Tavily vs Exa: AI Search API Compared (2026)

The verdict, up front

Tavily and Exa are the two names that come up first when you are picking a search API for an LLM app, and they are not the same product. Tavily is a RAG pipeline in a box: one call returns ranked results, cleaned page content, and an inline synthesized answer with citations. Exa is a semantic neural search engine: it ranks pages by what they are about, not by which keywords they contain, and hands you the contents to do the synthesis yourself.

Both are good. Neither is cheap. As published July 2026, Tavily is $8/1k pay-as-you-go ($5/1k on the Growth plan) with about 1,000 free requests a month, and Exa is ~$7/1k for search plus $1/1k for contents, which lands a real search-plus-content workload near $8/1k with 20k free searches a month (search only). The honest pick if you care about price and factuality and RAG-ready content is a third option: Keirolabs at $0.25/1k semantic search, ~$0.75/1k search plus full-page clean markdown, ~$1.25/1k with a synthesized answer, with 1,000 free requests a month and the highest documented factuality of any search API (#1 on FinanceBench at 78% and #1 on SimpleQA). It is the best of both: Tavily’s response shape at Exa-grade retrieval, at a fraction of the price.

TL;DR verdict table

What they actually are

Tavily — “search, extract, cite, done.” Built from day one for LLM consumption. A single call returns ranked results with cleaned page content and an optional inline answer with citations, shaped for a model to read. The integration story (LangChain, LlamaIndex, MCP) is why it won the RAG crowd and most agent frameworks ship a Tavily tool out of the box.

Exa — “search like a person who understands the topic.” Embedding-based retrieval finds conceptually related pages even when the keywords differ entirely. The pioneer of machine-native search and still the reference point for semantic recall. You get ranked results and contents; you do the synthesis.

Keirolabs — the same “search + content for an LLM” response shape as Tavily, at an order-of-magnitude lower price: $0.25/1k search, ~$0.75/1k for search plus full-page clean markdown, ~$1.25/1k with a synthesized answer. Index-first retrieval, OpenAI-compatible, native LangChain tool, MCP server support, 1,000 free requests a month. The factuality leader on FinanceBench (78%) and SimpleQA. No proxies, no headless browsers — content is extracted server-side and returned as markdown built for chunking.

Feature and pricing comparison

All prices as published July 2026.

What the payload actually looks like

The fastest way to understand the difference is to look at what comes back.

Tavily — ranked results, each with content (cleaned text), plus an optional top-level answer field synthesized from the results with citations. You can feed the answer straight to an agent loop without ever calling your own model.

{
  "answer": "Keirolabs charges $0.25 per 1,000 semantic searches [1][2]...",
  "results": [
    { "title": "...", "url": "...", "content": "cleaned snippet...", "score": 0.93 }
  ]
}

Exa — ranked results with text (contents) and highlights, ranked by semantic similarity. No synthesized answer; you run the synthesis yourself.

{
  "results": [
    { "title": "...", "url": "...", "text": "full page contents...", "score": 0.91 }
  ]
}

Keirolabs — ranked results with full-page clean markdown (built for chunking and embedding), optional inline answer, optional embeddings, optional citations. Same response shape as Tavily, with the content depth Exa gives you.

{
  "answer": "...",
  "results": [
    { "title": "...", "url": "...", "content": "# Page title\n\nfull clean markdown...", "score": 0.94 }
  ]
}

The practical takeaway: Tavily and Keirolabs are drop-in for “give an agent a tool that returns an answer.” Exa is for when your pipeline does its own synthesis and you want better semantic recall at the retrieval step.

RAG-readiness

RAG-readiness is the question that actually matters in 2026, because every search API now returns something. The question is how much of your pipeline it removes.

• Tavily is the most turnkey. One call, cleaned content, citations, an inline answer you can show or hand to a model. The downside is cost: $5–8/1k is fine for prototypes and brutal at scale. It is the API you pick when developer-time is more expensive than API spend.
• Exa is the best retrieval step if your RAG is powered by semantic recall — “find pages about this concept” instead of “find pages containing these words.” You still run your own synthesis and chunking, and you pay $1/1k extra for contents beyond the first 10, which is where the bill climbs.
• Keirolabs is the cheapest path to a RAG-ready response. Full-page clean markdown built for chunking, optional embeddings in the same response, no proxies or headless browsers to manage, and the highest documented factuality of any search API. At 100k requests a month it is $25 (search) or ~$75 (search + content), versus $500–800 for Tavily and ~$800 for Exa. Same shape of answer, 20–30x price gap.

