl/map/search tools with no glue code. Apify is a general platform; AI-readiness depends on which Actor you pick and what it returns.

4. Footprint and speed

A single small Rust binary, low idle RAM, local-first with no browser stack on the hot path. It runs on a $5 VPS. There is no cloud runtime cost floor.

5. Free self-host

The fastCRW engine is AGPL-3.0 with unlimited self-host and no license fee. Scraped data and target URLs never leave your infra — a hard requirement for regulated workloads that Apify's platform cannot match at the primitive level.

Migration: Apify to fastCRW

This is a re-platforming, not a base-URL swap — Apify's model is different. The good news: most AI use of Apify is a small subset (run an Actor → get results), which maps cleanly.

1. Inventory Actors by job. Site-specific scraper, generic crawler/mapper, LLM-extraction Actor, or workflow/orchestration.
2. Map each: generic crawler/mapper → fastCRW /v1/crawl + /v1/map; extraction Actor → /v1/scrape with formats: ["json"] + schema; site-specific scraper → write the selectors/logic once against fastCRW, or keep that one Actor; orchestration → move scheduling to your own Cron/Airflow/Temporal calling fastCRW.
3. Choose hosted vs self-host. Data residency → run the binary; otherwise fastCRW Cloud.
4. Pilot the highest-compute Actor first — that is where the pay-per-compute → flat-credit saving is largest.
5. Cut over once output parity holds.

Replacing a generic crawler Actor

from firecrawl import FirecrawlApp
app = FirecrawlApp(api_key="key", api_url="http://localhost:3000")
result = app.crawl_url("https://example.com",
    params={"maxDepth": 3, "limit": 200, "formats": ["markdown"]})
for page in result["data"]:
    print(page["metadata"]["url"])

Decision guide

Platform vs primitive: the architectural decision

Underneath the feature comparison is a single architectural choice: do you want to build on a platform or build with a primitive? Apify is a platform — its datasets, queues, key-value stores, scheduling, and Actor model are designed to be the substrate your scraping system lives inside. That is powerful and, for a team that wants a managed end-to-end scraping product, a legitimate choice. But it is also the source of lock-in: code written against Apify's storage and queue APIs is bound to Apify, and leaving means rewriting that integration layer, not just swapping an endpoint.

fastCRW is a primitive. It does one bounded thing — turn URLs and sites into clean data — and assumes you own the surrounding system (your scheduler, your storage, your queue). That is more assembly up front if you have nothing, but it means no lock-in: the API is standard REST, the engine is open source, and your orchestration was always yours. For teams that already have an orchestration stack — most engineering teams do — building with a primitive avoids paying for, and being captured by, a second platform.

What actually transfers in a migration

Being concrete about migration cost matters. The parts of an Apify integration that transfer cleanly to fastCRW: the "run a scrape/crawl and get results" core (maps to /v1/scrape and /v1/crawl), and any LLM-extraction step (maps to formats: ["json"] with a schema). The parts that require rework: anything coupled to Apify Datasets, KeyValueStore, or RequestQueue, and any reliance on the marketplace for a specific site's maintained Actor. The honest estimate is therefore "small if your usage is mostly run-Actor-get-results, larger if you built deeply on platform storage primitives." Most AI scraping integrations are the former, which is why the migration is usually less work than the platform-lock-in fear suggests — but you should audit your Dataset/Queue coupling before estimating.

Latency in the agent loop

For agentic systems there is a runtime argument independent of cost. Each web-data call inside an agent loop is latency the user experiences directly, and it compounds across multi-step reasoning. Routing through a cloud Actor runtime adds queueing and cold-start variance on top of fetch time. fastCRW's single-binary Rust engine, local-first with no browser stack on the hot path, keeps tool calls tight. When an agent makes ten web calls to answer one question, the difference between a snappy primitive and a variable-latency platform runtime is the difference between a responsive assistant and a sluggish one. For interactive AI products this is often a stronger argument than the pricing one.

Getting started

docker run -p 3000:3000 ghcr.io/us/crw:latest

Free self-host (AGPL-3.0) or fastCRW Cloud — one-time 500 free credits, no card. GitHub.

Further reading

• Apify alternatives
• Apify pricing explained
• Best self-hosted scrapers