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Firecrawl is usually the better fit when your team wants a focused API for search, scraping, crawling, and AI-ready output. Apify is usually the better fit when you need reusable scraping applications—called Actors—plus cloud storage, proxies, schedules, integrations, monitoring, and a marketplace. Neither product is a proven universal winner. Choose after testing your actual sites, output schema, operational ownership, and total cost.
Firecrawl and Apify solve different problems
The most important distinction is product shape, not a feature checklist.
Firecrawl: endpoint-oriented web data
Firecrawl presents a set of web-data operations: search the live web, scrape a page, crawl a site, map URLs, and return content in formats intended for AI and downstream data workflows. Its crawl workflow discovers subpages and can return Markdown or structured data. Search can return ranked URLs and snippets and, optionally, rendered page content in the same workflow.
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Apify: reusable Actors and a cloud platform
Apify centers its platform on Actors: packaged web-scraping or automation tools that users can run, develop, share, and publish. The platform adds storage for run results, proxy services, schedules, integrations, monitoring, collaboration and security features, APIs, JavaScript and Python clients, and an MCP server. Its Store provides ready-made Actors for particular websites and jobs.
Apify also documents Crawlee, a separate open-source Node.js and Python library for crawling, scraping, and browser automation. That gives engineering teams a path to build custom Actors rather than only consuming Store tools.
Feature and workflow comparison
| Decision axis | Firecrawl | Apify | What to evaluate |
|---|---|---|---|
| Primary model | API operations for search, scrape, crawl and map | Reusable Actors plus a managed platform and Store | Do you want endpoint calls or deployable, reusable jobs? |
| Typical output | Clean Markdown, structured data, HTML, links, metadata and, in crawl workflows, screenshots and other formats | Actor-defined datasets, key-value results and files through platform storage | Validate the exact schema, pagination and export format your pipeline needs. |
| Automation | Your application normally supplies orchestration around API calls | Schedules, monitoring, integrations and run management are platform features | Compare engineering effort, not only request price. |
| Hard-site tooling | Hosted service includes Fire-engine proxy and anti-bot infrastructure; self-hosting excludes that layer | Documentation describes proxy and anti-scraping resources, with behavior varying by Actor and target | Run permitted tests against your real domains; neither source establishes universal bypass success. |
| Deployment | Managed service or self-hosted open-source stack | Managed cloud platform; Crawlee is available separately as open source | Decide who owns browser infrastructure, proxies, upgrades and incident response. |
Which should an AI team choose?
Choose Firecrawl when an API is the product boundary
- Your application needs search plus page content without adopting a marketplace tool.
- You want a consistent Markdown or structured response for retrieval-augmented generation, extraction or indexing.
- You already operate queues, retries, persistence and observability.
- You prefer a hosted endpoint for proxy and anti-bot handling rather than operating that layer yourself.
Firecrawl Search can filter results by category, domain, location or time. Its crawl operation can emit Markdown, JSON, HTML, screenshots, links and metadata, but confirm which options add credits before setting a high-volume job.
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Choose Apify when the job should be a reusable application
- A Store Actor already matches the target site or business process.
- You need schedules, run history, datasets, files, integrations or platform monitoring in one place.
- Several teams will run, modify or publish the same scraper.
- You need browser automation and are prepared to tune an Actor for a difficult site.
Apify is particularly attractive when operational repeatability matters as much as extraction. A team can start with an existing Actor, inspect its inputs and outputs, then fork or develop one with Crawlee when the packaged behavior is insufficient.
Outputs, extraction and integration details
Firecrawl output questions
Before implementation, specify whether each operation returns Markdown, HTML, JSON, screenshots, links, metadata or rendered content. Crawl jobs can span many pages, so define URL discovery rules, maximum pages, duplicate handling, and whether PDF parsing or JSON mode is required. Search results may be enough for a discovery stage; fetching full rendered content for every result changes both latency and credit consumption.
Apify output questions
For an Actor, inspect the input contract, dataset schema, pagination behavior, file outputs, and whether data is written to a dataset, key-value store or another integration. Actor authors choose implementation details, so two Actors that appear similar can have different fields, retry policies and resource use. Treat the Actor documentation and a sample run as part of your integration contract.
Anti-bot, proxies and self-hosting
Firecrawl says its hosted service includes a managed Fire-engine proxy and anti-bot layer. Its self-hosted open-source stack includes scrape, crawl, map and search, but not that managed layer; a self-hosted operator must supply proxies and handle blocked sites. Firecrawl also lists screenshots, page actions, Agent, Browser and Interact as hosted-only capabilities.
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Apify documentation describes proxy services and anti-scraping resources, but the available material does not guarantee access to any particular website. Actor quality, proxy type, browser fingerprints, login requirements, rate limits and site changes all affect outcomes.
Use only targets and access methods permitted by the site’s terms, robots policy and applicable law. Test with a small, representative run. A successful request to one public page is not evidence that a whole domain, logged-in area or protected endpoint will work.
Pricing and how to estimate total cost
Firecrawl’s credit model
Firecrawl states that a scrape or crawl page costs one credit. Its Search FAQ states that ten search results cost two credits; optional content extraction can add normal scrape charges. The crawl page describes additional charges for JSON mode and PDF parsing. The official pricing page has listed a 1,000-credit free tier and larger Hobby, Standard, Growth and Scale tiers, with displayed paid prices billed yearly; amounts and terms are dynamic, so verify the live page before purchasing.
A simple estimate is:
(pages scraped or crawled × one credit) + search credits + mode-specific credits + retries
For example, a discovery run that performs 20 searches returning 10 results each and then scrapes 500 pages would start at 40 search credits plus 500 scrape credits, before optional modes and retries. This is an arithmetic illustration, not a quote.
