ML platforms, compared honestly.

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The shortest path from "we need an MLOps platform" to a defensible shortlist: what the category actually contains, how the managed suites and the open-source stack differ layer by layer, and a selector that weights those layers against your constraints. Every comparison is built from vendor documentation, pricing pages, published architecture guides and open-source code, not from a paid trial of each product.

• What an MLOps platform is: the seven layers

  The functional definition the rest of this site is built on, and the scoring sheet to run a vendor against before anyone books a demo.

• Enterprise MLOps platform comparison

  SageMaker, Vertex AI, Databricks, Azure ML and Kubeflow side by side, with the vendor questions and POC protocol that decide the call.

• SageMaker alternatives, component by component

  What you are actually replacing, which alternative replaces it well, and where the migration cost really sits.

• Feature store comparison: Feast, Tecton, Hopsworks

  The layer teams get wrong most often, compared on freshness, point-in-time correctness and operational burden.

• Model serving compared across the three clouds

  Autoscaling signals, multi-model density and data coupling, which is where managed serving stacks actually diverge.

• Assembling an open-source MLOps platform

  Which project covers which layer, the three assembly patterns that actually work, and the licence traps to check before committing.

• Kubeflow vs Metaflow vs Flyte, compared

  The orchestrator is the substrate everything else runs on. Flyte vs Kubeflow vs Metaflow on developer experience, Kubernetes needs, typing and scale.

• MLOps Platform Selector

  Weight the capability axes you care about, set hard constraints, and get a ranked shortlist with each platform's gap named.