WorkflowStack AI

Groq

AI Infrastructure
FreemiumVisit 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

Get a new AI workflow each week — many feature Groq and other tools in this category.