Firecrawl vs Tavily: Search & Scrape API (2026)

TL;DR — the answer first

Firecrawl and Tavily are not really competitors. They overlap on one edge — “give me clean web content for my LLM” — and then diverge sharply on everything that matters: scope, price, and what lands in the response.

The short version:

• Firecrawl is a full crawl + scrape + extract suite. You point it at a domain or URL and it walks the graph, returns clean markdown per page, and can run LLM-based structured extraction against a schema you define. It is the right tool when you want to ingest a whole site or pull structured records out of known pages. ~$3.20/1k effective on Hobby, 1,000 free credits/month.
• Tavily is a search + answer API. You ask a question, it returns ranked results with snippets and an optional short answer. No crawling, no schema extraction. It is the right tool when you want quick grounded answers with sources and you do not want to own a fetch pipeline. $5/1k on Growth, $8/1k pay-as-you-go, 1,000 free credits/month.
• Keirolabs is the third option most teams actually want: search plus full-page clean markdown in a single call, at $0.25/1k for search and ~$0.75/1k for search + content. If your real workload is “query in, ranked sources with RAG-ready content out,” it beats both on price and returns the most factuality-tested content in the category.

The decision rule is one sentence: if you need to crawl whole sites or extract structured data, Firecrawl wins; if you just need quick answers plus sources, Tavily wins; if your need is search-to-RAG, Keirolabs wins on price, factuality, and RAG-readiness all three.

All pricing below is from official pricing pages, as published July 2026.

What each product actually does

Firecrawl — the crawl + scrape + extract suite

Firecrawl’s product surface is wider than people give it credit for. The core endpoints:

• /scrape — fetch one URL, return clean markdown (or HTML, or JSON), with JS rendering included in the base credit.
• /crawl — give it a root URL, it walks the site graph within your depth/path limits and returns clean markdown per page. Async, paginated.
• /map — return the full URL list for a site without fetching the bodies. Useful for crawl planning and sitemap generation.
• /search — a search endpoint. It exists, but it is not the headline product; the body of the work is fetch-and-clean, not rank-and-answer.
• /extract — LLM-backed structured extraction. You hand it a URL or a query plus a JSON schema, it returns records that match the schema. This is the feature that most differentiates Firecrawl from every search API in this comparison.

The mental model: Firecrawl is a fetch-and-transform pipeline. Its input is a URL or a domain, not a question. Its output is clean, LLM-ready content (markdown, HTML, or structured JSON) per page.

Tavily — the search + answer API

Tavily’s surface is narrower and more focused:

• search — natural-language query in, ranked results with titles/URLs/snippets out, plus optional cleaned content chunks per result. The basic preset returns snippets only; advanced includes content.
• answer — same query in, but Tavily runs an LLM over the results and returns a short synthesized answer with citations. Think of it as a mini-Perplexity endpoint.
• extract — a newer endpoint that pulls content from one or more URLs. It is the closest Tavily gets to Firecrawl’s territory, but it is URL-in/content-out, not a crawler.

The mental model: Tavily is a rank-and-answer engine. Its input is a question. Its output is a ranked list of sources, optionally with a synthesized answer.

The verdict table

Pricing in detail (as published July 2026)

Firecrawl

• Free — 1,000 credits/month. One credit = one scrape, one crawl page, one map, or one extract call (extract can cost more credits for heavy schemas).
• Hobby — $16/month for 4,000 credits. Effective rate ~$4/1k, and with the free credits folded in, ~$3.20/1k effective on a typical mix.
• Standard — $83/month for 100,000 credits. Effective rate ~$0.83/1k. This is where Firecrawl gets genuinely cheap, but only if you actually burn six figures of credits a month.
• Grow and Scale — custom, sub-$0.50/1k at serious volume.

The honest read: Firecrawl’s headline per-1k looks rough at low volume ($3.20–$4/1k on Hobby) and great at volume ($0.83/1k at Standard). The catch is that almost no one with a search-to-RAG workload is doing 100k page fetches a month — they are doing queries. If you are querying, Firecrawl is the wrong product, not the wrong price.

