Apify vs. Firecrawl: LLM Crawling API vs. Scraping Platform
Firecrawl and Apify are both popular tools for getting web data into AI applications, but they solve fundamentally different problems.
- Firecrawl is an LLM-first crawling API. One call converts a URL or entire sitemap into clean, token-efficient Markdown. Zero configuration. Ideal for RAG, research agents, and AI content ingestion.
- Apify is a managed scraping and automation platform. It provides 30,000+ pre-built Actors, serverless execution, built-in scheduling, proxy management, and structured data storage. Built for recurring production pipelines.
The key question is not which is better. It is which fits your workload. Many teams use both.
Example: You're building a research assistant that needs to ingest any URL a user pastes in, plus a daily feed of competitor pricing from Amazon. Firecrawl handles the first job in one API call: clean Markdown in seconds, no config. Apify handles the second: a pre-built Amazon Actor runs on a schedule, stores structured JSON, and pipes it to your dashboard. Neither tool replaces the other here.
Quick verdict
| Your need | Better fit |
|---|---|
| Clean Markdown for RAG, AI agents, LLM prompts | Firecrawl |
| Structured JSON from Amazon, LinkedIn, TikTok | Apify |
| One-shot doc or blog ingestion | Firecrawl |
| Scheduled recurring runs (daily, weekly) | Apify |
| No scraper to build, pre-built tools only | Apify (30,000+ Actors) |
| Developer building an AI agent with MCP | Either (both have MCP servers) |
| Tight budget, simple crawl at low volume | Firecrawl |
| Anti-bot bypass for hard targets | Apify |
How they're built differently
| Dimension | Firecrawl | Apify |
|---|---|---|
| Model | API-first (scrape, crawl, map, extract) | Actor platform + marketplace |
| Primary output | Markdown, structured JSON via LLM extraction | Depends on Actor: JSON, CSV, Excel, XML |
| Scheduling | External (cron, Make, n8n) | Native cron scheduling + triggers |
| Pre-built scrapers | None; API only | 30,000+ community and official Actors |
| Custom code | None | Full SDK (Python + JavaScript/Crawlee) |
| MCP server | ✓ Built-in | ✓ Via Apify MCP Actor |
| LangChain / LlamaIndex | ✓ Native integrations | ✓ Native integrations |
| Proxy management | Automatic (included) | Full control (datacenter + residential) |
| Data storage | Not included; return value only | Built-in Datasets + Key-Value Stores |
| Cloud infrastructure | Serverless (Firecrawl-managed) | Serverless (Apify-managed) |
| Open source | ✓ Self-hostable (GitHub) | ✓ Crawlee library (MIT) |
What you actually pay
Pricing verified May 2026 against firecrawl.dev/pricing and apify.com/pricing. Verify on the vendor's pricing page before committing.
| Tier | Firecrawl | Apify |
|---|---|---|
| Free | 1,000 credits/month (no card) | $5/month recurring credit (no card) |
| Entry | Hobby: $16/mo billed annually, 5,000 credits | Starter: $29/mo ($26 billed annually), $29 prepaid usage |
| Mid | Standard: $83/mo billed annually, 100,000 credits | Scale: $199/mo ($179 billed annually), $199 prepaid usage |
| Scale | Growth: $333/mo billed annually, 500,000 credits | Business: $999/mo ($899 billed annually), $999 prepaid usage |
Billing model difference:
- Firecrawl bills per credit. Scrape, crawl, and map are 1 credit per page; Search is 2 credits per 10 results; Interact is 2 credits per browser minute; structured-extraction features can add credits per page (verify on the vendor's pricing page). Monthly plan credits do not roll over; auto-recharge packs do.
- Apify bills per Compute Unit (1 CU = 1 GB RAM × 1 hour) plus proxy/storage/transfer line items. Each plan includes prepaid usage; unused prepaid usage expires at the end of the billing cycle.
