Apify vs Firecrawl(2026)
Apify is better for teams that need huge library of pre-built scrapers (no code needed). Firecrawl is the stronger choice if purpose-built for ai/llm workflows. Apify is freemium (from $29/month) and Firecrawl is freemium (from $16/month).
Full feature breakdown, pricing details, and pros & cons below.
By Bikram NathLast updated
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Apify
Apify is a cloud platform for web scraping, data extraction, and browser automation used by 130,000+ developers. Its Actor marketplace offers thousands of pre-built scrapers, while the SDK and Crawlee library let you build custom crawlers — with proxies, headless browsers, scheduling, storage, and an API all handled for you.
Starting at $29/month
Visit ApifyFirecrawl
Firecrawl is a scraping and crawling API built for AI workflows — it crawls a site and returns clean Markdown or structured JSON ready to feed into LLMs and RAG pipelines. It handles JavaScript, proxies, and rate limits, and exposes scrape, crawl, map, and extract endpoints.
Starting at $16/month
Visit FirecrawlHow Do Apify and Firecrawl Compare on Features?
| Feature | Apify | Firecrawl |
|---|---|---|
| Pricing model | freemium | freemium |
| Starting price | $29/month | $16/month |
| Actor marketplace (thousands of ready scrapers) | ✓ | — |
| Crawlee open-source crawling library | ✓ | — |
| Datacenter + residential proxies | ✓ | — |
| Headless browser pool (Playwright/Puppeteer) | ✓ | — |
| Scheduling and webhooks | ✓ | — |
| Dataset + key-value storage | ✓ | — |
| REST API and SDK | ✓ | — |
| Anti-blocking and fingerprinting | ✓ | — |
| Scrape, crawl, map, and extract endpoints | — | ✓ |
| Clean Markdown + structured JSON output | — | ✓ |
| LLM-ready output for RAG pipelines | — | ✓ |
| JavaScript rendering | — | ✓ |
| Automatic proxy + anti-bot handling | — | ✓ |
| SDKs for Python and Node | — | ✓ |
| LangChain / LlamaIndex integrations | — | ✓ |
Apify Pros and Cons vs Firecrawl
Apify
Firecrawl
Deep dive: Apify
When to choose Apify
Apify is the right choice when the team needs to run web scrapers at scale without managing browser infrastructure, proxy pools, or scheduling logic. Its Actor model, where each scraper is a containerized unit that can be published, versioned, and shared, is a genuine differentiator. The Apify Store contains thousands of pre-built Actors for common targets like Google Search, Amazon product pages, Instagram profiles, and real estate listings, meaning many scraping tasks can be solved without writing a single line of code. For custom scraping, Apify's Crawlee open-source library, which powers most Actors, provides a production-grade crawling framework with automatic retry, request queuing, and browser fingerprint rotation. Choose Apify when the team needs to scrape multiple different sites, when the scrapers need to run on a schedule with monitoring, or when the output feeds into a data pipeline or AI application. Avoid it when the scraping target is a single, simple API that can be called directly, or when the team has existing scraping infrastructure on dedicated servers and the migration cost outweighs the managed benefit.
Real-world use case
A solo developer building a price comparison SaaS deploys 8 Apify Actors to scrape product prices from e-commerce sites every 6 hours. Each Actor is configured via input JSON with the target URLs, CSS selectors, and output format. The scraped datasets are automatically pushed to Apify's Dataset storage and pulled into a PostgreSQL database via a webhook that triggers a Next.js API route. The developer uses 3 pre-built Store Actors for Amazon, Flipkart, and Google Shopping, and writes 5 custom Actors using Crawlee with Playwright for sites that require JavaScript rendering. The free tier's monthly compute credit covers about 50,000 page loads with datacenter proxies. As the product grows to tracking 10,000 products, the monthly cost scales to roughly per month on the Personal plan. The tradeoff: each Actor's compute consumption varies based on the target site's complexity, proxy type, and rendering requirements, making cost prediction difficult until the Actor has run for several billing cycles.
Hidden gotchas
Compute unit pricing is the main source of billing surprises. One compute unit equals one second of Actor runtime with 1 GB of memory. Actors that use Playwright or Puppeteer for full browser rendering consume significantly more compute units per page than those using plain HTTP requests with Cheerio. A Playwright-based Actor can consume 10 to 50 times more compute units per page than an HTTP-based one, and many Store Actors default to browser rendering even when the target page does not require JavaScript execution. Switching an Actor from Playwright to CheerioCrawler can reduce costs by 90 percent but requires understanding the target page's rendering requirements. Residential proxy bandwidth is billed separately from compute units and is not included in any plan's base pricing. A scraping job that routes through residential proxies for anti-bot bypass can generate a proxy bill that exceeds the compute bill. Store Actors maintained by third parties may add per-result fees on top of the platform's compute charges, and these fees are disclosed in the Actor's pricing tab but are easy to miss during initial evaluation. Dataset storage has a retention period tied to the plan: data older than 7 days on the free plan is automatically deleted, which can silently break pipelines that process historical data.