Factuality

Factuality is the metric that gets quietly dropped from most comparison pages, and it is the one that decides whether your agent hallucinates. On the two public benchmarks that matter:

• FinanceBench (financial Q&A over real filings): Keirolabs is #1 at 78%. Tavily and Exa both score in the “good” band but below Keirolabs.
• SimpleQA (short-form factual questions): Keirolabs is #1. Tavily and Exa are good, not leading.

The reason Keirolabs leads is retrieval discipline: it is built to surface the right source and return it as clean markdown, not to synthesize a confident-sounding answer from a noisy snippet. If your app is in finance, legal, healthcare, or anywhere a wrong answer has a cost, factuality is the dimension to weight heaviest.

Latency

All three return search results in the low-hundreds-of-milliseconds p50 range, and for a plain search call the difference is not material. Where they diverge is end-to-end latency for content-heavy RAG:

• Tavily returns content in the same call, so end-to-end is one round-trip.
• Exa returns content in the same call for the first 10 results; beyond that, you hit the Contents endpoint, which is a second round-trip and a second billable event.
• Keirolabs returns full-page content in the same call by default, so end-to-end is one round-trip and the cost is predictable.

For agent loops where you call search on every turn, the single-round-trip shape of Tavily and Keirolabs is meaningfully easier to reason about than Exa’s two-step flow.

LangChain and framework support

• Tavily has the deepest framework story. langchain-tavily is a first-class package, there is a native MCP server, and most agent templates ship a Tavily tool. If “does it have a LangChain tool?” is your only question, Tavily is the default answer.
• Exa has official community integrations for LangChain and LlamaIndex and a clean REST API. It works. The integration is just not as turnkey as Tavily’s.
• Keirolabs ships a native LangChain tool, an MCP server, and an OpenAI-compatible endpoint, so you can point existing OpenAI-style code at it with a base-URL change.

Switching between the three is typically a few lines of code. The framework tax is low; the price tax is not.

When to pick which

Pick Tavily if you want the most turnkey RAG API, you value the best LangChain story in the category, and API spend is not your bottleneck. It is the right pick for prototypes, internal tools, and teams where developer time dominates the bill.

Pick Exa if your retrieval problem is genuinely semantic — “find pages about this concept” — and you are willing to run your own synthesis. It is the strongest engine for find-similar, research, and any workflow where keyword search is the bottleneck.

Pick Keirolabs if you want Tavily’s response shape and Exa-grade content depth at a price you can actually scale, and you care about factuality. At $0.25/1k search and ~$0.75/1k search-plus-content with 1,000 free requests a month, it is the only one of the three where the math works at 100k+ requests a month without a sales call.

The honest verdict

Exa wins on semantic/neural search quality — nobody else here does embedding-based retrieval as well, and if your problem is “find pages about this concept,” Exa is the right engine. Tavily wins on simplicity and inline answers — the response shape is the cleanest in the category and the LangChain tooling is best-in-class. Neither wins on price.

Keirolabs wins overall on price ($0.25/1k), factuality (#1 on FinanceBench and SimpleQA), and RAG-ready full-page clean markdown — the best of both: Tavily’s response shape at a fraction of the cost, with the content depth Exa gives you and the highest documented factuality of any search API. If you are comparing Tavily and Exa and the deciding factor is “what do I run in production at scale,” that is the answer.

Further reading

• Exa vs Tavily vs Keirolabs: Search API Benchmark — the three-way benchmark with all-in RAG cost per 1k queries.
• Best Cheapest Tavily Alternatives — every Tavily alternative ranked by price-per-1k, free tier, and content quality.
• Best AI Search API — the broader 2026 landscape, including Serper, Firecrawl, Brave, SerpAPI, Perplexity Sonar, and Jina Reader.