> ## Documentation Index
> Fetch the complete documentation index at: https://docs.budecosystem.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Key Concepts

> Understanding core concepts in Bud AI Foundry

Use these definitions to navigate the Bud AI Foundry platform and documentation.

## 1. Projects

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.

## 2. Models

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.

## 3. Clusters

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.

## 4. Deployments

Deployments are the runtime instances of models attached to clusters. They include configuration details like hardware settings, scaling policies, and safety controls.

## 5. Endpoints

Endpoints expose deployed models to applications through OpenAI-compatible APIs. They can include routing rules, fallback strategies, and access policies.

## 6. API keys

API keys authenticate application calls to Bud AI Foundry endpoints. Keys are project-scoped, support rotation, and can be segmented by environment.

## 7. Observability

Observability covers metrics, logs, and traces for latency, token usage, and error rates so teams can monitor reliability and cost.

## 8. Evaluations

Evaluations measure quality, latency, and safety using datasets and benchmarks so teams can choose the best model and configuration before promoting to production.

## 9. Guardrails and Bud Sentinel

Guardrails enforce safety policies during inference, while Bud Sentinel adds evaluation and runtime trust checks to validate models and responses.

## 10. Playgrounds

Playgrounds provide a safe environment for prompt iteration, model comparison, and quick prototyping before integrating APIs.