Apify’s subscription-plus-usage model
Apify combines a subscription with platform usage. Its pricing page lists Free, Starter, Scale and Business plans. Store Actors may be priced per event or per usage. Actual charges can include compute units, data transfer, storage operations, and residential or SERP proxies; retries and resource-intensive browser runs increase consumption. Because each Actor has its own pricing and implementation, run a representative test and inspect the platform usage before forecasting monthly spend.
A fair cost experiment
- Choose a fixed set of permitted URLs representing easy, JavaScript-heavy and failure-prone pages.
- Specify output fields, screenshots or PDFs, proxy requirements, concurrency, retries and retention.
- Run each product long enough to observe successful pages, failures and duplicate handling.
- Record billable requests or credits, compute, proxy, storage, transfer and result-read costs.
- Multiply by your real schedule, then add a separately stated allowance for retries and site changes.
Performance and reliability: what the evidence does—and does not—show
There is no neutral head-to-head speed result in the available evidence. Firecrawl’s published benchmark is its own vendor measurement, not a comparison with Apify. Firecrawl reports 57.6% overall Recall@10, measured August 21, 2026, on a 1,179-task developer retrieval dataset; it reports 63.1% for the Firecrawl Developer Index on that same dataset. These figures describe that vendor’s retrieval evaluation and should not be converted into an Apify ranking.
Reliability should instead be measured for your workload: success rate by domain, median and tail latency, freshness, extraction completeness, duplicate rate, retry recovery, proxy consumption and cost per accepted record.
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Managed Firecrawl
You avoid operating the service’s hosted extraction path and managed proxy layer. The trade-off is dependence on Firecrawl’s API limits, plan terms and hosted-only capabilities.
Self-hosted Firecrawl
You gain control over deployment and data flow, but must provide proxies and handle blocked sites. Hosted-only features listed by Firecrawl are not automatically available in the open-source stack.
Apify cloud
You receive a platform for running and scheduling Actors, storing results and connecting integrations. The trade-off is that Actor behavior and resource consumption vary, so platform configuration and per-Actor testing are essential.
A practical evaluation plan
- Write the acceptance contract. Define fields, freshness, allowed domains, maximum missing fields, latency and cost per accepted record.
- Build the same small corpus. Include static pages, client-rendered pages, pagination, PDFs and pages likely to rate-limit.
- Implement the simplest path first. Use Firecrawl’s scrape or crawl endpoints; in Apify, start with the closest Store Actor or a minimal Crawlee-based Actor.
- Compare normalized results. Measure valid records rather than raw pages, and count retries, blocked pages and manual fixes.
- Stress operations. Test scheduled runs, alerting, result retrieval, credential rotation and a target-site layout change.
- Choose by total ownership. Include engineering time, proxy administration, storage, transfer, observability and incident response.
Common failure modes and fixes
Pages return empty or incomplete content
Cause: content is rendered after initial HTML, a selector changed, or the site requires interaction. Fix: verify rendered-content options, wait conditions and extraction selectors; test the same URL in a browser and preserve the failing response for diagnosis.
Requests are blocked
Cause: rate limits, bot checks, IP reputation, authentication or a policy restriction. Fix: reduce concurrency, use an appropriate permitted proxy or authenticated session, and confirm the target allows automated access. Self-hosted Firecrawl users must supply and operate their own proxy layer.
Costs exceed the estimate
Cause: retries, PDF or JSON modes, browser-heavy Actors, residential proxies, storage operations or result reads. Fix: separate discovery from full extraction, cap retries, set retention, and inspect a representative run’s usage before scaling.
Schema changes break downstream code
Cause: an Actor update or extraction prompt changes fields. Fix: validate against a versioned schema, quarantine unknown fields, retain raw outputs, and pin or review Actor versions where the platform permits.
Self-hosted deployment lacks a hosted feature
Cause: Firecrawl documents screenshots, page actions, Agent, Browser and Interact as hosted-only, and its managed proxy/anti-bot layer is not included in self-hosting. Fix: redesign around available open-source operations or use the hosted service for that workflow.
When screenshots are part of the pipeline
If your data workflow needs reliable page images or PDFs in addition to extracted text, ScreenshotNeo is an alternative to try first: it removes consent banners, newsletter popups and chat widgets before capture, bills only clean shots, and has an MCP server for AI agents.
Or skip the browser setup
One GET request returns a PNG, JPEG, WebP or PDF. The API supports full-page and element capture, device and retina settings, dark mode, custom CSS and JavaScript, waits, blocking rules, headers, cookies, user agents, authorization, timezone, geolocation, resizing, caching, signed links, asynchronous webhooks and bulk capture.
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Bottom line for 2026 teams
Pick Firecrawl for a focused, API-first path from web search or pages to AI-ready content. Pick Apify when reusable Actors, Store tools and platform operations are central to the work. Run both against the same permitted corpus, calculate cost per accepted record, and make the decision from measured workload fit rather than a universal winner claim.
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Can I use Firecrawl and Apify together?
Yes. A common design is to use Firecrawl for search or standardized page extraction and Apify Actors for specialized site automation, scheduled jobs or workflows that need platform storage. Define ownership of retries, deduplication and billing before combining them.
Is Apify just a scraping API?
No. Its core abstraction is the reusable Actor, supported by cloud execution, storage, proxies, schedules, integrations, monitoring, APIs and a Store. Individual Actors can expose APIs, but the platform is broader than one extraction endpoint.
Does self-hosting Firecrawl include its anti-bot proxy service?
No. Firecrawl describes the managed Fire-engine proxy and anti-bot layer as separate from the self-hosted open-source stack. Self-hosted operators must provide proxies and handle blocked sites.
Quick Recap
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