Groq
Ultra-fast LLM inference powered by custom LPU hardware.
Pricing
Free tier
Usage-based
Adoption
→StableLicense
Proprietary
Data freshness
—Overview
What is Groq?
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.
Key differentiator
“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.”
Capability profile
Strength Radar
Honest assessment
Strengths & Weaknesses
↑ Strengths
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.
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.
30 req/min and 14,400 req/day at no cost — enough to build and test most applications without a credit card.
Llama 3.1 8B at $0.05/1M input tokens undercuts most proprietary model APIs while delivering significantly faster response times.
P50 TTFT consistently under 100ms in benchmarks, making it practical for voice interfaces, interactive coding assistants, and streaming UIs.
Fully managed API — no GPU provisioning, autoscaling config, or model deployment. Works out of the box at any scale.
↓ Weaknesses
Only serves open-source checkpoints optimised for Groq LPU hardware (Llama 3, Mixtral, Gemma). No access to GPT-4, Claude, or Gemini.
Unlike Fireworks AI or Together AI, Groq offers no fine-tuning API and cannot host custom or proprietary model weights.
6,000 tokens/min and 14,400 requests/day on the free plan. Production workloads require upgrading to paid tiers.
Most Groq-hosted models cap at 8K–32K context. Long-document RAG pipelines may need a different provider.
Speed advantage is hardware-tied — if Groq raises prices or has outages, there is no equivalent LPU-based alternative. GPU providers are the fallback.
Fit analysis
Who is it for?
✓ Best for
Teams building real-time applications requiring fast inference speeds.
Projects where low latency is critical for user experience.
Developers needing access to large models without managing infrastructure.
✕ Not a fit for
Budget-constrained projects that cannot afford usage-based pricing.
Applications with very high request volumes, potentially leading to high costs.
Cost structure
Pricing
Free Tier
Available
Rate-limited dev access — 30 req/min, 14,400 req/day, 6,000 tokens/min
Starts at
~$0.05/1M tokens
Model
Usage-based
Enterprise
Available
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.
View full pricing details ↗Performance benchmarks
How Fast Is It?
Ecosystem
Relationships
Alternatives
Works well with
Next step
Get Started with Groq
Step-by-step setup guide with code examples and common gotchas.