GroqFreemium
An inference provider whose custom LPU hardware delivers exceptionally low-latency responses for open-weight models.
AI Infrastructure comparison
Groq vs Portkey
Pricing, pros, cons, and ideal use cases — side by side.
PortkeyFreemium
An AI gateway that adds routing, caching, observability, and guardrails to LLM traffic through a single control plane.
At a glance
| Groq | Portkey | |
|---|---|---|
| Pricing | FreemiumFree tier for evaluation. Usage-based paid tiers for production volume. | FreemiumFree developer tier. Paid Pro and Enterprise plans add volume, self-hosting, and governance. |
| Category | AI Infrastructure | AI Infrastructure |
| Ideal for | Latency-sensitive applications like voice agentsReal-time and interactive AI experiencesTeams running supported open models | Enterprises standardizing LLM access across teamsPlatform teams needing routing and failoverOrgs wanting cost and reliability controls in one place |
Pros & cons
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
Portkey
Pros
- Routing, failover, and caching out of the box
- Built-in observability and cost analytics
- Guardrails enforced at the gateway
- Managed or self-hosted
Cons
- Another vendor in the critical request path
- Overlaps with observability tools you may already run
- Full governance features need a paid plan
Which should you choose?
Groq is built around latency-sensitive applications like voice agents; Portkey leans more toward enterprises standardizing llm access across teams. Shortlist the one whose strengths line up with your biggest constraint.