Projects are the primary workspace for teams. Each project contains its own models, deployments, endpoints, and API keys so you can isolate workloads by application or business unit.
Models are registered artifacts or managed APIs that power inference. Bud AI Foundry supports cloud providers, Hugging Face repositories, URL-based imports, and disk-mounted checkpoints, all governed under a single catalog.
Clusters represent the infrastructure where models run. They can be cloud-managed Kubernetes clusters or on-prem hardware pools, and they define the compute profiles available to deployments.
Deployments are the runtime instances of models attached to clusters. They include configuration details like hardware settings, scaling policies, and safety controls.
Endpoints expose deployed models to applications through OpenAI-compatible APIs. They can include routing rules, fallback strategies, and access policies.
Evaluations measure quality, latency, and safety using datasets and benchmarks so teams can choose the best model and configuration before promoting to production.
