Together AI has published a practical guide to how its Dedicated Model Inference service is organized, with a focus on the pieces developers control when they reserve capacity for production workloads.
The post breaks the service into three resources: endpoints, deployments, and configs. Endpoints are the stable interfaces applications call. Deployments connect those endpoints to model capacity. Configs define how the system should run a model, including settings that affect performance and routing.
The useful change is not a new model but a clearer operating map for teams that need predictable inference instead of best-effort shared access. Capacity-aware routing matters because it can send requests to deployments that have available resources, reducing surprises when demand shifts.
For engineering teams, the guide turns a managed inference product into something closer to an infrastructure workflow. It explains where to make changes, which resource owns which responsibility, and how to reason about scaling without treating the service as a black box.