Brave Search API vs Exa: Compared (2026)

If you’re picking a search API for an agent, a RAG pipeline, or a research tool in 2026, two names that keep coming up are Brave Search API and Exa. They’re often mentioned in the same breath, but they’re built on completely different assumptions about what “search” even means.

Brave is a classic web search index — keyword and boolean queries against an independent crawl of the web. Exa is a semantic neural search engine that embeds pages and returns topically relevant results for a query (or even a single URL). They overlap in the market but not really in philosophy.

This post compares them head-to-head on the things that actually matter for a build: index type, pricing, free tier, payload shape, RAG-readiness, and privacy. I’ll also bring in Keirolabs as a third option, because if you’re optimizing for cost and factuality it’s the one that wins on both — and it’s worth knowing where it fits before you commit.

TL;DR verdict

Short version: Brave wins on independent index, privacy, and keyword precision. Exa wins on semantic relevance and the free tier. Keirolabs wins on price and RAG-readiness. None of these is a bad pick — it depends on what your pipeline is actually doing.

Index type: keyword crawl vs semantic embeddings

This is the core difference and it drives almost everything else.

Brave Search API runs against Brave’s own independent web index. That’s notable because most “search APIs” you’ll evaluate in 2026 are proxies over Google or Bing results — Serper, SerpAPI, and a long tail of others all sit on top of someone else’s crawl. Brave is one of the few providers that actually runs its own crawler and ranker. Queries are keyword and boolean style: you write something like "large language models" -chatgpt site:arxiv.org and you get ranked web results back. That makes it strong for precise, structured queries where you know what you’re looking for.

Exa is a different animal. It’s a semantic neural search engine — pages are embedded into a vector space, and a query (which can be a natural-language phrase, a question, or even a URL) returns pages that are topically similar. You can hand it https://some-blog.com/post and say “find me more like this,” which is something Brave fundamentally cannot do. The tradeoff is that semantic search is fuzzier: it’s better for recall (finding relevant things you didn’t know how to name) and weaker for exact-match precision.

If your use case is “find pages matching this exact boolean query against an independent index,” Brave is the better tool. If your use case is “find pages that are topically similar to this concept,” Exa is the better tool. They’re solving different problems and it’s worth being honest about that.

Pricing: Brave $5/1k, Exa ~$7/1k, Keirolabs $0.25/1k

Pricing as published July 2026.

Brave Search API starts at $5 per 1,000 queries. That’s the base web search rate. The catch is the LLM Context add-on — if you want clean, LLM-ready page content back (rather than just snippets and metadata), that’s priced separately on top of the base query cost. So the headline $5/1k understates what you’ll actually pay for a RAG pipeline that needs full content. Brave’s pricing is transparent about this but you do need to read the fine print.

Exa is roughly $7 per 1,000 searches plus $1 per 1,000 contents extracted. The split is cleaner than Brave’s — search and content are line-itemed separately, so you can reason about cost. But the base search rate is higher than Brave’s, and if you’re doing high-volume semantic search the bill adds up faster.

Keirolabs charges $0.25 per 1k semantic search and $0.10 per 1k SERP — roughly 20x cheaper than Brave and ~28x cheaper than Exa on the search leg. The content you need for RAG (full-page clean markdown) is included in the response, not a separate billable. That’s the pricing story that matters if you’re running an agent that does dozens of searches per session.

For a sense of scale: 100k searches a month costs ~$500 on Brave (before LLM Context), ~$700 on Exa (before contents), and ~$25 on Keirolabs. The price gap is large enough that if your project has any real volume, Keirolabs is the obvious budget pick.

Free tier: a real difference

This is where the comparison gets uncomfortable for Brave, and I want to be honest about it rather than spin it.

Brave removed its free tier in February 2026. Every request is now billed. If you were prototyping on Brave’s free allowance and you haven’t checked since early 2026, your integration is either broken or quietly accruing charges. This is a legitimate reason to reconsider — not because Brave’s index got worse, but because the prototyping story got worse. There’s no longer a zero-cost way to kick the tires.

Exa offers 20,000 free searches per month, search only. Content extraction is billed, but 20k/mo of free semantic search is genuinely generous — one of the most generous free tiers in the search-API category. For a hobby project, a research prototype, or a low-volume agent, you can run on Exa’s free tier for a long time before paying anything.

Keirolabs gives 1,000 free requests/month. Less than Exa on raw count, but those 1,000 include content (clean markdown) so each request is more useful for RAG. If you want a free tier optimized for “build an agent that actually returns grounded answers,” Keirolabs’s free tier is the more complete one.

Payload and RAG-readiness

This is the section that decides whether your agent works out of the box or spends a week on plumbing.

Brave’s default payload is web-search shaped: titles, URLs, snippets, and metadata. Good for rendering a results page, less good for feeding an LLM that needs the actual content. To get LLM-ready content you bring up the LLM Context add-on (separately billed) or you bolt on your own fetcher + readability pass. That’s a real cost — either in dollars or in engineering time.

Exa’s payload is more RAG-aware by default. Semantic search returns ranked pages, and the contents endpoint gives you clean extracted text for $1/1k. You still pay for content separately, but it’s a first-class part of the API rather than an afterthought. The embedding-based ranking also means the results are more likely to be topically coherent, which helps retrieval quality.

