Deployments
Dedicated capacity for a base or custom model: pick a performance tier and Sference provisions and runs it for you.
Deployments
A deployment gives a model dedicated capacity: reserved throughput and predictable performance on a private endpoint, separate from the shared serverless pool. Deploy any catalog model or one of your own custom models, with the same OpenAI-compatible API, same pinned versions, same audit trail.
Performance tiers
Instead of choosing hardware, you choose a performance tier. Sference maps the tier to the right capacity and provisions it for you, so you describe the outcome you need, not the GPUs behind it.
| Tier | Best for |
|---|---|
| Cost saving | Lowest cost, for async and latency-tolerant workloads. |
| Standard | Balanced cost and speed. The default. |
| Turbo | Highest throughput and lowest latency. |
Hardware is handled for you
You never pick or manage GPUs. Sference selects the hardware behind your chosen tier, provisions it, and keeps it running.
Request a deployment
- In the console, open Deployments and choose Create deployment.
- Select the model: a base catalog model or one of your available custom models.
- Choose a region, a performance tier, and the replica range (min/max), then give the deployment a name.
- Submit. Sference provisions and activates it; the status moves from pending through deploying to available.
Use it
Once the deployment is available, send traffic to it the same way you call any model on the API: streaming, tool calling, and structured output all work unchanged. Because the model id and request shape are identical to serverless, moving a hot path onto dedicated capacity needs no code changes beyond routing.
Pricing
Dedicated capacity is arranged with your account. Talk to us to set up a plan that fits your throughput and commitment.