Best Open Source MLOps Platforms in 2026: 8 Platforms Compared

MLflow is the default first tool for most ML teams, and for good reason. Its tracking component logs parameters, metrics, and artefacts for each run, so you can compare experiments without digging through notebooks. Its model registry then gives approved models a version, a stage, and a clear promotion path from testing to production.

MLflow does not try to be a full pipeline platform, so teams usually pair it with an orchestrator like Airflow, Flyte, or Metaflow, and a serving tool for production. That modular approach is a strength if you like choosing each piece, and a weakness if you want one vendor to own the whole stack. For tracking and registry, though, MLflow remains the safest open source starting point.

Kubeflow is the platform for teams that already run Kubernetes and want ML to live on the same infrastructure. It brings together pipelines, notebooks, training operators, and serving components under one Kubernetes-native umbrella, so a model can move from a notebook to a pipeline to a served endpoint without leaving the cluster.

That power comes with real operational cost. Installing, upgrading, and securing Kubeflow needs platform engineering skill, and a small team without Kubernetes experience will spend more time on the platform than on models. If your organisation already runs Kubernetes well, Kubeflow is a strong, coherent choice. If not, start with a lighter pipeline tool and revisit Kubeflow later.

Metaflow, originally built at Netflix, is designed for data scientists who think in Python, not in YAML or infrastructure. You write a workflow as Python code, run it locally, and then scale the same code to the cloud, with built-in handling for data, dependencies, and versioning. That short path from prototype to production is its main appeal.

Metaflow is less flexible if your stack is not Python-first, and you still need cloud knowledge to run it well in production. For a Python team that wants pipelines without adopting a heavy platform, Metaflow is one of the most pleasant open source options. Pair it with MLflow for tracking if you want the best of both.

Flyte is built for large, complex ML workloads where reliability and repeatability matter more than a quick notebook run. Workflows are typed, tasks can be cached and reused, and execution scales on Kubernetes, which suits teams running many pipelines with many dependencies. Its design helps catch errors early, before a long training job fails halfway through.

The learning curve is steeper than Metaflow or ZenML, and a small team may find Flyte heavier than it needs. For a platform team supporting many data scientists, or for workloads where a failed pipeline is expensive, Flyte is a strong, well-engineered choice. For a first pipeline, start simpler and grow into Flyte if you need its scale.

ZenML aims to stop pipeline lock-in. You write a pipeline once in Python, and ZenML lets you swap the underlying stack: a different orchestrator, a different tracking tool, or a different cloud, without rewriting the pipeline logic. That portability is valuable for teams that expect their infrastructure to change, or that run across more than one cloud.

ZenML is younger than MLflow and Airflow, so its ecosystem is smaller and some integrations are less mature. The core idea is sound, though, and for a team that wants clean, portable pipelines with a simple Python API, ZenML is worth a serious trial. Check its current integrations for your tracking and serving tools before you commit.

DVC solves a problem that experiment trackers do not: versioning the data and model files themselves. It brings Git-style versioning to large datasets and model artefacts, so you can check out the exact data version that produced a given model, and reproduce a pipeline step without guessing which file was used. For teams that care about reproducibility, that is essential.

DVC is not a replacement for MLflow, and it works best alongside a tracker. Large data also needs storage planning, whether that is cloud object storage or a shared drive, and the Git-based workflow takes a little learning for team members who have never used Git. If your models change when your data changes, and they always do, DVC belongs in your stack.

Apache Airflow is the general-purpose scheduler that many ML teams already know. It schedules and monitors workflows as directed graphs, retries failed tasks, and integrates with a huge range of systems through operators. If your ML pipelines sit next to data pipelines, Airflow lets you run both on one scheduler, which simplifies operations.

Airflow is not ML-specific, so experiment tracking, model versioning, and feature handling still need other tools. Dynamic, data-driven ML workflows can also feel clumsy in Airflow compared with Flyte or Metaflow. For scheduled batch pipelines and for teams that already run Airflow for data work, it remains a solid, proven choice.

Feast addresses a classic production bug: a model trained on one version of a feature, then served with a slightly different version, quietly performs worse. As a feature store, Feast gives training and serving the same feature definitions and values, so the model sees in production what it saw in training. That consistency matters most for real-time or frequently updated models.

Setting up Feast needs data engineering work, including feature definitions, offline and online stores, and data pipelines. For a simple batch model with a handful of stable features, it is more than you need. For a team serving many models, or a team that has already been bitten by training-serving skew, Feast is the open source feature store to evaluate first.