Traditional platforms offload that complexity back to the customer: replica count, per-replica concurrency, autoscaling thresholds. This is still DIY, just at a higher abstraction. And it never stops: every new model and traffic shift means re-profiling and re-tuning, so your best engineers fire-fight production before and after shipping, and serving becomes the anchor that slows every launch. The result is the cost that matters most — models proven in dev sit for weeks before they reach production.

Re-tuning serving infrastructure by hand is a tax on every model an organization runs; at scale it becomes structural, with teams standing up dedicated serving groups whose whole job is keeping models alive and performant in production. We call it the ML Stack Tax.

Databricks Custom Model Serving is a fully managed real time inference platform for any model packaged in MLflow. Our mission is to erase that tax across three stages of a model's life so that our customer’s serving teams can focus on more sophisticated value addition:

This works because Custom Model Serving is built natively into Databricks: data, features, training, MLflow packaging, serving, and agents are one governed stack, not separate systems stitched together.