What is Bud AI Foundry?
Bud AI Foundry was created to make GenAI accessible and sustainable. Instead of locking teams into expensive GPU-only stacks, the platform optimizes inference and routing on commodity hardware while still allowing burst-to-accelerator when latency or throughput requires it. This approach reduces cost, avoids hardware scarcity, and helps enterprises move from prototype to production faster.Key Benefits
2.1 GPU-optional deployment
Run on CPU-first infrastructure with the ability to burst to GPUs when workloads demand higher performance. Bud AI Foundry optimizes placement, routing, and scaling based on cost and latency targets.2.2 Unified model lifecycle
Register, evaluate, and version cloud and local models in one catalog with consistent metadata and approvals.2.3 Built-in governance
Apply guardrails, signing, and audit controls to models, routes, and deployments so teams stay compliant.2.4 OpenAI-compatible APIs
Expose deployments through familiar request formats so application teams can integrate quickly while still using Bud-native extensions for routing and safety.2.5 Performance and cost visibility
Track usage, latency, and spend across providers and clusters to optimize workloads continuously.2.6 Built-in observability
Monitor latency, throughput, token usage, and safety signals from a single dashboard, and connect metrics to deployments for fast troubleshooting.3. Primary use cases
3.1 Production inference for applications
Deploy and scale model endpoints for customer-facing apps, internal copilots, or workflow automation with consistent routing and uptime guarantees.3.2 Model governance and evaluation
Compare models and configurations with evaluations and benchmarks, then promote the best-performing versions to production.3.3 Hybrid AI infrastructure
Blend managed cloud APIs with on-prem or private deployments while keeping a single catalog, access layer, and monitoring stack.3.4 Enterprise GenAI enablement
Deliver a governed model hub and standardized deployment workflows so multiple teams can launch AI features with consistent policies.3.5 Cost optimization
Use routing and observability to shift workloads to the most cost-efficient backend without sacrificing reliability.3.6 Production readiness
Validate, benchmark, and monitor models before promoting them to critical applications.4. How Bud AI Foundry is organized
- Workspaces and projects: Define teams, access, and billing boundaries.
- Model catalog: Register cloud models and local checkpoints in a unified hub.
- Deployments and routing: Launch models on clusters and expose endpoints.
- Observability and guardrails: Monitor usage and enforce safety policies.
5. Next steps
- Platform overview to understand the architecture.
- Account setup to configure your workspace and roles.
- Quick start to deploy a model and call your first endpoint.