Inference Is The New Bottleneck

For the last few years, the conversation has been about training — bigger models, more data, more GPUs. But training happens once. Inference happens on every single request, forever, at the edge of your product's latency budget.

Where the cost goes

In production, the model is rarely the expensive part. The expensive part is everything around it: idle GPUs waiting for traffic, 30-second cold starts on the first request, and per-token pricing that assumes you run at full capacity.

Nebula collapses that into a single serving path: a warm, autoscaled fleet with per-token billing.

Batching is the whole game

• Requests are batched across a shared, optimized fleet.
• Custom kernels squeeze more tokens per GPU-hour.
• Models stay warm, so there's no spin-up tax.

The result is 4× lower cost per token and 8ms cold starts — every day, not just at peak.