Cost at different scales (estimated):
| Monthly pages | Firecrawl cost | Apify cost |
|---|---|---|
| 1,000 pages | Free tier (1,000 credits) | Free tier ($5 credit) |
| 5,000 pages | Hobby (~$16/mo annual) | ~$5–15 (within free or Starter) |
| 100,000 pages | Standard (~$83/mo annual) | ~$29–80 (depends on site complexity) |
| 500,000+ pages | Growth (~$333/mo annual) | Scale plan + optimized Actors |
For simple HTML pages under 100K/month, Firecrawl is often cheaper because credit costs are predictable. For pages with heavy JavaScript rendering, anti-bot requirements, or complex Actor logic, Apify's compute-based billing can be more efficient with optimized code.
Category-by-category breakdown
Output quality for AI applications
Firecrawl is purpose-built for AI output. Its scraper pipeline:
- Fetches the page with JavaScript rendering
- Strips navigation, ads, and boilerplate
- Returns clean Markdown, ready to paste into an LLM prompt or embed in a vector store
No prompt engineering or post-processing needed. You get token-efficient, semantically clean text.
Apify returns structured data (JSON fields, prices, IDs) rather than document text. For AI use cases like knowledge bases and RAG, the Website Content Crawler Actor provides Markdown output with configurable cleaning options, but it requires configuring the Actor vs. Firecrawl's zero-config default.
Winner for LLM content ingestion: Firecrawl
Winner for structured AI training data: Apify
For the LLM and RAG ingestion-specific take, including Markdown-for-AI workflows, see our blog deep dive: Firecrawl vs Apify for LLM ingestion.
Scheduling and automation
Firecrawl is a stateless API. It has no built-in scheduler, so you trigger runs from your own cron job, Make workflow, or n8n automation.
Apify includes native cron scheduling with timezone support, trigger-based runs (webhooks, Actor success events), and integrations with Make, n8n, and Zapier baked into the platform. Scheduled runs store results automatically in Datasets.
Winner: Apify. No external orchestration needed for recurring jobs.
Pre-built scrapers
Firecrawl has no marketplace. It is an API you call with a URL. It handles any public page but doesn't know about LinkedIn's rate limiting rules, Amazon's pagination, or TikTok's API boundaries.
Apify's Store has 30,000+ Actors maintained by Apify's team and the community. They are purpose-built for specific platforms, already handling rate limits, pagination, authentication, and structure. See our best Actor guides for curated lists.
Winner: Apify. No comparison for platform-specific extraction.
Anti-bot and proxy control
Firecrawl handles most common bot-detection patterns automatically. For hard targets (Cloudflare, PerimeterX, Akamai), results vary and aren't configurable: you can't specify proxy country or rotate at the session level.
Apify gives you full proxy control: datacenter IPs, residential IPs, SERP proxies, country selection, and session management via Crawlee. For the hardest targets you can layer Bright Data proxies on top.
Winner: Apify for targets with serious anti-bot defenses.
MCP and AI agent integration
Both tools support the Model Context Protocol (MCP), the standard for connecting AI assistants to live tools.
- Firecrawl MCP: converts
scrape,crawl,map, andsearchinto MCP tools. Claude or Cursor can crawl any URL on-demand. See the Firecrawl MCP setup guide. - Apify MCP: exposes Actors as MCP tools. Any of the 30,000+ Actors (Google Maps, LinkedIn, Amazon) becomes callable from Claude, Cursor, or any MCP client. See How to use Apify MCP servers.
For AI agents that need clean text from arbitrary URLs, Firecrawl's MCP is simpler. For agents that need structured platform data (LinkedIn profiles, Maps listings, pricing data), Apify's MCP is more powerful.
Developer experience
Firecrawl's API surface is intentionally minimal: scrape, crawl, map, extract, search. Getting started takes minutes. There is no local development, Actor concept, or SDK to learn.
Apify has more surface area: the Crawlee SDK, CLI, Web IDE, Actor concepts, and storage system. The learning curve is steeper but the ceiling is much higher. You can build any custom extractor with full debugging, git-based deployment, and CI/CD.
Winner for time-to-first-result: Firecrawl
Winner for production custom pipelines: Apify
Using Firecrawl and Apify together
The most effective setup for AI applications often uses both:
- Firecrawl handles ad-hoc page ingestion: research queries, dynamic docs, RAG context fetching
- Apify handles scheduled structured extraction: LinkedIn jobs, Amazon prices, Google Maps listings
- Both write to the same destination (vector DB, data warehouse, or flat file)
This is not a "pick one" decision for many teams. The tools operate at different layers: Firecrawl at the text-transformation layer, Apify at the data-extraction layer.
