Exa vs Tavily: which search API for your AI agent

Exa and Tavily are two search APIs built for AI agents. This guide compares them on accuracy, speed, price, and ownership. It tells you which one to pick for each kind of project.

Pick Exa when your agent needs to find information by meaning across the open web. Exa runs on a neural index. It handles broad research and semantic queries well. It also searches people, companies, and code.

Pick Tavily when your agent needs fast, grounded answers. Tavily joins search and content extraction in one API call. It suits question answering and RAG, where you want clean context and low token use.

Both tools are good. The rest of this guide shows the differences behind that choice.

Exa is a search engine built for AI. Its core is a neural index. The index reads the meaning of a query, not only the keywords. You ask Exa for pages that match an idea. Exa returns results ranked by how well they fit that idea. Exa also offers people search, company search, and code search.

Tavily is a search API built for agents and for RAG. It runs the search and pulls the page content in one call. This keeps the agent loop simple. You send a query. You get back clean text that the model can use right away.

On hard retrieval tasks, published comparisons give Exa the edge. One comparison reports Exa at 81 percent and Tavily at 71 percent. The same source notes that Exa runs two to three times faster. It also adds people, company, and code search that Tavily does not have.

Exa fits research work that needs breadth and depth. Tavily fits direct question answering, where tight grounding matters more than breadth. A second review reaches the same conclusion.

Speed matters inside an agent loop. In one enterprise evaluation, Exa returned results with a p95 latency of about 1.4 to 1.7 seconds. Tavily returned results with a p95 latency of about 3.8 to 4.5 seconds in the same tests.

Tavily wins on the shape of the API. Its single call returns search results and page content together. That design suits agents, because it cuts the steps in the loop. Exa is faster on raw latency. Tavily is simpler to wire into a basic agent.

Exa lists a price of about 7 dollars per 1,000 searches with page contents included. Tavily bills in credits. Each credit costs about 0.008 dollars. A basic Tavily search costs 1 credit, which is about 8 dollars per 1,000 searches. An advanced Tavily search costs 2 credits, which is about 16 dollars per 1,000 searches.

So Exa and basic Tavily land close on price. Tavily costs more when you use advanced search. Check the current pricing pages before you commit. Both vendors change their plans over time.

Ownership shapes a roadmap. In February 2026, Nebius, an AI cloud company based in Amsterdam, acquired Tavily. Nebius plans to fold Tavily into its cloud platform. Exa stays independent and works only on search for AI. This change is reported in the recent comparisons.

This point is worth a thought. An independent vendor keeps its focus on one product. A vendor inside a larger platform may shift its priorities toward that platform. Neither path is wrong. It is a factor to weigh for a long project.

Sometimes the job is not search. If you need to pull structured data from a page you already know, Firecrawl is the better fit. If you need long research across many sources, Parallel is built for that. Our comparison of all four tools covers these cases. The agentic search guide maps the wider field.

Exa and Tavily are both strong choices. Pick Exa for semantic research and breadth. Pick Tavily for fast, grounded answers in RAG. If you are choosing an AI stack and want help, Garden works with teams to pick the right tools and ship them.