SambaNova

SambaNova comes from a serious place: founders drawn from Stanford's chip and machine-learning research, and a custom Reconfigurable Dataflow Unit built specifically to serve large models fast. Its differentiator in a crowded inference market is that it delivers that speed at full 16-bit precision, so you are not quietly trading accuracy for tokens-per-second the way some aggressive quantized services do — which matters for large reasoning models like DeepSeek R1 where precision shows up in output quality. The free tier lets you test, and a self-serve Developer tier scales you up per token. The reasons it lands at Worth Trying rather than a blanket Recommended are practical: the model catalog is narrower than Together or OpenRouter, there is no proprietary frontier model, and public per-token pricing is less front-and-center than GPU rivals, so you will spend a little time in the console. But if you want fast, honest-precision serving of big open models — especially with an enterprise or on-prem path in your future — SambaNova is a strong, credible option worth benchmarking against Cerebras and Groq.