I have spent the last year asking teams the same question at every conference. Your demo works. Why is it not in production?

The answers are always the same. Latency is unpredictable. Costs swing month to month. Nobody can tell legal where the data actually lives. The model was never the problem. The system around it was.

That is the gap Nebius Token Factory is built to close. And the part I find most interesting is that it treats deployment as something you design, not something you inherit.

Why this matters right now

Most inference platforms hand you a shared endpoint and wish you luck. Token Factory hands you the controls. You choose the GPU type, define GPUs per replica, set scaling limits, and pick your region. Your fine-tuned checkpoint deploys to an isolated endpoint that behaves the way you configured it to behave.

The result is the three things every production team actually needs. Stable latency, because the endpoint is yours. Predictable cost, because pricing is transparent per token. Clear data residency, because you decide whether inference runs in the EU or the US.

Dedicated endpoints with isolation and autoscaling. Distillation can cut inference cost and latency by up to 70%. Llama, DeepSeek, Qwen, GPT OSS and more. Explore the platform

Explore Nebius Token Factory

This edition of The Ravit Show newsletter is sponsored by Nebius. As always, the takes are mine.

The Ravit Show | Data & AI interviews, insights and events | 137k+ subscribers