Tavily when the input is a question: live search returning context, $8–16/1k payg, the category's widest framework support. Firecrawl when the input is URLs: scrape, crawl and extract whole sites into markdown, open-source core, free 1,000 credits/month. Using Firecrawl's search endpoint as your agent's main search is paying scraper metering for a search job — and using Tavily's crawl for heavy ingestion is the same mistake mirrored. For pure search economics, the benchmarked outsider is SERPdive at $5/1k.
The job decides the tool
“What does the web say about X?” is a search job: find the right live pages and return what they say, now. Tavily's one-call model fits: basic (1 credit) or advanced (2 credits) search, snippets or raw content, framework modules everywhere.
“Turn these 300 pages into data” is a scraping job: rendering, retries, structure, volume economics. Firecrawl's five surfaces (scrape, crawl, map, search, extract) are built around it, the core is source-available and self-hostable, and its credit metering (1 credit per page scraped) prices exactly that work.
Head to head
| Tavily | Firecrawl | |
|---|---|---|
| Core job | Search → context for agents | URLs → clean markdown/data |
| Input | A question | A URL or domain |
| Search pricing (payg) | $8/1k basic · $16/1k advanced | 2 cr/10 results + 1 cr/page read |
| Whole-site crawling | Light (crawl/map endpoints) | Core competence |
| JS rendering / screenshots | No | Yes |
| Self-hostable | No | Core is |
| Free tier | 1,000 credits/mo | 1,000 credits/mo |
| Paid from | $30/mo (4k credits) | $16/mo (Hobby, yearly billing) |
| Ecosystem | LangChain/LlamaIndex modules, n8n | Popular MCP, SDKs, open source community |
- Your agent's inputs are questions and you want one call returning context.
- You lean on existing framework integrations rather than custom plumbing.
- You ingest known sites into a RAG store or dataset — its actual specialty.
- You need rendering, structured extraction or self-hosting.
- Your “search” calls are really “find URLs, then scrape them anyway”.
If you land on the search side
Then compare search APIs on the only axis that resists marketing: what the model receives, per token, per dollar. SERPdive publishes that comparison as a replayable benchmark — 60.7% of decided blind duels won against Tavily's default search on 1,000 public questions, 20.2% fewer tokens, $5 per 1,000 credits with extraction included — and pairs with Firecrawl cleanly when you also have an ingestion pipeline.
1,000 free credits monthly, no card — run your real queries through and read what your model would have received.
Get your free API keyFrequently asked questions
Are Tavily and Firecrawl competitors?
Partially. They overlap on “get web content into an LLM”, but their cores differ: Tavily starts from a question (search, then context), Firecrawl starts from URLs (scrape, crawl, extract — with a search endpoint added later). Each has bolted on a bit of the other's job, and each does its own job much better.
Which is cheaper for web search, Tavily or Firecrawl?
For search specifically (July 2026): Firecrawl's endpoint is 2 credits per 10 results, but reading the result pages bills 1 credit per page on top — search-plus-5-pages ≈ 7 credits. Tavily includes context in the price: $8/1k basic, $16/1k advanced pay-as-you-go. For search-with-content, Tavily is usually cheaper than Firecrawl-plus-scrapes; SERPdive undercuts both at $5/1k with extraction included.
Can Tavily scrape a whole website like Firecrawl?
Tavily has extract, crawl and map endpoints, so light jobs work. For serious ingestion — thousands of pages, JS rendering, anti-bot pressure, structured extraction, self-hosting — Firecrawl is the purpose-built tool and the honest recommendation.
What should an AI agent use for day-to-day web questions?
A search-native API — the agent asks questions, not URLs. Tavily is the incumbent; SERPdive is the same shape with a public blind benchmark behind it (60.7% of decided duels won vs Tavily default search, 20.2% fewer tokens, $5/1k). Keep a scraper like Firecrawl alongside for ingestion pipelines; the two layers compose well.