Firecrawl vs Zyte(2026)
Firecrawl is better for teams that need purpose-built for ai/llm workflows. Zyte is the stronger choice if built by the creators of scrapy. Firecrawl is freemium (from $16/month) and Zyte is paid (from from $0.13/1K requests).
Full feature breakdown, pricing details, and pros & cons below.
By Bikram NathLast updated
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Firecrawl
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 FirecrawlZyte
Zyte (formerly Scrapinghub) is a web scraping platform from the team behind the open-source Scrapy framework. Its Zyte API unifies proxies, headless browser rendering, and anti-ban handling with usage-based, per-successful-response pricing tiered by site difficulty.
Starting at from $0.13/1K requests
Visit ZyteHow Do Firecrawl and Zyte Compare on Features?
| Feature | Firecrawl | Zyte |
|---|---|---|
| Pricing model | freemium | paid |
| Starting price | $16/month | from $0.13/1K requests |
| 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 | ✓ | — |
| Zyte API (proxies + browser + anti-ban) | — | ✓ |
| Smart proxy management | — | ✓ |
| Headless browser rendering | — | ✓ |
| Automatic ban detection | — | ✓ |
| Scrapy Cloud hosting | — | ✓ |
| Per-successful-response billing | — | ✓ |
| Difficulty-tiered pricing | — | ✓ |
Firecrawl Pros and Cons vs Zyte
Firecrawl
Zyte
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.
Deep dive: Zyte
When to choose Zyte
Zyte is the right choice when the team already uses Scrapy or has Python-based scraping infrastructure and wants managed proxy and anti-ban services without switching frameworks. Built by the creators of Scrapy, Zyte provides the Zyte API, which wraps proxy management, browser rendering, and anti-bot bypass into a single endpoint, and Scrapy Cloud, which hosts Scrapy spiders in the cloud with scheduling, monitoring, and log viewing. The pay-per-successful-response model is a genuine differentiator: the team only pays for requests that return a 200-status response, eliminating the billing risk of failed requests consuming credits. Choose Zyte when the team has existing Scrapy spiders and wants to scale them without managing server infrastructure, or when the target sites vary in difficulty and the team wants pricing that reflects actual difficulty rather than a flat per-request rate. Avoid it when the team does not use Python, when a no-code visual builder is required, or when the team needs structured output formats like Markdown for LLM ingestion.
Real-world use case
A data team at a price comparison startup runs 50 Scrapy spiders on Zyte's Scrapy Cloud platform. Each spider scrapes a different e-commerce site for product prices and availability, running on a configurable schedule from every 2 hours to daily depending on the site's update frequency. The spiders use the Zyte API for proxy management and anti-bot handling, with automatic escalation from datacenter to residential proxies when the target site's protection level requires it. The difficulty-tiered pricing means simple sites cost /bin/zsh.13 per 1,000 requests while heavily protected sites cost up to per 1,000 requests. The team processes approximately 2 million pages per month across all spiders, with an average cost of /bin/zsh.80 per 1,000 requests, totaling about ,600 per month. The tradeoff: the per-request cost is unpredictable until the spider has run against each target site long enough to establish the difficulty tier.
Hidden gotchas
The difficulty-tiered pricing model means costs can vary 30x between easy and hard sites. A spider that scrapes simple HTML pages might cost /bin/zsh.13 per 1,000 requests, while the same spider pointed at a Cloudflare-protected site could cost per 1,000 requests. The difficulty assessment is automatic and not transparent: teams cannot predict which tier a new target site will fall into without running test requests first. Scrapy Cloud uses a proprietary deployment format that requires the shub CLI tool and does not support standard Docker containers, locking the team into Zyte's deployment pipeline. Scrapy Cloud's job monitoring dashboard shows runtime metrics but does not provide cost breakdowns per spider per run, making cost attribution across spiders and projects manual. The Zyte API's browser rendering mode is significantly more expensive than HTTP-only mode, and some target sites that appear to require JavaScript actually serve the required data in the initial HTML response, so testing with HTTP-only first can save substantial costs. Spider-level concurrency and download delays must be tuned per target site, and the default settings can trigger rate limiting or bans on targets that expect slower request patterns.
Pricing breakdown
Zyte API pricing is per successful response, starting at /bin/zsh.13 per 1,000 for easy sites (simple HTML, no protection) and scaling to per 1,000 for the hardest tier (heavy anti-bot, browser rendering, residential proxies required). Scrapy Cloud is priced per compute unit, with the free tier including 1 concurrent spider and limited storage. The Professional plan at per month includes more concurrency and longer data retention. A team running 500,000 easy-tier requests and 100,000 hard-tier requests per month would pay approximately for easy requests plus for hard requests, totaling per month on the Zyte API alone, plus the Scrapy Cloud hosting fee.
Should You Use Firecrawl or Zyte?
For most teams, Firecrawl is the better default: it offers purpose-built for ai/llm workflows and is freemium (from $16/month). Choose Zyte instead if built by the creators of scrapy matters more than credits do not roll over on monthly plans. There is no universal winner — the right pick depends on your budget, team size, and whether you value purpose-built for ai/llm workflows or built by the creators of scrapy more.
Choose Firecrawl if…
- •Purpose-built for AI/LLM workflows
- •Clean Markdown output saves prep work
- •Free tier with 1,000 credits/month
Choose Zyte if…
- •Built by the creators of Scrapy
- •Pay only for successful responses
- •Very cheap entry tier for simple sites