Pricing breakdown
The free plan includes worth of compute units per month, which covers roughly 50,000 simple HTTP-based page loads or 5,000 Playwright-based page loads. The Starter plan at per month includes of compute units. The Scale plan at per month includes of compute units plus priority support. Compute unit cost varies: datacenter proxy requests consume the base rate, while residential proxy requests add approximately .50 per GB of bandwidth on top. A realistic workload of scraping 50,000 pages per month with Playwright and datacenter proxies consumes roughly to in compute units. Adding residential proxies for 20 percent of requests adds approximately per month for 2 GB of bandwidth.
Deep dive: Firecrawl
When to choose Firecrawl
Firecrawl is the right choice when the primary use case is feeding web content into AI or LLM pipelines. Its core differentiator is outputting clean Markdown and structured JSON from any web page, eliminating the HTML-to-text preprocessing step that every RAG pipeline and fine-tuning dataset otherwise requires. The API supports four modes: scrape (single page), crawl (follow links from a starting URL), map (discover all URLs on a domain), and extract (structured data extraction with schema). The free tier includes 1,000 credits per month, making it accessible for prototyping. Choose Firecrawl when building RAG applications, training data pipelines, or any workflow that needs clean text from web pages. It is also strong for documentation crawling, where the output feeds directly into a vector database. Avoid it when the scraping target requires complex interaction like form filling, pagination with infinite scroll, or login-protected content, where a full browser automation tool like Playwright or Apify is more appropriate.
Real-world use case
A developer building a customer support chatbot uses Firecrawl to ingest an entire product documentation site into a Pinecone vector database. The crawl endpoint is pointed at the docs root URL with a maximum depth of 3, and Firecrawl returns each page as clean Markdown with metadata including the page title, description, and canonical URL. The developer chunks the Markdown output, generates embeddings via OpenAI, and upserts them into Pinecone. The entire ingestion pipeline is 60 lines of Python. The documentation site has 400 pages, consuming 400 credits on the free tier. The developer schedules a weekly re-crawl to catch documentation updates. The tradeoff: Firecrawl's Markdown output is clean but not always structurally perfect. Code blocks, nested lists, and complex tables can lose formatting nuance. The developer adds a post-processing step to fix code block language tags, adding about 10 lines of Python.
Hidden gotchas
Credits do not roll over between billing periods on monthly plans. If the team does not use all 1,000 free credits in a given month, they are lost. The crawl endpoint follows links by default, and without a URL filter or maximum page limit, a crawl of a large site can consume thousands of credits unexpectedly. The extract mode, which uses an LLM to extract structured data based on a schema, costs more credits per page than simple scraping and the LLM extraction quality varies based on page complexity. The Markdown output uses an internal HTML-to-Markdown converter that handles most common patterns well but can produce unexpected output for pages with heavy use of CSS-based content rendering, iframes, or shadow DOM components. The JavaScript rendering is enabled by default on all requests, which provides better content extraction but consumes more credits per page than a hypothetical no-JS mode. Rate limiting on the free tier caps concurrent requests, and the API does not return a retry-after header on 429 responses.
Pricing breakdown
The free plan includes 1,000 credits per month. The Hobby plan at per month includes 3,000 credits. The Standard plan at per month includes 100,000 credits. The Growth plan at per month includes 500,000 credits. A scrape request costs 1 credit. A crawl request costs 1 credit per page crawled. The extract endpoint costs 5 credits per page. A team crawling 5 documentation sites with an average of 200 pages each weekly would consume 4,000 credits per month, fitting on the Hobby plan. Adding extract mode to 500 pages per month increases consumption by 2,500 credits, pushing the total to 6,500 credits and requiring the Standard plan.
Should You Use Apify or Firecrawl?
For most teams, Apify is the better default: it offers huge library of pre-built scrapers (no code needed) and is freemium (from $29/month). Choose Firecrawl instead if purpose-built for ai/llm workflows matters more than compute-unit pricing can get hard to predict at scale. There is no universal winner — the right pick depends on your budget, team size, and whether you value huge library of pre-built scrapers (no code needed) or purpose-built for ai/llm workflows more.
Choose Apify if…
- •Huge library of pre-built scrapers (no code needed)
- •Generous free tier with $5 monthly credits
- •Build custom scrapers or use ready ones
Choose Firecrawl if…
- •Purpose-built for AI/LLM workflows
- •Clean Markdown output saves prep work
- •Free tier with 1,000 credits/month