Firecrawl vs. Tavily

Tavily summarizes the web.
Firecrawl turns it into AI-ready data.

Search, crawl, scrape, and interact with the web to get clean data for AI agents and apps.
Full pages instead of snippets, plus 90+ data providers through Firecrawl Alexandria.

Trusted by 150,000+
companies of all sizes
Shopify
Lovable
Read story
Canva
Zapier
Read story
Apple
Replit
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Alibaba
PHMG
DoorDash
Gamma
Read story
You.com
Sprinklr
Cognism
Ada
11x
Botpress
Aleph Alpha
Sierra
Shopify
Lovable
Read story
Canva
Zapier
Read story
Apple
Replit
Read story
Alibaba
PHMG
DoorDash
Gamma
Read story
You.com
Sprinklr
Cognism
Ada
11x
Botpress
Aleph Alpha
Sierra
[ 01 / 08 ]
·
Why Firecrawl

See why teams choose Firecrawl over Tavily.

When comparing Firecrawl vs Tavily, the difference comes down to full-page extraction, one API from search through interaction, and full open-source control.

apple.com
Endpoint
Scrape
Status
Success
Started
Mar 16, 2026
2:51 PM
Formats
Markdown
JSON

Clean, reliable data for AI pipelines

Firecrawl returns clean LLM-ready markdown or structured JSON on every request, with full pages instead of snippets. Tavily is built for summary-first retrieval: ranked results and citations, with raw content available through include_raw_content or /extract.

See use cases
Scrape
Search
Crawl
Agent
Browse

Firecrawl Alexandria and first-party indexes

One search call reaches 90+ data providers through Firecrawl Alexandria: first-party sources such as SEC EDGAR, FRED, and the World Bank, plus Firecrawl's own Developer Index of 70M+ READMEs, issues, pull requests, and docs, and Research Index of 43M+ paper abstracts.

Explore Firecrawl Alexandria
firecrawl/firecrawlPublic

Turn entire websites into LLM-ready markdown or structured data.

184K
9.9K
473
TypeScript
JavaScript
HTML
Python
Other
licenseAGPL-3.0
downloads18M
contributors175

Open source and self-hostable

Run on your own infrastructure with full source code, 180K+ GitHub stars.

View on GitHub
[ 02 / 08 ]
·
Benchmarks

Firecrawl leads on quality + token efficiency.
And so much more.

Coverage
96%
success rate
Quality
0.638
F1 score for accuracy
Recall
0.639
content recall rate
Speed
3,387 ms
P95 latency

Internally conducted benchmark, run Jan 13, 2026. Tested 1,000 URLs drawn from diverse public web domains (news, documentation, e-commerce, finance, and more) and measured whether each tool retrieved at least 10% of the expected content — defined as core page text, excluding navigation, ads, and footers. Dataset publicly available at the Firecrawl scrape-content-dataset-v1.

· Figures from the run of Jan 13, 2026

Token efficiency for coding agents.

Median LLM tokens a coding agent spends per task with each search API, on 100 hard retrieval tasks. Lower is better. Firecrawl ranks first of 14 configurations search-only and third when the agent may fetch pages; Tavily ranks ninth (basic) search-only and sixth (advanced) and eighth (basic) with fetch.

Metric
Firecrawl
Tavily
Median task tokens, search only
7,456
16,299 (basic)
Task completion, search only
70.3%
51.0% (basic)
Median task tokens, search and fetch
17,379
26,269 (advanced); 27,405 (basic)
Task completion, search and fetch
76.0%
60.0% (advanced); 59.0% (basic)

Independent benchmark by OpenBenchmarks, published as most token-efficient web search API for coding agents. 100 hard retrieval tasks on documentation questions, run through each web search API with the same coding agent, model, and prompts. The metric is the median number of LLM tokens the agent spent per task, reported beside task completion. A pass requires a grounded source URL from that run. Search-only and search-and-fetch are separate boards, and Tavily's basic and advanced depths are separate entries. Runner and scoring are public at openbenchmarks-labs/web-search-for-coding-agents.

· Figures from the run of Sep 15, 2026

Search quality for AI agents.

AIMultiple scored 8 search APIs on 100 AI and LLM queries, five results each. Firecrawl ranks second of eight and Tavily fifth.

