Groq

What it is

Groq is an AI inference platform built on its proprietary Language Processing Unit (LPU) — custom silicon purpose-built for token generation, not adapted from GPU workloads. The result is token throughput 5–10× faster than GPU-based cloud providers at comparable cost. Groq serves open-source models (Llama 3, Mixtral, Gemma) via an OpenAI-compatible API, meaning most existing OpenAI integrations work with a one-line base URL change. The platform targets latency-sensitive applications: real-time voice assistants, interactive coding tools, and agentic pipelines where response speed directly affects user experience. Trade-offs: model selection is limited to open-source checkpoints that Groq has optimised for its hardware, and the free tier is rate-limited. There is no fine-tuning or private model hosting.

LPU (Language Processing Unit) custom silicon delivers 5–10× faster token generation than GPU inference at comparable pricing — the fastest API for open-source LLMs.

When it fits — and when it doesn’t

Known weaknesses

Each one links to the public issue, changelog or docs page it came from, where we have one.

1. 01

   Limited model selection

   Only serves open-source checkpoints optimised for Groq LPU hardware (Llama 3, Mixtral, Gemma). No access to GPT-4, Claude, or Gemini.

2. 02

   No fine-tuning or private model hosting

   Unlike Fireworks AI or Together AI, Groq offers no fine-tuning API and cannot host custom or proprietary model weights.

3. 03

   Rate limits on free tier

   6,000 tokens/min and 14,400 requests/day on the free plan. Production workloads require upgrading to paid tiers.

4. 04

   Context window smaller than frontier models

   Most Groq-hosted models cap at 8K–32K context. Long-document RAG pipelines may need a different provider.

5. 05

   Proprietary hardware creates single-vendor dependency

   Speed advantage is hardware-tied — if Groq raises prices or has outages, there is no equivalent LPU-based alternative. GPU providers are the fallback.

What it does well

• Fastest inference speed on the market

  Groq consistently benchmarks at 500–800 tokens/sec on Llama 3 70B — 5–10× faster than equivalent GPU-based providers like Together AI or Fireworks AI.

• OpenAI-compatible API

  Drop-in replacement for OpenAI SDK: change base_url and api_key, no other code changes required. Works with LangChain, LlamaIndex, and most AI frameworks.

• Generous free tier for development

  30 req/min and 14,400 req/day at no cost — enough to build and test most applications without a credit card.

• Competitive per-token pricing

  Llama 3.1 8B at $0.05/1M input tokens undercuts most proprietary model APIs while delivering significantly faster response times.

• Low operational latency for real-time applications

  P50 TTFT consistently under 100ms in benchmarks, making it practical for voice interfaces, interactive coding assistants, and streaming UIs.

• No infrastructure to manage

  Fully managed API — no GPU provisioning, autoscaling config, or model deployment. Works out of the box at any scale.

Pricing

Free tier

YesRate-limited dev access — 30 req/min, 14,400 req/day, 6,000 tokens/min

Paid from

—

Model

Usage-based

Enterprise plan

Yes

Pay-as-you-go per million tokens. Llama 3.1 8B starts at $0.05/1M input tokens. Llama 3.3 70B at $0.59/1M input. No seat-based pricing — you only pay for tokens consumed.

Pricing not independently confirmed — check the vendor’s page. Vendor pricing ↗