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MLflow vs the MLOps Ecosystem

Where MLflow stops and W&B, DVC, Kubeflow and SageMaker begin.

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Know the seams

MLflow is a hub, not the whole airport

MLflow deliberately solves four things well — tracking, projects, models, registry — and leaves the rest open. Knowing the seams stops you from either reinventing a wheel or paying for two.

NeedMLflowCommon alternative / complement
Experiment tracking✅ coreWeights & Biases, Neptune, Comet
Data/artifact versioningpartial (artifacts)DVC, LakeFS, Delta
Pipeline orchestration❌Airflow, Dagster, Kubeflow Pipelines
Model registry & lineage✅ coreSageMaker Model Registry, Vertex
Serving at scalewrapper onlyKServe, Seldon, BentoML, SageMaker
Feature store❌Feast, Tecton, Databricks FS

The healthy pattern: MLflow as the system of record for experiments and models, wired into an orchestrator (Airflow/Dagster) for scheduling and a serving platform (KServe) for scale. Resist the urge to make MLflow do orchestration; it has no scheduler, retries or DAG semantics.

Analogy

MLflow is the library catalogue, not the library, the delivery van, or the printing press. It tells you exactly which book (model) is which edition (version) and where it lives — but you still need shelves (storage), couriers (serving) and a schedule (orchestration) around it.

Reflect

Audit your current (or planned) MLOps stack.

  • ›Which of the four MLflow pillars are you using, and which tool covers the gaps?
  • ›Are you accidentally asking MLflow to orchestrate?
  • ›Where would a feature store remove duplicated feature code?

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