Cerebras is best known for its Wafer-Scale Engine: instead of following the usual path of shrinking dies and networking many of them, Cerebras keeps a whole wafer intact as one enormous processor with on-chip memory, which removes a large share of the communication overhead that normally limits AI workloads. The company pairs that silicon with its CS-series systems, and the current generation, CS-4, is a rack-scale solution that Cerebras says delivers up to 30x faster inference than GPU-based systems.
The commercial model has two arms. First, the Cerebras Inference Cloud: a public endpoint serving open and frontier models behind an OpenAI-compatible API, so developers can swap a base URL and key into existing code and get substantially lower latency. Second, dedicated and on-premise deployments for organisations with data-residency, compliance or latency constraints, plus training and fine-tuning services on the same platform.
The company targets workloads where speed changes what is possible: coding assistants that need to keep developers in flow, agentic workflows that cannot afford timeouts, real-time voice and conversation, and deep-search or reasoning applications that need many tokens per second within a tight latency budget. Customers and partners have included major cloud providers and enterprises, and the messaging leans heavily on price-performance relative to GPU clouds. Because the flagship systems and dedicated clusters are sold directly, enterprise pricing is quoted on contact, while the cloud endpoint typically starts with a free trial.