Metric
Firecrawl
Tavily
Agent Score (relevance times quality)
14.58
13.67
Mean relevant results, out of 5
4.30
4.18
Quality, out of 5
3.39
3.27

Third-party benchmark by AIMultiple. 8 search APIs, 100 AI and LLM queries drawn from the publisher's own search traffic, 5 results retrieved per query with default settings. An LLM judge scored relevance and quality; Agent Score is Mean Relevant multiplied by Quality. Confidence intervals come from 10,000 bootstrap resamples, and the top four APIs overlap, so read the order as a ranking rather than a proven gap. The study dates its data to a December 2025 snapshot.

· Figures from the run of Dec 2025

Scrape coverage and quality, side by side.

The Jan 13, 2026 run on 1,000 URLs, with Tavily's figures next to Firecrawl's on the two metrics both tools were scored on.

Metric
Firecrawl
Tavily
Extraction accuracy (F1)
0.638
0.494
Latency (P95)
3,387 ms
7,339 ms

Internally conducted benchmark, run Jan 13, 2026. Tested 1,000 URLs drawn from diverse public web domains (news, documentation, e-commerce, finance, and more) and measured whether each tool retrieved at least 10% of the expected content, defined as core page text, excluding navigation, ads, and footers. Dataset publicly available at the Firecrawl scrape-content-dataset-v1.

· Figures from the run of Jan 13, 2026

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.

Published Firecrawl benchmark results with dataset and measurement date
MetricValueDatasetMeasuredSource
Coverage (success rate)96%Scrape coverage and quality, 1,000 URLsJan 13, 2026Methodology
Extraction accuracy (F1)0.638Scrape coverage and quality, 1,000 URLsJan 13, 2026Methodology
Content recall0.639Scrape coverage and quality, 1,000 URLsJan 13, 2026Methodology
Latency (P95)3,387 msScrape coverage and quality, 1,000 URLsJan 13, 2026Methodology

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.

[ 03 / 08 ]
·
Firecrawl vs. Tavily

Firecrawl is purpose-built for AI agents and developers.

In any Firecrawl vs Tavily comparison, the difference comes down to full-page extraction by default, a unified single-key API that also reaches data providers, and open-source flexibility.

Web search
Firecrawl
Tavily
LLM-ready output by default
Firecrawl
Tavily
Site-wide crawling
Firecrawl
Tavily
Browser interaction (interact endpoint)
Click, fill forms, and navigate pages programmatically before scraping
Firecrawl
Tavily
Batch and async jobs
Batch scrape and crawl jobs with webhooks
Firecrawl
Tavily
Data providers via Firecrawl Alexandria
90+ official APIs, publishers, and indexes returned as tools in one search call
Firecrawl
Tavily
Developer Index for coding agents
70M+ READMEs, issues, pull requests, and docs, refreshed daily, via categories: developer
Firecrawl
Tavily
Prompt injection detection
Opt-in checkPromptInjection flags injected instructions in scraped content during JSON extraction
Firecrawl
Tavily
Token efficiency for coding agents
7,456 median task tokens, rank 1 of 14 on the OpenBenchmarks search-only board
Firecrawl
Tavily
Open source + self-hostable
Full control for compliance, data residency, and infra
Firecrawl
Tavily
Predictable pricing
1 credit per page and 2 credits per 10 search results, on every plan
Firecrawl
Tavily
SDKs and integrations
Firecrawl
Tavily
Keyless start with 1,000 free credits a month
Call the API with no signup and no API key required to try any endpoint
Firecrawl
Tavily
AI agent self-onboarding
Agents choose their integration path and are ready after a single authorization
Firecrawl
Tavily
[ 04 / 08 ]
·
Customer Testimonials
Zapier

“Firecrawl allows our customers to pull the web information they need directly in our product.”

Andrew Gardner
Sr. Engineer
Replit

“If your agent or LLM needs web content, Firecrawl delivers the best-formatted results.”

Zhen Li
Staff AI Engineer
Dub

“What makes Firecrawl essential is how it turns messy web data into clean, AI-ready content.”

