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 Unstructured
Pricing, pros, cons, and ideal use cases — side by side.
UnstructuredFreemium
A platform for turning messy enterprise documents — PDFs, slides, emails, scans — into clean, structured data ready for RAG and LLMs.
At a glance
| Exa | Unstructured | |
|---|---|---|
| Pricing | FreemiumFree trial via the dashboard; usage-based pricing published on the pricing page rather than the homepage. | FreemiumOpen-source libraries are free. The managed API and platform are sold on usage-based paid plans. |
| 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 | Enterprises building RAG over real document setsTeams handling PDFs, scans, and complex file typesData engineers preparing content for LLMs |
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
Unstructured
Pros
- Purpose-built for messy real-world documents
- Handles PDFs, tables, scans, and many formats
- Connectors for common enterprise data sources
- Open-source or managed API
Cons
- Complex document extraction is never perfect
- Managed API costs scale with document volume
- One stage of the pipeline — not end-to-end RAG
Which should you choose?
Exa is built around teams building research and enrichment agents; Unstructured leans more toward enterprises building rag over real document sets. Shortlist the one whose strengths line up with your biggest constraint.