Why developers are choosing Linkup over Exa

How Each API Retrieves Information

The most important difference between Linkup and Exa is where semantic matching happens, and this determines retrieval precision for everything downstream.

Exa's index unit is the web page. When Exa crawls the web, it converts each page into a single document-level embedding. When you query Exa, the engine matches your query against those page-level embeddings and returns the most semantically similar pages. You can then optionally request highlights, which are query-relevant excerpts extracted from those pages after retrieval. The semantic matching, however, already happened at the page level: what you get is the best-matching document, not necessarily the best-matching fact.

Linkup's index unit is the information fragment. Each page is chunked into distinct semantic units at index time, and each chunk is embedded separately. When you query Linkup, the engine matches your query directly against those fragments. You receive the specific piece of content that answers your question, not the full document it lives in. The grounding is tighter because the match happened at the level of meaning, not the level of the page.

This architectural difference has real consequences:

• For semantic discovery, finding pages similar to a reference, exploring a topic neighborhood, building entity lists, Exa's page-level approach is well-suited. Its Websets product is particularly strong here.
• For RAG pipelines, compliance research, and multi-entity fact-checking, where you need the right fact and not the right document, Linkup's fragment-level retrieval produces more precise, lower-hallucination results.

Accuracy: What the Benchmarks Show

On simple, single-fact retrieval, both providers perform well. The gap becomes significant on complex, multi-hop queries, which are the workloads that define production AI agents.

On SimpleQA, OpenAI's factuality benchmark across science, politics, arts, and technology:

On Linkup's open-source complex query evaluation, 600 queries drawn from real user traffic with blind LLM-as-judge scoring:

• Linkup retrieved up to 3x more unique source domains per query
• Missing-entity rates were up to 4x lower
• Linkup had the lowest hallucination rates across all four providers tested

The benchmark methodology and evaluation code are publicly available for replication.

Pricing: What You Actually Pay

Exa's published price is $5–7 per 1,000 requests depending on tier. In practice, most AI applications need page content alongside results, and each additional content type is a separate billing line.

The deep research comparison is the most significant. Exa's research tier compounds per search operation and per page read. A query that reads 70 pages internally costs roughly $375 per 1,000 calls. Linkup Deep Search is flat at $50 per 1,000 queries, regardless of how many internal steps are required.

Linkup has two tiers and two prices, with no per-result, per-content, or per-token charges on top.

Latency

Speed was historically one of Exa's clearest advantages. With the launch of Exa 2.0, Exa Fast now delivers sub-350ms P50 latency, which is genuinely fast.

Linkup launched its own Fast tier in February 2026 at sub-second latency, at the same $5 per 1,000 queries as Standard. Both providers now offer competitive real-time performance. The differentiation has shifted back to accuracy and cost.

Compliance

For organizations in Europe, the compliance posture of a search provider is part of the infrastructure decision.

Linkup is the only European web search API, built with GDPR compliance as a structural requirement. It includes zero data retention, a signed Data Processing Agreement (DPA), SOC 2 Type II certification, and infrastructure that operates within EU data centers. These properties matter for enterprise procurement reviews, regulated industries, and any organization that needs to demonstrate data residency to an internal or external auditor.

Exa does not offer equivalent compliance positioning for European deployments.

Linkup vs Exa: At-a-Glance Comparison

When to Choose Linkup vs Exa

Choose Exa if your primary use case is semantic discovery or entity enrichment, such as finding content similar to a reference, building lead lists, or exploring a topic through neural similarity. Its Websets product is purpose-built for this and has no direct equivalent in Linkup.

Choose Linkup if your use case centers on factual accuracy, multi-hop research, or production RAG pipelines where hallucination rates and source diversity matter. If you are expanding in Europe or working in a regulated industry, Linkup is the only option with GDPR compliance built into the infrastructure. At scale, Linkup's flat pricing model also makes cost forecasting significantly more reliable.

FAQ

What is the main difference between Linkup and Exa?

The core difference is where semantic matching happens. Exa indexes and matches at the page level: you get the most relevant document, with optional highlight extraction after retrieval. Linkup indexes and matches at the fragment level: each page is chunked at index time, and retrieval returns the specific piece of content that answers your query. This produces more precise grounding for AI agents handling complex or multi-entity questions.

Is Linkup better than Exa for RAG?

Yes, for most RAG use cases. Linkup's fragment-level retrieval reduces context window noise and produces lower hallucination rates. On SimpleQA, Linkup scores 92% vs 63% for Exa. For complex, multi-hop queries, the kind that define production agents, Linkup retrieves up to 3x more unique sources and has up to 4x lower missing-entity rates.

Is Linkup cheaper than Exa?

For standard search with page content, Linkup is cheaper and more predictable. Linkup Standard is $5 per 1,000 queries regardless of result count or content type. Exa charges $7 per 1,000 queries for search, plus additional fees per content page and per extra result. For deep research, the difference is dramatic: Linkup Deep is $50/1K flat; Exa's research tier can reach $375–400/1K depending on how many pages are read internally.

Which is better for AI agents: Linkup or Exa?

Linkup is generally the better choice for AI agents that need factual accuracy, multi-hop reasoning, or grounded answers. The fragment-level retrieval model is better aligned with how agents consume context. Exa is a strong choice for agents focused on discovery tasks, such as finding similar companies, surfacing adjacent entities, or exploring topic neighborhoods.

Does Linkup work with LangChain, LlamaIndex, and other AI frameworks?

Yes. Linkup integrates with LangChain, LlamaIndex, CrewAI, n8n, Zapier, and Make, as well as direct API access via Python and JavaScript SDKs.

Is Linkup GDPR-compliant?

Yes. Linkup is the only European web search API, with EU data residency, zero data retention, SOC 2 Type II certification, and a signed DPA available for enterprise deployments. Exa does not offer equivalent compliance for European organizations.

Can I use Linkup instead of Exa in my existing application?

In most cases, yes. Linkup offers a simple REST API and SDKs for Python and JavaScript, and integrates with the same orchestration frameworks as Exa (LangChain, LlamaIndex, CrewAI, MCP). The main migration consideration is that Linkup returns information fragments rather than full documents, so prompts that previously expected full page content may need adjustment.

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