Groq
AI InfrastructureFreemiumVisit Site
An inference provider whose custom LPU hardware delivers exceptionally low-latency responses for open-weight models.
Overview
Groq runs open models on its own LPU hardware, and the headline is speed — token throughput and latency well beyond typical GPU inference. For latency-sensitive use cases — voice agents, real-time assistants, interactive UX — that responsiveness genuinely changes what is possible. It is an inference layer, not a platform: you get a fast OpenAI-compatible API for a curated set of open models, and everything else (evals, governance, retrieval) is on you. Best treated as a specialized speed component in a larger stack.
Pros & Cons
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
Workflows that use Groq
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