ExaFreemium
A search API built for AI agents rather than humans — structured outputs, token-efficient highlights, and sub-200ms responses.
AI Infrastructure comparison
Exa vs OpenRouter
Pricing, pros, cons, and ideal use cases — side by side.
OpenRouterPaid
A unified API and marketplace that routes requests to hundreds of models from many providers through a single endpoint and bill.
At a glance
| Exa | OpenRouter | |
|---|---|---|
| Pricing | FreemiumFree trial via the dashboard; usage-based pricing published on the pricing page rather than the homepage. | PaidUsage-based — you pay per token, billed through OpenRouter on top of provider costs. |
| Category | AI Infrastructure | AI Infrastructure |
| Ideal for | Teams building research and enrichment agentsDevelopers replacing scrape-and-parse pipelinesCoding agents needing current documentationAnyone paying too many tokens for web content | Teams experimenting across many modelsApps that need breadth of model choiceDevelopers who want one API and one bill |
Pros & cons
Exa
Pros
- Roughly 90% token reduction versus fetching full pages
- Structured JSON output against your own schema
- Sub-180ms latency
- Zero data retention plus SOC 2 Type II and HIPAA coverage
- Used in production by Cursor, Cognition, and HubSpot
Cons
- Usage pricing is not on the homepage — model your volume first
- Another metered dependency in the agent loop
- Index coverage differs from Google; verify recall on your domain
- Overkill if you only need occasional lookups
OpenRouter
Pros
- One API for hundreds of models
- Easy price and latency comparison
- Automatic provider failover
- Fast way to try new models
Cons
- Adds a margin and a middleman to each call
- Less ideal than going direct for steady high volume
- No direct commercial relationship with model providers
Which should you choose?
Exa is the lighter-weight option (Freemium), while OpenRouter sits higher on the pricing ladder (Paid). Exa is built around teams building research and enrichment agents; OpenRouter leans more toward teams experimenting across many models. Shortlist the one whose strengths line up with your biggest constraint.