Exa

Exa is a neural search API built for LLMs and AI agents, developed by Exa Labs (formerly Metaphor Systems) and backed by an $85 million Series B from Benchmark, NVIDIA, OpenAI, and other top investors. It is not a wrapper around a search engine but AI-native infrastructure with its own index and embedding models, already adopted by OpenAI, Cursor, and Notion AI.

Core Capabilities

• Neural semantic search: In-house embedding models understand query intent rather than matching keywords, achieving 94.9% semantic retrieval accuracy
• Clean content extraction: Returns parsed plain text/Markdown, minimizing LLM token consumption and lowering hallucination risk
• Ultra-fast responses: The Exa Instant endpoint has P50 latency under 200ms, meeting real-time agent workflow needs
• Deep research mode: The /research endpoint automatically runs multi-round searches and outputs structured JSON with citations
• FindSimilar discovery: Finds semantically similar pages from a URL — a capability keyword search simply cannot deliver
• Websets data extraction: Describe your need in natural language and automatically generate structured company/people lists with enriched fields

Use Cases Engineers building RAG pipelines and AI agents, product teams injecting real-time web knowledge into LLM applications, enterprise customers in finance and healthcare with strict ZDR privacy-compliance requirements, and sales teams replacing ZoomInfo with Websets for market research.

Unique Advantages The industry consensus is to use Exa for research. Tavily is faster (187ms vs 350ms) and Perplexity is better for trending news, but on semantic search quality (81% vs 71%) and zero data retention, no competitor goes head-to-head with Exa. Websets is a differentiated product in a class of its own.

Editor's Review ARR is up 1010% year over year, and customers include OpenAI and Cursor — that says it all. Users report a 40% improvement in query quality after integrating, and Websets speeds up market research 15-20x. The $10 free credit is enough to thoroughly test core features. The one caveat: at scale, multi-dimensional billing stacks up quickly, so plan your budget ahead.