Execution Context
Request Routing serves as the critical dispatch mechanism within the Model Deployment lifecycle. It ensures that every inference call is directed to the optimal model instance based on real-time metrics such as latency, throughput, and model compatibility. By analyzing request headers and payload characteristics, the system dynamically selects the target service, balancing performance optimization with cost efficiency. This process prevents load imbalances and ensures high availability across the compute infrastructure.
The routing engine parses incoming API payloads to identify the required model version and input format.
It evaluates current cluster health metrics to determine available capacity for specific model families.
A decision algorithm selects the target endpoint, applying load balancing rules before forwarding traffic.
Operating Checklist
Validate incoming request schema against registered model specifications.
Query model registry for active deployments matching the requested capabilities.
Apply load balancing algorithm to select the optimal target instance.
Forward request headers and payload to the designated inference endpoint.
Integration Surfaces
API Gateway
The initial entry point where request metadata and authentication tokens are validated prior to routing logic execution.
Model Registry
A data store providing real-time status of available models, including version tags, deployment health, and resource quotas.
Inference Cluster
The distributed compute environment hosting model instances, where the selected model executes the actual inference task.
