Cerebras

About

Cerebras is an AI inference platform built on the Wafer-Scale Engine, a purpose-built chip that delivers inference speeds 20x faster than GPU-based competitors like OpenAI and Anthropic. If you have ever waited seconds for a long response from GPT-4 or Claude, Cerebras eliminates that bottleneck entirely. The platform serves popular open-source models including Llama, Qwen, DeepSeek, Mistral, and GLM through a drop-in OpenAI-compatible API, meaning you can switch your existing code with a single base URL change. The free tier is genuinely generous: unlimited access to all Cerebras-powered models with community Discord support, making it one of the best ways to experiment with fast inference at zero cost. The Developer tier adds 10x higher rate limits and priority processing starting at just $10 self-serve. Enterprise customers get dedicated queue priority, custom model weights, fine-tuning services, and guaranteed uptime with a dedicated support team. Cerebras Code Pro offers a $50/month plan with 24 million tokens per day, ideal for indie developers, and a $200/month Max plan with 120 million tokens per day for heavy coding workflows and multi-agent systems. Cerebras has landed major enterprise customers including OpenAI (for low-latency inference), Meta, GSK, Mayo Clinic, AlphaSense, and Notion. The recent AWS partnership brings Cerebras inference to AWS Marketplace and Bedrock, making it accessible through existing cloud billing. Additional integrations with OpenRouter, Hugging Face, and Vercel make adoption straightforward for any stack. The main limitation is the model selection: you are restricted to supported open-source models, with no access to proprietary models like GPT-4 or Claude. For teams that need raw speed on open models, though, nothing else comes close.

Key Features

• 20x faster inference than GPU-based competitors
• Drop-in OpenAI-compatible API
• Supports Llama, Qwen, DeepSeek, Mistral, GLM models
• Free tier with access to all models
• Cerebras Code Pro for IDE integrations
• AWS Marketplace and Bedrock integration
• OpenRouter, Hugging Face, and Vercel integrations
• Custom model weights and fine-tuning (Enterprise)
• SOC2 and HIPAA certification
• On-premises deployment option

Use Cases

• 1Real-time AI copilots and deep search requiring sub-second responses
• 2Multi-step agent workflows that stall on slow inference
• 3Code generation, debugging, and refactoring at interactive speed
• 4Voice AI applications needing instant accurate responses
• 5Production LLM APIs for startups and enterprises
• 6Heavy coding workflows with multi-agent systems