Groq

Groq

Usage Based

The premier neocloud for fast inference.

APIs & Infrastructure
AI Runtime & Serving

Published 27 September 2026

Scores

Popularity4/5

A $650M fundraise and Nvidia's ~$20B LPU licensing deal signal strong external validation, though its curated model catalogue keeps it a step behind aggregators in raw adoption breadth.

Learning Curve5/5

OpenAI-compatible API and a single API key get requests flowing in minutes, no infrastructure or model-serving setup required.

Flexibility3/5

Model catalogue is curated around what runs well on LPU hardware, narrower than a general-purpose aggregator, and there's no self-hosting or custom deployment option.

Performance5/5

Sub-100ms time-to-first-token and a purpose-built LPU architecture make it the fastest, most consistent inference provider by a real technical margin, not just marketing.

Portability2/5

Closed, hosted-only SaaS running on proprietary hardware, no self-hosting path and no way to replicate the LPU speed advantage outside Groq's own infrastructure.

About Groq

Groq is an AI inference company whose GroqCloud service runs open-weight models on its own hardware rather than routing requests to other providers. That hardware is the LPU (Language Processing Unit), a chip designed specifically for running language models rather than training them, which keeps time-to-first-token low and generation speed high and consistent from one request to the next.

Groq's model lineup is curated to what runs well on LPUs: Meta's Llama family, OpenAI's open-weight gpt-oss models, Moonshot's Kimi, Alibaba's Qwen, and speech models such as Whisper, all behind an OpenAI-compatible API, so existing SDKs work by changing the base URL. Pricing is per token with no subscription and among the lowest of the major providers for small and mid-size models. A free tier with rate limits covers experimentation, and the Batch API and prompt caching each halve the cost of eligible workloads.

In December 2025, Nvidia signed a non-exclusive license to Groq's inference technology, reported at about $20 billion, and hired its founder and several senior leaders. Groq continues as an independent company and GroqCloud keeps operating, so the buying question is unchanged: for real-time chat, voice agents, and coding assistants where latency shapes the experience, Groq is usually among the fastest options at its price point.

Key Features

  • Custom LPU (Language Processing Unit) hardware built for LLM inference
  • Low, consistent time-to-first-token and high output speed
  • Pay-per-token pricing with no subscription
  • Free tier with rate limits for experimentation
  • Batch API and prompt caching, each at 50% off
  • OpenAI-compatible API for easy migration from other providers

Pros

  • Among the fastest and most consistent time-to-first-token of the major inference providers
  • Low per-token pricing, especially for small and mid-size open-weight models
  • Runs on its own LPU hardware rather than rented GPUs
  • OpenAI-compatible API keeps migration friction low
  • Free tier for trying models before paying

Cons

  • Hosted only, with no self-hosting option for data residency or full control
  • Model catalogue is limited to what runs well on LPUs, narrower than an aggregator like OpenRouter
  • A single inference provider, not a router, so there is no built-in multi-provider fallback
  • Closed frontier models such as GPT, Claude, and Gemini are not available

Groq Pricing

Usage Based
FreeFree
  • · Rate-limited access to hosted models
  • · No credit card required
Pay-As-You-GoFree
  • · Per-token pricing with no subscription
  • · From $0.05 per million input tokens (Llama 3.1 8B)
  • · Kimi K2 at $1.00 input and $3.00 output per million tokens
  • · Higher rate limits than the free tier
Batch APIFree
  • · 50% off standard per-token pricing
  • · For asynchronous jobs submitted as a batch file
EnterpriseContact sales
  • · Custom rate limits and volume pricing
  • · Contact sales for pricing

Tools Related to Groq

Works well with Groq(8)

Groq is a documented supported backend for Open WebUI (listed as GroqCloud), pairing Groq's fast inference with Open WebUI's chat interface.

LiteLLM supports Groq as one of its 100+ documented providers, letting a self-hosted gateway route requests to Groq's fast LPU-based inference.

LibreChat can add Groq as a custom endpoint in librechat.yaml, giving its multi-provider chat UI access to Groq's fast hosted open-weight models.

AnythingLLM ships Groq as a built-in LLM provider, so a Groq API key is enough to power its chats, RAG workspaces, and agents with fast hosted inference.

Llama is the flagship hosted family on Groq's LPU inference stack.

Groq hosts Moonshot's Kimi K2 on its LPU hardware and offers it through the same OpenAI-compatible API as its other models.

Alternatives to Groq(2)

Groq serves open models on its own LPU hardware as a hosted API with very low latency; Ollama runs open-weight models locally on your own machine. Groq for fast hosted inference, Ollama for private, offline, free local use.

Groq is a focused hosted API running models on its LPU hardware for speed; Hugging Face offers the largest model hub with serverless and dedicated inference endpoints. Groq for latency, Hugging Face for model breadth.

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Tags

Machine LearningWeb

Details

Maintained
Yes