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 Weaviate
Pricing, pros, cons, and ideal use cases — side by side.
WeaviateFreemium
An open-source vector database for production semantic search and RAG, available self-hosted or as a managed cloud service.
At a glance
| Exa | Weaviate | |
|---|---|---|
| Pricing | FreemiumFree trial via the dashboard; usage-based pricing published on the pricing page rather than the homepage. | FreemiumOpen-source and free to self-host. Weaviate Cloud is a managed, usage-based service. |
| 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 building production RAG and semantic searchSaaS apps needing multi-tenant data isolationEnterprises wanting self-hosted or managed options |
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
Weaviate
Pros
- Mature open-source vector database
- Hybrid keyword-plus-vector search
- Multi-tenancy for isolating customer data
- Self-hosted or managed cloud
Cons
- Self-hosting carries an operational burden
- Retrieval quality still depends on your tuning
- Another datastore to run and monitor
Which should you choose?
Exa is built around teams building research and enrichment agents; Weaviate leans more toward teams building production rag and semantic search. Shortlist the one whose strengths line up with your biggest constraint.