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 Groq
Pricing, pros, cons, and ideal use cases — side by side.
GroqFreemium
An inference provider whose custom LPU hardware delivers exceptionally low-latency responses for open-weight models.
At a glance
| Exa | Groq | |
|---|---|---|
| Pricing | FreemiumFree trial via the dashboard; usage-based pricing published on the pricing page rather than the homepage. | FreemiumFree tier for evaluation. Usage-based paid tiers for production volume. |
| 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 | Latency-sensitive applications like voice agentsReal-time and interactive AI experiencesTeams running supported open models |
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
Groq
Pros
- Exceptional inference speed and low latency
- OpenAI-compatible API, easy to adopt
- Strong fit for real-time use cases
- Competitive usage pricing
Cons
- Curated model selection, not every model
- Pure inference — no platform or governance layer
- Capacity can be constrained at peak demand
Which should you choose?
Exa is built around teams building research and enrichment agents; Groq leans more toward latency-sensitive applications like voice agents. Shortlist the one whose strengths line up with your biggest constraint.