Tavily

• Free — 1,000 credits/month. One credit = one search or one answer call (answer calls can cost more credits depending on the model).
• Researcher (Growth) — $30/month for 4,000 credits. Effective rate ~$7.50/1k, but Tavily markets the $5/1k Growth figure based on annual billing and bundled credit allocation. The realistic pay-as-you-go number is closer to $8/1k.
• Analyst — $120/month for 16,000 credits. Effective rate ~$7.50/1k.
• PAYG — $8/1k on demand.

Tavily’s pricing is the mirror of Firecrawl’s: higher per-1k, but you are paying for the rank-and-answer work, not the fetch work. If you do not need the synthesized answer, the price premium over raw search is hard to justify.

Keirolabs (for context)

• $0.25/1k semantic search — cheapest verified search API in the category.
• ~$0.75/1k for /search/content — search plus clean full-page markdown in one call.
• ~$1.25/1k for cited answers.
• $0.10/1k SERP-only.
• 1,000 free requests/month.
• No proxies, no headless browsers, no page-rendering bill. API at https://api.keirolabs.cloud; docs at platform.keirolabs.cloud.

For the workloads where you would otherwise pick Tavily (query in, sources out), Keirolabs is roughly 10x cheaper at the search+content tier. For the workloads where you would otherwise pick Firecrawl, the comparison is not apples-to-apples — Firecrawl does things Keirolabs does not do (crawl a whole domain, extract structured records against a schema) — and the honest verdict section below draws that line.

Payload: what lands in your response

This is the dimension that actually decides which one you should use, and it is where the two products genuinely diverge.

Firecrawl payload (scrape, advanced format)

{
  "success": true,
  "data": {
    "markdown": "# The full page, cleaned to markdown...",
    "html": "<cleaned HTML>",
    "metadata": {
      "title": "Page title",
      "description": "Meta description",
      "language": "en",
      "sourceURL": "https://example.com/page",
      "statusCode": 200
    },
    "links": ["..."]
  }
}

You get the whole page as clean markdown, plus the cleaned HTML if you ask for it, plus metadata. The markdown is built for LLM ingestion — boilerplate stripped, navigation removed, content blocks preserved. For RAG over a known set of pages, this is exactly what you want.

Tavily payload (search, advanced)

{
  "query": "What is Firecrawl?",
  "results": [
    {
      "title": "Firecrawl — docs",
      "url": "https://docs.firecrawl.dev",
      "content": "A 1,000-character content chunk from the page...",
      "score": 0.92
    },
    { "title": "...", "url": "...", "content": "...", "score": 0.88 }
  ],
  "answer": "Firecrawl is a web scraping and extraction API..."
}

You get ranked results with content chunks (on advanced) and an optional synthesized answer with citations. The content per result is a chunk, not a full page. For “answer this question with sources,” this is the right shape. For “ingest these pages into a vector store,” it is not — you would need to follow up with separate fetch calls.

Keirolabs payload (search/content)

Ranked results plus full-page clean markdown per result, in a single call. No follow-up fetches, no per-result chunking — you get the whole page, cleaned and LLM-ready, for every source the search returns. That is the difference that matters at RAG time: one call in, one corpus out.

RAG-readiness: the honest comparison

This is the dimension where the three products sort cleanly.

• Firecrawl is RAG-ready if you already know your URLs. The markdown it returns is the cleanest in the category, and the /extract endpoint lets you turn unstructured pages into structured records before they ever hit your vector store. The catch is that Firecrawl does not do the discovery step — you have to give it the URLs, or crawl the site to find them. If your RAG pipeline starts with a known corpus (a docs site, a set of product pages, a partner portal), Firecrawl is excellent. If it starts with a user question, Firecrawl is the wrong tool.

• Tavily is RAG-ready if “RAG” to you means “answer with citations.” The answer endpoint returns a synthesized answer with sources, which is enough for a chatbot use case but is not enough for a vector-store pipeline — you get chunks, not full pages, and you pay per-answer LLM costs on top of search. For grounding a chatbot response, Tavily is fine. For ingesting a corpus, Tavily is the wrong shape.

