Exa
Semantic search driven by neural embeddings, offering find-similar, clean extraction and an integrated answer endpoint.
Where Exa lands
Composite scores out of 10, from benchmark runs where every provider faces the identical task corpus. Rank is within that capability's field; the newest run backing this page is 2026-06-21. Full method: how we measure.
- Correctness
- 1.00
- Citation faithfulness
- 0.48
- Latency p50
- 1.5 s
- Cost
- $0.005 / call
- Errors
- 0%
- Recall@10
- 0.61
- Latency p50
- 1.6 s
- Cost
- $0.014 / useful result
- Errors
- 0%
The honest pitch
Reach for it if the task is semantic discovery, find-similar lookups, research, or using its built-in /answer endpoint.
Skip it when full keyword/SERP parity is required; content and summary are billed per page and per type, and those charges stack.
About Exa
Exa searches by meaning: neural embeddings surface pages matching what a query intends rather than the words it uses. Around that core sit a find-similar capability, first-party extraction that comes back clean, and an integrated answer-and-research layer — so a fuzzy starting idea can progress to relevant sources and then to a synthesized response without switching tools. For research, discovery and recommendation tasks — the places keyword search underperforms — it is the strongest fit, and it carries certification for sensitive domains such as healthcare.
Reaching it through NativePort
Identical paths, parameters and responses to Exa's own documentation — the gateway holds the upstream credential and meters your balance at Exa's real published usage price.
curl https://api.nativeport.ai/exa/<native-path> \ -H "Authorization: Bearer $NATIVEPORT_API_KEY"