MLflow vs Kubernetes Native Model Registry: Speed & Cost

#mlflow #kubernetes #modelregistry #mlops

MLflow vs Kubernetes Native Model Registry: Speed & Cost

Most teams pick MLflow because "everyone uses it." Then they discover their model registry takes 4 seconds to fetch metadata, costs $200/month in S3 storage, and requires a dedicated server just to stay online.

Kubernetes-native registries (storing models as container images in your existing container registry) sound hacky at first. But after running both in production for six months, the performance gap is impossible to ignore.

Here's what actually happens when you benchmark them.

Close-up of the 'Standesamt' sign at the Zittau City Hall, Germany. — Photo by Jonas Horsch on Pexels

What You're Actually Comparing

MLflow Model Registry: Python-native model versioning system. Models stored as artifacts (pickle, ONNX, SavedModel) in S3/GCS/Azure Blob. Metadata in SQL database (SQLite, PostgreSQL, MySQL). REST API for model retrieval.

Kubernetes Native Registry: Models packaged as container images, versioned via Docker tags, stored in container registry (Docker Hub, ECR, GCR, Harbor). No separate model storage layer — your deployment manifest references the image directly.

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