Groq
Ultra-fast LLM inference on custom LPU hardware with an OpenAI-compatible API — access Llama, Gemma, Mixtral, and Whisper models at sub-second latency.
Best for developers who need the fastest possible LLM inference for interactive apps where latency matters more than throughput.
Use Cases
Free Tier
30 RPM, 6K TPM, 1K requests/day, no credit card required
How to Maximize the Free Tier
The daily cap of 1,000 requests is the binding limit for most use cases — spread usage evenly across the day rather than burning through requests in bursty testing sessions. Use Gemma 2 9B for higher throughput (15K TPM vs 6K default). The API is OpenAI SDK-compatible, so swap your base_url to console.groq.com/openai to migrate existing code in one line. Combine with a free tier from another provider (like Hugging Face) for overflow when hitting daily limits. Batch smaller prompts together to maximize token throughput within the 6K TPM cap.
Getting Started
Sign up at console.groq.com → create a free API key (no credit card) → set GROQ_API_KEY environment variable → install groq Python package or use the OpenAI SDK with Groq's base URL → send a chat completion request → receive response in under 500ms.
Pros
- Speed: Custom LPU hardware delivers 5-10x faster inference than GPU-based APIs — responses often arrive in under 200ms
- OpenAI compatible: Drop-in replacement for OpenAI SDK — change base_url and you're running on Groq with zero code changes
- No credit card: Genuinely free tier with no billing info required — just sign up and start calling the API immediately
Cons
- Daily request cap: 1,000 requests/day on the free tier exhausts quickly during development sprints or load testing
- Model selection: Fewer models than Hugging Face — only offers Llama, Gemma, Mixtral, and Whisper — no image or specialized models
- Rate limiting: 30 RPM limit means interactive apps with multiple users can hit the wall fast without careful client-side throttling