Steven Tey
Founder & CEO
[ 05 / 08 ]
·
FAQs

Frequently asked questions

The core difference is what comes back. Tavily is a search-first API: it returns AI-ranked results, snippets, and citations, with raw page content available through include_raw_content or its /extract endpoint. Firecrawl returns clean, LLM-ready markdown or structured JSON on every request, and puts search, crawl, scrape, interact, and agent under one API key. Choose Tavily for summary-first retrieval. Choose Firecrawl when your agent needs to read full pages, crawl whole sites, extract structured data, or reach data providers through Firecrawl Alexandria.
Yes. Every Firecrawl request returns clean markdown or structured JSON with no post-processing, and search can attach the full page for each result in the same call. Tavily returns ranked snippets by default and can include raw content when asked, which suits answer generation but leaves extraction and structuring to you.
Firecrawl uses credit-based pricing at 1 credit per page and 2 credits per 10 search results, on every plan. Standard covers 100,000 credits at $99/month billed monthly, or $83/month billed annually. Tavily starts at $30 per month for 4,000 credits and then charges $0.008 per credit pay as you go, with a basic search costing 1 credit and an advanced search 2 credits. Both offer 1,000 free credits a month, and Firecrawl's free tier also works without an API key.
Yes. Firecrawl is fully open source under the AGPL-3.0 license with 180K+ GitHub stars and can be self-hosted for complete control over your data and infrastructure. Tavily is a proprietary SaaS platform with no self-hosting option.
Most developers are productive in minutes. Firecrawl has one API with clear endpoints for search, scrape, crawl, interact, and agent, SDKs in Python, Node, Rust, and Go, and a keyless free tier so the first call needs no signup. Tavily is also quick to start, though you choose a search depth and endpoint per task and need an API key from the first request.
In Firecrawl's internal scrape benchmark on 1,000 real URLs, run Jan 13, 2026, Firecrawl scored 0.638 F1 on extraction accuracy against Tavily's 0.494, with a P95 latency of 3,387 ms against 7,339 ms. AIMultiple's independent agentic search study ranks Firecrawl second of eight with an Agent Score of 14.58 and Tavily fifth at 13.67, with overlapping confidence intervals at the top of the table. The scrape dataset is public on Hugging Face as firecrawl/scrape-content-dataset-v1.
Firecrawl, on OpenBenchmarks' independent web search benchmark for coding agents. On the search-only board Firecrawl ranks first of 14 at a median of 7,456 LLM tokens per task with 70.3% task completion, while Tavily basic sits at 16,299 tokens and 51.0% completion. On the search-and-fetch board Firecrawl spends 17,379 tokens per task against 26,269 for Tavily advanced and 27,405 for Tavily basic. Fewer tokens per task means a lower model bill for every agent loop.
Yes. Firecrawl /search returns ranked results and can attach the full markdown of each page in the same request, so an agent reads sources without a second fetch. Tavily returns ranked snippets and can include raw content, but structuring that content into JSON or crawling beyond the result page needs additional tooling.
Firecrawl is the better fit when the pipeline needs depth: crawling a docs site or wiki, converting pages to clean markdown with structure preserved, and extracting typed fields with a JSON schema. Tavily is a good fit for breadth, pulling fresh ranked context from many sources for a single agent query. Teams that need both usually run Firecrawl for ingestion and can use Firecrawl /search for the fresh-context step as well.
Yes. Firecrawl Alexandria is a library of 90+ data providers that Firecrawl returns as tools inside a search call: add alexandria to the sources parameter and the response carries a tools array next to the web results, covering official APIs such as SEC EDGAR, FRED, and the World Bank, licensed publishers such as Fiscal.ai and Benzinga, connectors such as the Wayback Machine and Greenhouse job boards, and Firecrawl's Research, Developer, and Government indexes. Finding a tool is free and running one is billed at the price the tool lists. Tavily's search, extract, crawl, map, and research endpoints run over web content, so structured providers are a separate integration. With Firecrawl the same API key and search call reach both the web and those providers.
Swap the SDK (pip install firecrawl-py or npm install firecrawl), set your Firecrawl API key or start keyless, and replace Tavily search() calls with Firecrawl /search, adding scrapeOptions when you want full page markdown instead of snippets. Replace /extract calls with /scrape, which also takes a JSON schema for structured output, and use /crawl where you previously stitched together multiple searches to cover a site. Most teams finish the switch in under an hour.
Yes. Firecrawl is SOC 2 Type II compliant with GDPR compliance and a DPA available. Enterprise plans include zero data retention and a 99.9% SLA. Self-hosting under AGPL-3.0 is available for air-gapped environments, and the managed cloud has served more than 5 billion requests to date for over 150,000 companies.