Groq offers the fastest LLM inference at prices that undercut most competitors.
Llama 3.1 8B at $0.05/M input tokens is 3x cheaper than OpenAI GPT-4o-mini. The free tier has tight rate limits (6K-30K TPM depending on model) but is enough for prototyping.
Batch API at 50% off and prompt caching make production workloads affordable. The catch: you are limited to open-source models — no proprietary GPT-4o or Claude equivalents.
Free
Free tier rate limits are per-organization, not per-user — shared across your whole team
Compound AI tools charged separately
Basic Search $5/1K requests, Advanced Search $8/1K
Whisper ASR has a minimum 10-second billing per request even for short audio
Text-to-speech is expensive at $22-40 per million characters vs pennies for text generation
Developers needing ultra-low-latency inference
Startups optimizing inference costs on open-source models
Real-time applications requiring 500-1000+ tokens per second
Teams using Llama, Qwen, or Whisper models in production
startup
Free tier for prototyping, then pay-as-you-go. At $0.05-0.59/M input tokens for most models, costs stay minimal until you hit serious scale.
enterprise
Enterprise plan for custom rate limits, SLAs, and on-premises GroqRack deployment. Batch API at 50% off significantly reduces bulk workload costs.
OpenAI GPT-4o costs $2.50/M input tokens — 5-50x more than Groq equivalents, though you get proprietary models. Anthropic Claude Sonnet 4.6 is $3/M input. Together AI offers similar open-source models at comparable prices ($0.05-0.85/M tokens) but without Groq custom LPU hardware speed advantage. Groq wins on latency; OpenAI/Anthropic win on model capability.