1. Description
Bud AI Foundry connects model onboarding, deployment, routing, observability, and evaluation into a unified platform. It supports hybrid environments so teams can run cloud APIs, local checkpoints, and private endpoints under the same governance and monitoring framework.2. System architecture
2.1 Core components
- API Gateway: Single entry point for client requests, authentication, and request routing.
- Model servers: Runtime services for model loading, batch inference, and GPU/CPU management.
- Storage layer: Model artifacts, evaluation datasets, and deployment metadata.
- Observability stack: Metrics, logs, and traces for latency, throughput, and errors.
- Control plane services: Projects, RBAC, API keys, guardrails, and policy enforcement.
2.2 Component details
2.2.1 API Gateway
- Load balancing: Distributes requests across model servers.
- Authentication: Validates API keys and JWT tokens.
- Rate limiting: Enforces usage quotas and throttling rules.
- Request routing: Selects the optimal deployment or fallback target.
2.2.2 Model servers
- Model loading: Efficient caching and warm starts for deployments.
- Batch processing: Groups requests to maximize throughput.
- GPU management: Optimizes memory utilization across replicas.
- Health monitoring: Regular health checks and auto-recovery.
2.2.3 Storage layer
Stores model artifacts, deployment configuration, evaluation datasets, and runtime logs with encryption and access policies applied at rest.3. Request flow
3.1 Text generation request
- Client sends a request to the API Gateway.
- Gateway authenticates credentials and checks rate limits.
- Routing selects the optimal deployment.
- Model server loads or reuses the model, runs inference, and returns a response.
3.2 Image generation request
- Preprocess the prompt and select an image model.
- Run diffusion-based generation.
- Post-process and store the image output.
- Return an accessible URL to the client.
4. Scaling architecture
4.1 Horizontal scaling
Scale out by adding model replicas and gateway instances to handle higher request volumes.4.2 Vertical scaling
Scale up by assigning larger hardware profiles to improve throughput and latency targets.5. High availability
5.1 Redundancy
- Multi-zone deployment to spread risk across availability zones.
- Model replication with multiple copies of each model.
- Database replication for metadata and audit trails.
- Gateway redundancy for uninterrupted request handling.
5.2 Failover strategy
Failover policies reroute traffic when replicas or clusters degrade, ensuring continuity for critical applications.6. Performance architecture
6.1 Caching strategy
Cache frequently used models, prompt templates, and response artifacts to reduce cold starts and improve latency.6.2 Batch processing
Batching reduces overhead per request and increases throughput during peak load.7. Security architecture
7.1 Network security
Network policies isolate control plane services from data plane traffic, with ingress controls enforced at the gateway layer.7.2 Data security
- Encryption at rest: All stored data is encrypted.
- Encryption in transit: TLS for all communications.
- Key management: Integrated with KMS providers.
- Access control: RBAC with fine-grained permissions.