companies of all sizes
Choose Firecrawl for the best LLM-ready web data. /scrape delivers clean markdown, /agent does autonomous research with no URLs required, and Parallel Agents batch hundreds of queries at once. No-code integrations like Lovable let you build workflows with simple prompts.
Choose Apify when you need pre-built scrapers for specific platforms (Instagram, TikTok, Google Maps) or want to build and monetize your own Actors. Note: Apify only offers SERP scraping, not autonomous research like Firecrawl's /agent.
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Firecrawl vs. Apify: Key Differences
| Feature | Firecrawl | Apify |
|---|---|---|
| Output format | LLM-ready markdown, JSON, HTML, screenshots (all included) | Varies by Actor (typically JSON/CSV, requires post-processing) |
| JavaScript rendering | Automatic on all requests (included) | Depends on Actor (some use Puppeteer, some don't) |
| Structured extraction | Natural language prompts + JSON Schema | Requires CSS selectors or custom Actor code |
| Autonomous research | /agent does deep research with no URLs needed | SERP scraping only, no autonomous data gathering |
| Pricing model | Flat rate: 1 credit = 1 page ($0.0008/page at scale) | Compute-based: varies by Actor, runtime, memory |
| Open source / Self-hosted | Yes (fully open source) | Partial (Crawlee is open source, platform is closed) |
Last updated: Feb 04, 2026 • See full matrix ↓
Firecrawl vs. Apify: Full comparison matrix
Here's a complete feature overview of Firecrawl vs. Apify.
| Feature | Firecrawl | Apify | What this means |
|---|---|---|---|
LLM-ready output | Clean markdown optimized for AI. Preserves links, code blocks, formatting | Raw HTML/JSON output requires post-processing for LLM use | Firecrawl pioneered markdown output for AI. Apify requires you to convert and clean data yourself. |
When it matters Critical for RAG pipelines, AI agents, and any LLM-based application. Trade-offs Firecrawl is ready for AI out of the box. Apify needs additional processing. | |||
JavaScript rendering | Automatic on all requests (included) | Depends on Actor (some use Puppeteer/Playwright, some don't) | Firecrawl always renders JS. Apify varies by Actor (some are headless, some aren't). |
When it matters Modern SPAs and dynamic content require JS rendering. Trade-offs Firecrawl is consistent. Apify requires checking each Actor's capabilities. | |||
Output formats | Markdown, JSON, HTML, screenshots, links, summary, branding (all included) | Varies by Actor (typically JSON, CSV, or custom formats) | Firecrawl has consistent output across all scrapes. Apify output depends on the Actor. |
When it matters Consistent formats simplify downstream processing. Trade-offs Firecrawl is predictable. Apify Actors may offer more specialized output formats. | |||
Structured extraction | Natural language prompts + JSON Schema | Requires CSS selectors or custom Actor code | Firecrawl: describe what you want in plain English. Apify: write selectors or use Actor-specific config. |
When it matters Important for non-developers or rapid prototyping. Trade-offs Firecrawl is more flexible. Apify Actors can be more precise for known structures. | |||
Pricing model | Flat rate: 1 credit = 1 page ($0.0008/page at scale) | Compute-based: $0.20-$0.30/compute unit + Actor rental fees + proxy costs | Firecrawl: predictable costs. Apify: costs vary by Actor, runtime, memory, and proxy usage. |
When it matters Budget predictability matters for production workloads. Trade-offs Firecrawl is simpler to budget. Apify can be cheaper for specific use cases but harder to predict. | |||
Site crawling | /crawl discovers and scrapes entire sites with one call | Website Content Crawler Actor with configuration required | Firecrawl crawls and returns LLM-ready markdown. Apify requires Actor setup and compute estimation. |
When it matters Critical for documentation ingestion, knowledge bases, and site-wide scraping. Trade-offs Firecrawl is simpler. Apify offers more granular control over crawl behavior. | |||
Autonomous research (/agent) | /agent does deep research autonomously, no URLs required | No equivalent. SERP scraping only, no autonomous data gathering | Firecrawl's /agent searches, navigates, and extracts intelligently. Apify requires URLs and manual Actor configuration. |
When it matters Critical for AI agents, lead enrichment, and research where you don't know URLs upfront. Trade-offs Firecrawl handles the full research pipeline. Apify requires manual orchestration. | |||
Entry pricing | $19/month billed monthly, or $16/month billed annually (5,000 pages guaranteed) | $29/month (prepaid usage, varies by Actor) | Firecrawl: fixed page count. Apify: prepaid credits consumed at varying rates. |
When it matters Important for startups and individual developers. Trade-offs Firecrawl is cheaper and more predictable. Apify's $29 may go further or shorter depending on usage. | |||
Open source / Self-hosted | Yes (fully open source, self-host available) | Partial (Crawlee library is open source, platform is closed) | Firecrawl can be fully self-hosted. Apify's platform requires their cloud. |
When it matters Data residency, air-gapped environments, cost optimization at scale. Trade-offs Firecrawl offers full control. Apify's Crawlee can be self-hosted but without the platform features. | |||
No-code integrations | Native Lovable integration + n8n, Zapier, Make | Zapier, Make, Airbyte (requires Actor selection and configuration) | Firecrawl's Lovable integration lets you build scraping workflows with natural language. Apify integrations still require Actor configuration. |
When it matters Critical for non-developers or teams wanting to build quickly without code. Trade-offs Firecrawl is more accessible to non-developers. Apify offers more customization for technical users. | |||
Click any row to see when it matters and trade-offs
Measured performance
Published Firecrawl results, each with the dataset it was measured on and the date it was run. Follow a row to the run it comes from.
| Metric | Value | Dataset | Measured | Source |
|---|---|---|---|---|
| Coverage (success rate) | 96% | Scrape coverage and quality, 1,000 URLs | Jan 13, 2026 | Methodology |
| Extraction accuracy (F1) | 0.638 | Scrape coverage and quality, 1,000 URLs | Jan 13, 2026 | Methodology |
| Content recall | 0.639 | Scrape coverage and quality, 1,000 URLs | Jan 13, 2026 | Methodology |
| Latency (P95) | 3,387 ms | Scrape coverage and quality, 1,000 URLs | Jan 13, 2026 | Methodology |
Scrape coverage and quality scored against the public dataset firecrawl/scrape-content-dataset-v1, so the inputs are checkable. The harness is not published yet, so the run cannot be reproduced end to end.
Every benchmark Firecrawl runs is listed on /benchmarks.
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