Groq

Groq's LPU inference delivers latency that no GPU-based competitor matches. But the model selection is limited and capacity constraints have been a real headache for production customers.

Groq's bet on custom LPU silicon paid off on the narrow dimension it targeted: inference latency. For supported models (Llama, Mixtral, and newer open-weight options), Groq delivers token speeds 5-10x faster than GPU-based providers. For real-time voice applications, interactive agents, and any use case where sub-second latency is product-critical, nothing else comes close. The API is OpenAI-compatible, which keeps integration cheap.

The weaknesses are structural. First, model availability: Groq only runs the models it has physically deployed, which is a small subset of what Together, Fireworks, or Replicate offer. You're not running Claude on Groq, and the flagship commercial models stay on their native providers. Second, capacity has been a real issue — during high-demand windows, enterprise customers have hit rate limits and waitlists, which is unacceptable for production-critical workloads without a fallback. Third, fine-tuned models and custom deployments require a higher-tier contract with sales, not a self-serve experience.

Pricing is competitive — often cheaper per token than GPU-based providers for the supported models — but the total value depends on whether your use case actually benefits from the latency. If you're doing batch inference or async agent workflows, Groq's speed advantage doesn't matter and a cheaper or broader provider wins.

Use Groq for latency-sensitive workloads on supported open-weight models. Pair it with a fallback provider (Together, Fireworks, or Anthropic direct) for reliability. Don't make Groq your default if latency isn't the bottleneck.