Which is better for a RAG pipeline, Exa or Linkup?

It depends on whether your pipeline rewards breadth or factual precision. Exa returns conceptually adjacent pages from its neural index, including academic papers and niche sources keyword search misses, which is useful when you are building a knowledge base that needs coverage. Linkup returns extracted, source-grounded answers and ranks #1 on OpenAI's SimpleQA factuality benchmark at 91.0% F-score (Deep tier), ahead of Exa at 90.04%, so it is the safer pick when a wrong fact in the context window is the failure you care about most. Many teams use Exa for retrieval breadth and Linkup where answer correctness is non-negotiable.

Is Linkup or Exa faster?

Both ship a dedicated sub-second tier for latency-sensitive agents. Linkup /fast (beta) does single-pass direct index access with no query reformulation and is marketed as sub-second; exact millisecond figures are not published. Exa publishes more granular numbers: Exa Instant runs 100-200ms and Exa Fast is sub-350ms P50, with a default Auto mode around 1s and a Deep mode around 3.5s. If you need a vendor-stated millisecond floor, Exa discloses one; Linkup states the tier exists but not the number.

How do Exa and Linkup price their search APIs?

Linkup is the cheaper headline. Its Standard and Fast tiers are $5 per 1,000 queries for search results and $6 per 1,000 for sourced or structured answers; the Deep tier jumps to $50-$55 per 1,000. Exa's Search API is $7 per 1,000 requests plus $1 per 1,000 pages for contents, with Deep Search at $12-15 per 1,000. Both have a real free tier: Exa allows up to 20,000 requests per month, Linkup gives professional-email accounts a $20 monthly credit that refills. Neither prices per token, so cost maps cleanly to request volume.

What can each do that the other can't?

Exa's findSimilar primitive, where you pass a URL and get back semantically adjacent documents from a 500B+ URL index, has no equivalent in Linkup. Linkup's edge is the other direction: a benchmark-leading accuracy posture (91.0% SimpleQA F-score) and structured, grounded-answer output aimed at human-readable correctness rather than link discovery. Exa is built for discovery; Linkup is built for accuracy.

Is there an ownership or independence concern with either?

Neither has been acquired, which separates them from Tavily (bought by Nebius in early 2026). Exa is independently VC-backed and well capitalized after a $250M Series C led by Andreessen Horowitz in May 2026 at a $2.2B valuation, with 400,000+ developers. Linkup is a much earlier-stage independent startup: it raised a $10M seed led by Gradient in February 2026, following an earlier round of roughly EUR 3M. The practical difference is runway and scale, not ownership. Exa is a funded incumbent; Linkup is a young company you are betting will keep shipping.

Exa vs Linkup: Pricing & Features Compared (2026)

Each tool is evaluated against our methodology using public docs, vendor demos, and hands-on testing.

Attribute	Exa	Linkup
Pricing tier	Freemium	Freemium
Free tier	Yes	Yes
JS rendering	No	No
Structured output	Yes	Yes
Open source	No	No
Self-host	No	No
Primary category	AI Search Apis	AI Search Apis
Notable strength	The most technically differentiated search API in the category.	The ethical play in AI search – licensing content from publishers rather than scraping positions Linkup well as the legal landscape…

Exa and Linkup are both AI search APIs, but they sit at different points in the category's maturity curve. Exa is a funded incumbent that built a proprietary neural index of the web and recently raised at a $2.2B valuation. Linkup is a seed-stage startup from 2024 making a narrower bet: be the most factually accurate search API for agents. They overlap on the surface, since both return web results for LLM applications, but the reasons to choose one over the other are specific. For the wider field, including Tavily, Perplexity's Sonar API, Parallel, and You.com, see our AI search APIs guide.

Positioning and who each is for

Exa is for teams whose product is search-shaped: research assistants, competitive-intelligence tools, content-discovery features, anything where surfacing pages that keyword search would miss is the value. Its index spans 500B+ URLs and refreshes continuously, and the company now reports 400,000+ developers and 5,000+ businesses including Cursor, Cognition, and HubSpot. That scale matters when you are choosing infrastructure you intend to build on for years.

Linkup is for teams that treat search as a fact-retrieval step inside an agent and want the lowest rate of wrong answers. It positions on accuracy and returns extracted, source-grounded answers built for consumption rather than raw blue links. It is a newer, smaller company, founded in 2024 with offices in New York, San Francisco, and Paris, and it cites customers including KPMG and Artisan. If your differentiator is that the agent does not hallucinate facts it pulled from the web, Linkup's pitch is aimed directly at you.

Retrieval approach

Exa encodes the web into dense semantic embeddings and uses next-link prediction to find conceptually relevant pages. The result is retrieval that understands what a query means rather than which words it contains. Its findSimilar primitive, where you pass in a URL and get back semantically adjacent documents, has no direct equivalent among keyword or SERP-based competitors, and Linkup does not offer it. This is what makes Exa good at discovery: it returns a more diverse mix of source types because the model does not inherit Google's bias toward high-authority commercial domains.

