Cerebras builds wafer-scale AI chips and runs a cloud service that serves models at extremely high token speeds. Its CS-4 rack-scale system is pitched as up to 30 times faster at inference than GPU-based setups, and the public endpoint offers a rotating lineup of open models such as Qwen and Kimi alongside other hosted options. Developers connect with an API key, and drop-in OpenAI API compatibility means existing code can switch over in under 30 seconds.
The audience is engineering teams where response time directly shapes the product: coding assistants, voice agents, search products and multi-step agent workflows. Startups use the cloud endpoint to ship fast without buying hardware, while larger enterprises can choose dedicated capacity or on-premise installs to meet data residency and compliance needs. Machine learning groups that want to adapt a model can also fine-tune or pre-train on their own data, keeping training and serving on one stack.
Customer case studies show the typical scenarios. Code generation tools debug and refactor in near real time so developers stay in flow. Agent builders run multi-step workflows without delays or timeouts, and research products return complex reasoning answers in under a second for deep search and analysis. Conversational products, including voice assistants, rely on instant responses to keep exchanges natural. Partnerships with companies such as Lovable and AMD show where the speed is applied, from AI software creation to disaggregated inference that pairs high-throughput prefill with fast token generation. A free trial of the Cerebras Inference Cloud is the entry point for developers.
