AI Serving Platform That Adapts to Your Model

Databricks Custom Model Serving is a fully managed inference platform that adapts infrastructure to each model's resource profile and traffic patterns, eliminating manual tuning.

• •Removes need for manual configuration of replica counts and autoscaling thresholds across different model types
• •Uses AutoPilot Pod Autoscaler combining request-based horizontal scaling with model-aware vertical scaling
• •Routes each model to optimal inference engine (Gunicorn, vLLM, Triton) with minimal per-request overhead
• •Learns model resource characteristics at runtime and adjusts concurrency limits to maintain low latency and cost efficiency
• •Provides isolated Kubernetes deployments with integrated observability emitting metrics, logs, and traces to Unity Catalog