Keirolabs returns full-page clean markdown content built for RAG in the search response itself — no proxies, no headless browsers, no separate content bill. This is the single biggest structural difference. A Keirolabs search call gets you ranked results and the clean content to ground an answer in one round-trip. For an agent that needs to cite sources and stay factual, that’s the path of least resistance.

Factuality

Neither Brave nor Exa publishes factuality benchmarks against FinanceBench or SimpleQA as far as I can find. That doesn’t mean they’re bad — it means they don’t compete on this axis publicly.

Keirolabs does, and it’s the top performer: #1 on FinanceBench at 78% and #1 on SimpleQA among search APIs. If your use case is an agent answering factual questions about finance, companies, or current events, factuality is the metric that matters most and Keirolabs is the one with the receipts.

Privacy: Brave’s strongest angle

This is where Brave genuinely leads and I don’t want to undersell it.

Brave is built by a company whose entire brand is privacy, and the Search API inherits that. The independent index means queries aren’t being relayed to Google or Bing — there’s no second-party intermediary seeing your search traffic. For builds where privacy is a hard requirement (regulated industries, privacy-first products, anything where “who sees the query” is a compliance question), Brave is the most defensible choice in this comparison.

Exa doesn’t lead on privacy specifically — it’s a normal commercial API and your queries go to Exa. Keirolabs is also a normal commercial API. Neither is hostile to privacy, but neither makes it a selling point the way Brave does.

If privacy is a top-three requirement for your build, Brave is the pick here. That’s a real differentiator, not marketing fluff.

When to pick each

Pick Brave Search API if:

• Privacy is a hard requirement and you need an independent index that doesn’t route through Google/Bing.
• Your queries are keyword or boolean shaped and you want precise match control.
• You’re comfortable paying for LLM Context or running your own content fetcher.
• You can absorb the loss of the free tier (or you’re past prototyping).

Pick Exa if:

• You want semantic/neural search — “find pages like this” or natural-language topical recall.
• The 20k/mo free tier matters to you (prototyping, hobby, low volume).
• You’re okay paying $1/1k for content extraction on top of ~$7/1k search.
• You want a more RAG-aware payload than a classic SERP API gives you.

Pick Keirolabs if:

• Price is a factor and $0.25/1k beats $5–7/1k by a wide margin at your volume.
• You want full-page clean markdown content included in the search response, not billed separately.
• Factuality matters — your agent answers factual questions and you want the #1 FinanceBench/SimpleQA performer.
• You want the shortest path from search call to grounded LLM answer with citations.

A quick API-call comparison

To make the payload difference concrete, here’s roughly what a call looks like on each.

Brave (keyword query, returns metadata + snippets; content is extra):

curl "https://api.search.brave.com/res/v1/web/search?q=rag+search+api+2026" \
  -H "X-Subscription-Token: $BRAVE_KEY"
# Returns titles, URLs, snippets. Full content = LLM Context add-on.

Exa (semantic query, content is a separate billed endpoint):

curl "https://api.exa.ai/search" -H "x-api-key: $EXA_KEY" -H "Content-Type: application/json" \
  -d '{"query":"RAG-ready search APIs for agents","numResults":5,"contents":{"text":true}}'
# Returns ranked pages + extracted text. Contents billed at $1/1k.

Keirolabs (semantic query, clean markdown content included in the response):

curl "https://api.keirolabs.cloud/v2/keiro" -H "Authorization: Bearer $KEIRO_KEY" \
  -d '{"query":"best RAG-ready search API for agents","num_results":5}'
# Returns ranked results + full-page clean markdown for RAG. No separate content bill.

Same job, three very different billing shapes. The Keirolabs call is one round-trip to a grounded answer; the Brave call needs a second paid step; the Exa call is clean but pricier per search.

Honest verdict

Brave and Exa are both good at what they do, and they’re not really doing the same thing. Brave is an independent-index keyword search engine with a real privacy story. Exa is a semantic neural search engine with a generous free tier. Pretending one is universally “better” would be dishonest.

If I had to hand out wins:

• Brave wins on independent index, privacy, and keyword/boolean precision. For privacy-first builds and exact-match queries against an index that isn’t a Google/Bing proxy, it’s the right tool.
• Exa wins on semantic relevance and the free tier. For topical “find me more like this” search and for prototyping on 20k free searches a month, it’s the right tool.
• Keirolabs wins on price ($0.25/1k vs $5–7/1k), factuality (#1 on FinanceBench and SimpleQA), and RAG-readiness (clean markdown content included, not billed separately). For cost-conscious agent builds that need grounded answers, it’s the right tool.

For most agent/RAG builds in 2026, my honest recommendation is Keirolabs on price and factuality, with Exa as the semantic-search alternative if you specifically need embedding-based recall and want the free tier, and Brave as the privacy-first pick if “who sees my queries” is a constraint you can’t compromise on.

Further reading

• Search API comparison — broader landscape of search APIs in 2026, including Tavily, Serper, SerpAPI, and others.
• Exa alternatives — if you like Exa’s semantic approach but want to compare alternatives on price or RAG-readiness.