Your agent needs the web. So you reach for the name you've seen the most, wire it in, and it works — until the day it doesn't. You picked a clean search API and now you need the full text of one specific pricing page, and all you get back is a snippet. Or you picked a great scraper and now you need to find pages you don't have URLs for, and it has nothing to search with. You didn't buy the wrong tool. You bought the right tool for a different question.

The axis that matters isn't "which web API is best." It's discovery vs extraction. Discovery is find me the right pages. Extraction is read this page cleanly. Tavily and Exa live on the discovery side. Firecrawl lives on the extraction side. Get that split right and the choice makes itself.

Tavily: "search the web, hand my agent a clean answer"#

Tavily is a search API built specifically for LLM agents and RAG. You send a query; it returns fresh, ranked, LLM-ready results — and optionally a short synthesized answer with the sources it used — so your model doesn't have to wade through raw HTML. It's the closest thing to "Google for agents that returns something a model can eat immediately." (If your shortlist also includes Linkup, we compared the discovery-side trio in Tavily vs Exa vs Linkup.)

What comes back: a list of ranked results with clean content, plus an optional answer string. Beyond /search it also offers /extract, /crawl, and /map, but discovery-plus-light-extraction is the sweet spot.

Who it's for: builders who want grounded, cited answers with the least wiring. Start with one call to the /search endpoint (Python and JS SDKs exist). Pricing shape: a free monthly credit tier (no card required), then usage-based per credit — check current pricing before you budget.

Exa: "find the right pages, including the ones keyword search misses"#

Exa is neural, embeddings-based search — it searches "the way an LLM would." Instead of matching keywords, it encodes pages as vectors and finds ones whose meaning is closest to your query. That surfaces conceptually-adjacent pages a keyword engine would never rank, which is exactly the discovery problem hard queries have.

What comes back: semantically ranked pages, with optional contents and highlights. The endpoints map to distinct jobs — /search to find, /contents to pull page text, /answer for a cited answer, and find-similar-by-URL (/findSimilar) to expand from a page you already like into its semantic neighbors.

Who it's for: builders where discovery is the hard part — research agents, competitive/landscape mapping, "find me more like this." Start with the /search endpoint (SDKs available). Pricing shape: a free monthly search allowance, then per-request usage — confirm on the pricing page.

Discovery finds the page; extraction reads it. Using a search API to do deep extraction — or a scraper to do discovery — is how good stacks quietly fail.

Firecrawl: "I have the URL — turn this site into clean data my LLM can eat"#

Firecrawl is not a search engine. It's an extraction engine: give it a URL (or a whole domain) and it returns clean, LLM-ready markdown or structured JSON, stripping the nav, ads, and boilerplate. This is the job the search APIs are bad at — pulling the complete, reliable content of pages you've already identified.

What comes back: markdown or schema-shaped JSON. /scrape handles one page, /crawl walks a whole site, /map lists a site's URLs, and /extract pulls structured fields against a schema. It's also open-source (AGPL core at github.com/firecrawl/firecrawl) with a hosted API, so you can self-host the pipeline or let them run it. If it's the extraction side you're weighing, we put it head-to-head with the alternatives in Firecrawl vs Crawl4AI vs Jina Reader.

Who it's for: builders doing deep extraction — ingesting docs, monitoring competitor pages, feeding a knowledge base from known sources. Start with the /scrape endpoint (Python/JS SDKs, plus LangChain/LlamaIndex integrations). Pricing shape: a free credit tier, then plan- and usage-based — see current pricing.

The stack most people actually want#

Here's the thing the three-way "versus" hides: the strongest setups use two of them together. Discovery and extraction are different jobs, so you use a tool for each.

The pattern: discover with Tavily or Exa, extract with Firecrawl. Your agent searches (Exa when semantic discovery is hard, Tavily when you want a fast grounded answer), gets back the URLs that matter, then hands those URLs to Firecrawl to pull complete, structured content into the model. Search APIs give you snippets and ranking; Firecrawl gives you the whole page, cleanly. Chaining them means each tool does what it's built for.

When is one enough? If your job is purely discovery — you just need a ranked answer with sources and never the full page — Tavily or Exa alone is plenty. If your job is purely extraction — you already have the URLs and never need to find new ones — Firecrawl alone is plenty. You only need the pair when your agent has to both find pages and read them deeply.

The decision rule#

And when your agent has to do both — find and deeply read — stop trying to make one API do the other's job. Pair a discovery API with Firecrawl and let each do what it was built for.