Linkup optimizes for a different target. Rather than maximizing recall across a neighborhood of related pages, it grounds extracted answers in trusted sources and measures itself on factual correctness. Its public claim is SOTA on OpenAI's SimpleQA benchmark at 91.0% F-score on the Deep tier, ahead of Exa at 90.04%, Perplexity Sonar Pro at 86%, and Tavily at 73%. SimpleQA measures short-form factuality, so that number speaks to "did it return the right fact," not "did it find every relevant page." The two tools are tuned for different failure modes: Exa minimizes missed-relevant-content, Linkup minimizes wrong-fact-returned.

Pricing reality

Both price per request, not per token, so budgeting maps directly to query volume. That is a relief compared with Perplexity's Sonar API, where per-million-token fees stack on top of per-request search fees.

Linkup is cheaper at the headline level. Standard and Fast tiers are $5 per 1,000 queries for search results and $6 per 1,000 for sourced-answer or structured output, roughly $0.005-$0.006 per query. The Deep tier is an order of magnitude more, $50-$55 per 1,000 queries, which is where the chain-of-thought research work gets billed. The free tier is a $20 monthly credit for accounts with a professional email, topped back up each month, and failed requests are not charged.

Exa's Search API is $7 per 1,000 requests, with each result beyond the first ten adding $1 per 1,000, and contents billed separately at $1 per 1,000 pages. Deep Search is $12-15 per 1,000. So a typical Exa call that fetches content lands near $8 per 1,000 once you add the contents fee, against Linkup's $5-6. Exa's free tier is more generous in raw volume at up to 20,000 requests per month.

For a buyer, at low-to-mid volume the dollar gap is small and Linkup is modestly cheaper. The decision should not turn on a $2-per-thousand difference. It should turn on whether you need Exa's discovery breadth and findSimilar, or Linkup's accuracy posture, because both vendors charge roughly 10x more for their deep tiers, and that is where real spend concentrates once an agent starts escalating hard queries.

Latency and speed

Both ship a tier built for latency-sensitive agents, and both let you trade speed for depth.

Exa publishes specific numbers. Exa Instant runs 100-200ms (launched February 2026 for real-time agents), Exa Fast is sub-350ms P50, the default Auto mode is around 1s, and Exa Deep is around 3.5s P50 for agentic multi-step retrieval. If you need a vendor-stated floor to design an SLA around, Exa gives you one.

Linkup offers three tiers: Fast (beta), Standard, and Deep. Fast does single-pass direct index access with no query interpretation or reformulation and is marketed as sub-second, but exact millisecond figures are not published. The company cites a 99.9% SLA-backed uptime. For an apples-to-apples latency comparison Exa is the more transparent vendor today; Linkup confirms the fast tier exists without disclosing the number, so you would benchmark it yourself before committing a latency-critical path to it.

Integrations and ecosystem

This is where the maturity gap shows. Exa provides Python and TypeScript SDKs, a LangChain integration, MCP server support, and a set of API primitives beyond search: a Contents API, AI Summaries, Monitors, and a usage-priced Agent API. The $250M Series C is earmarked partly for infrastructure to handle hundreds of thousands of searches per second, so the platform surface will keep widening.

Linkup's ecosystem is younger and narrower but growing in a sensible direction: recent integration partnerships with Baseten and FriendliAI bring its real-time search to serverless and open-source model endpoints, which is exactly where latency-sensitive agent inference is moving. If your stack already lives on those platforms, that is a real convenience. If you depend on first-party framework integrations across the board, Exa's surface is larger today, though neither matches Tavily's breadth of agent-framework plugins.

Pick Exa when, pick Linkup when

Pick Exa when discovery is the job. If your application benefits from surfacing semantically related pages, if findSimilar enables a feature you cannot build otherwise, or if you want a funded incumbent with a 500B-URL index and published latency tiers to standardize on, Exa is the stronger foundation. It is also the better choice if you are building search-heavy product features rather than a single fact-retrieval step.

Pick Linkup when factual accuracy is what you are buying. If a wrong fact in the context window is your worst-case failure, Linkup's benchmark-leading SimpleQA posture and grounded-answer output are aimed precisely at that problem, at a slightly lower per-query price. The tradeoff is stage risk: Linkup is a 2024 startup on a $10M seed, so you are betting on a young company's trajectory rather than a funded incumbent's. For accuracy-critical, latency-sensitive agent retrieval where you are willing to benchmark the fast tier yourself, that bet can be the right one.

Neither has been acquired, which distinguishes both from Tavily under Nebius. If the choice comes down to two strong-but-different options, the cleanest decision rule is Exa for breadth and discovery, Linkup for correctness. For where each fits among the rest of the field, the AI search APIs comparison maps the full field.