GroqFreemium
An inference provider whose custom LPU hardware delivers exceptionally low-latency responses for open-weight models.
AI Infrastructure comparison
Groq vs LiteLLM
Pricing, pros, cons, and ideal use cases — side by side.
LiteLLMFreemium
An open-source LLM gateway that gives you one consistent API and proxy across 100+ model providers, with key management and spend tracking.
At a glance
| Groq | LiteLLM | |
|---|---|---|
| Pricing | FreemiumFree tier for evaluation. Usage-based paid tiers for production volume. | FreemiumOpen-source and free to self-host. A paid enterprise edition adds SSO, audit logs, and support. |
| Category | AI Infrastructure | AI Infrastructure |
| Ideal for | Latency-sensitive applications like voice agentsReal-time and interactive AI experiencesTeams running supported open models | Teams using multiple model providersPlatform teams centralizing LLM access and spendEnterprises wanting provider portability |
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
LiteLLM
Pros
- One consistent API across 100+ providers
- Proxy adds key management, budgets, and spend logging
- Avoids provider lock-in
- Lightweight to adopt
Cons
- Self-hosting the proxy is your operational burden
- A gateway is one more hop to monitor
- Advanced governance features are in the paid edition
Which should you choose?
Groq is built around latency-sensitive applications like voice agents; LiteLLM leans more toward teams using multiple model providers. Shortlist the one whose strengths line up with your biggest constraint.