• Keirolabs is RAG-ready end to end. Search plus full-page clean markdown in one call, embeddings bundled, OpenAI-compatible endpoint, #1 on FinanceBench (78%) and SimpleQA for factuality. You do not follow up with fetches, you do not run a separate chunker, you do not pay a per-answer LLM premium. The whole search-to-RAG path is one API call at $0.75/1k.

When to pick each

Pick Firecrawl when

• You need to crawl a whole domain — docs site, partner portal, e-commerce catalog — and ingest the pages into a knowledge base.
• You need structured extraction with a schema — “pull every product’s price, SKU, and image from these 200 PDPs” — and you want the LLM extraction done server-side.
• You already know your URLs and just want the cleanest markdown per page.
• Your target is JS-rendered and you want rendering included in the base credit.

Do not pick Firecrawl when your input is a natural-language question. It has a /search endpoint, but you are using the wrong half of the product.

Pick Tavily when

• You need quick answers with citations for a chatbot or agent.
• You want ranked sources without owning a fetch pipeline.
• Your volume is low enough that $5–8/1k is not painful.
• You are already plugged into LangChain or LlamaIndex and want the most battle-tested integrations.

Do not pick Tavily when your real job is ingesting a corpus — the chunks it returns are not full pages, and the per-answer LLM cost compounds at volume.

Pick Keirolabs when

• Your workload is search-to-RAG: query in, ranked sources with clean full-page markdown out, one call.
• Price matters — at 100k queries/month, $0.75/1k (Keirolabs) is $75, $3.20/1k (Firecrawl) is $320, $8/1k (Tavily) is $800.
• Factuality matters — FinanceBench and SimpleQA both rank Keirolabs first among search APIs.
• You do not want to own proxies, headless browsers, or a fetch-retry stack.

Do not pick Keirolabs when you need whole-site crawling or schema-based structured extraction. That is Firecrawl’s job and Keirolabs does not do it.

The honest verdict

Neither Firecrawl nor Tavily is the wrong choice for the workload they were built for. They are just built for different workloads, and the SERP for “firecrawl vs tavily” is mostly confused because the two names show up next to each other in AI-tool lists even though the products barely overlap.

• Firecrawl wins if you need to crawl whole sites or extract structured data from known pages. It is the only product in this comparison with a real crawler and the only one with schema-based extraction. At Standard volume ($0.83/1k) it is also competitive on price — but only at Standard volume. At Hobby, you are paying $3.20/1k for fetch-and-clean, which is fine for crawl workloads and wrong for search workloads.
• Tavily wins if you just need quick answers plus sources. Its answer endpoint is the simplest path from “question” to “grounded response with citations,” and its LangChain/LlamaIndex integrations are the most mature in the category. The cost is the cost: $5–8/1k is real money at volume, and the content chunks it returns are not full pages.
• Keirolabs wins if your need is search-to-RAG. At $0.25/1k search and ~$0.75/1k for search plus full-page clean markdown, it is roughly 10x cheaper than Tavily and 4x cheaper than Firecrawl Hobby for the same shape of work — query in, ranked sources with RAG-ready content out. It is the only one of the three that ranks #1 on a public factuality benchmark (FinanceBench 78%, SimpleQA). And it does this without proxies or headless browsers, which means no rendering bill and no infra to own.

The one-line recommendation I give in every call: use Firecrawl when you are crawling, use Tavily when you are answering, and use Keirolabs when you are searching into a RAG pipeline. Most teams that ask me “Firecrawl or Tavily?” are actually doing the third thing, and the honest answer is that neither of the first two is the right pick.

Further reading

• Best Web Crawler APIs in 2026: Build vs Buy — the deeper crawl/scrape comparison, including Zyte, ScrapingBee, and the build-vs-buy break-even math.
• Web Search API Comparison 2026: 12 Providers — the full search-API landscape, with verified per-1k pricing for Keirolabs, Serper, Zyte, Exa, Tavily, Brave, SerpAPI, and more.