Side-by-side for common use cases
| Use case | Firecrawl | Apify | Recommendation |
|---|---|---|---|
| RAG knowledge base from docs/blogs | ✓ Best | ✓ (Website Content Crawler) | Firecrawl (zero config) |
| Scrape Amazon product data | No | ✓ Best | Apify (pre-built Actor) |
| LinkedIn company/profile data | No | ✓ Best | Apify (pre-built Actor) |
| Google Maps business listings | No | ✓ Best | Apify (pre-built Actor) |
| Sitemap to Markdown for LLM | ✓ Best | ✓ | Firecrawl (one API call) |
| Competitor price monitoring | No | ✓ | Apify (schedule + storage) |
| AI agent web browsing | ✓ | ✓ | Either (both have MCP) |
| One-off custom site, no Actor exists | ✓ | ✓ (build Actor) | Firecrawl for speed; Apify for reuse |
| 5M+ pages/month | No | ✓ | Apify (cost + control at scale) |
When to choose Apify
- You need structured data from specific platforms (Amazon, LinkedIn, TikTok, Google Maps)
- You want pre-built, maintained scrapers without building anything
- You need recurring scheduled runs with storage and monitoring
- You need proxy control for anti-bot targets
- Your team uses Python or JavaScript and wants a dev-friendly SDK
- You need 30+ native integrations (Make, n8n, Zapier, Google Sheets, LangChain, LlamaIndex)
Apify free plan gives you $5/month in credits, no credit card required. Enough to test Google Maps, LinkedIn, or any Actor before committing.
When to choose Firecrawl
- Your output goes directly into an LLM, vector DB, or RAG pipeline and you need clean Markdown
- You want zero-configuration page-to-text conversion
- You're building an AI agent that needs to browse arbitrary URLs (not platform-specific data)
- Your crawl volume is under 100K pages/month where per-credit pricing is predictable
- You want a self-hostable, open-source solution for full control over the pipeline
Try Firecrawl free: 1,000 credits/month, no card required →
For LLM-ready Markdown output and AI content ingestion, Firecrawl is simpler and faster: zero configuration, one API call. For structured data extraction from specific platforms (Amazon, LinkedIn, Google Maps), pre-built scheduling, and production pipelines, Apify is far more capable. Most AI teams end up using both for different jobs.
Firecrawl is often cheaper for simple HTML crawls under 100,000 pages/month; the credit-per-page model is predictable. Apify can be more cost-effective at large scale or for JavaScript-heavy sites because optimized Actors reduce compute time. Apify's free plan gives $5 in recurring monthly credits; Firecrawl's free tier gives 1,000 credits per month. Both offer meaningful free tiers.
No. Firecrawl is a general-purpose crawling API: you pass it any URL and it returns clean text. Apify's Store has 30,000+ pre-built Actors for specific platforms. If you need a Google Maps extractor, LinkedIn scraper, or Amazon data tool, Apify is the correct choice without any building required.
Both support MCP. Firecrawl's MCP server exposes scrape, crawl, map, and search as tools, ideal for AI agents that need to read any webpage on demand. Apify's MCP server exposes all 30,000+ Actors as tools, ideal for agents that need structured data from specific platforms. For a general-purpose browsing agent, Firecrawl is simpler. For structured-data agents, Apify's MCP is more powerful.
Yes, and many AI engineering teams do. Firecrawl handles fast, ad-hoc Markdown ingestion for RAG (research, docs, context fetching). Apify handles scheduled, structured extraction for production pipelines (prices, profiles, listings). Both can write to the same vector database or data warehouse downstream.
Yes. Firecrawl's core is open source on GitHub and can be self-hosted via Docker. Apify's scraping library, Crawlee, is also open source (MIT licensed), but the Apify platform itself is a proprietary cloud service. Both offer self-hosting paths, though with different complexity levels.
Firecrawl is the faster path for RAG: one API call crawls a sitemap, strips boilerplate, and returns token-efficient Markdown. Apify's Website Content Crawler Actor does the same job with more configuration options (CSS selectors, crawl depth, link filtering). Firecrawl is better for speed and simplicity; Apify is better when you need scheduling, storage, and fine-grained crawl control.