Managed API
Fewer infrastructure duties, with provider-specific controls, limits, pricing, and data terms.
AI Inference Guide
AI Model Deployment
Move a validated model into production with explicit capacity, security, observability, and rollback decisions, not just a working inference endpoint.
Choose the serving shape
Dedicated service
More control over models and hardware, but your team owns scaling, patching, and failure recovery.
Edge or on-device
Local execution can improve privacy and offline behavior, within device memory and compute limits.
Production release path
- 1. ContractVersion the model, tokenizer, prompt, tool schema, and response contract together.
- 2. CapacityLoad-test representative inputs and set concurrency, queue, timeout, and token limits.
- 3. SafetyApply authentication, least privilege, input limits, output handling, and abuse controls.
- 4. EvaluationRun offline quality and safety suites before sending production traffic.
- 5. Progressive rolloutStart with internal or canary traffic and compare against the current release.
- 6. RecoveryKeep a tested rollback path for both model artifacts and surrounding application changes.