Kubeflow vs. MLflow: Choosing the Right Tool for Your ML Team

As machine learning matures from research to production, reliable tooling becomes essential not just for training models but for managing the entire ML lifecycle. That’s where Kubeflow and MLflow come in.

Both are powerful open-source tools designed to streamline machine learning operations (MLOps). While they are often compared, they serve different purposes and, in many cases, can work better together.

What Are Kubeflow and MLflow?

What is Kubeflow?

Kubeflow is a full-featured MLOps platform built to run ML workloads on Kubernetes. It’s designed for scale, helping teams build, orchestrate, and deploy complex ML pipelines in cloud-native environments.

Key capabilities:

  • Training Pipelines:  Define and automate multi-step ML workflows.

  • Hyperparameter Tuning: Run large-scale search experiments.

  • Model Deployment: Serve models via TensorFlow Serving or custom containers.

  • Notebook Servers:  Run Jupyter notebooks in isolated environments.

  • Built-in Kubernetes integration: Leverages cloud-native infrastructure.

  • Multi-user support:  Designed for teams with isolated workspaces.

What is MLflow?

MLflow, created by Databricks, focuses on experiment tracking and model lifecycle management. It’s lightweight, runs anywhere (locally or in the cloud), and integrates easily with your existing ML workflows.

Key capabilities:

  • Experiment Tracking:  Log parameters, metrics, and model outputs

  • Projects:  Package ML code and environments for reuse.

  • Model Registry:  Manage models across stages (e.g., Staging → Production).

  • Model Serving: Serve models with basic deployment support.

Kubeflow is built for orchestration and scale on Kubernetes, while MLflow is built for tracking and simplicity, ideal for early-stage or smaller teams. 

Kubeflow vs. MLflow: What's the Difference?

While both Kubeflow and MLflow are designed to support machine learning operations, they differ significantly in their architecture, complexity, and focus. 

Scalability: Kubeflow is tightly integrated with Kubernetes, meaning it requires a Kubernetes cluster to run. This makes it well-suited for scalable, containerized environments but also adds a layer of infrastructure complexity. In contrast, MLflow is platform-agnostic, which means it can run locally, on virtual machines, or in the cloud, offering much more flexibility in terms of setup.

Scope:  In terms of scope, Kubeflow is an end-to-end MLOps platform that focuses heavily on the orchestration of ML pipelines. It’s ideal for managing the entire ML workflow, from data preparation to training and deployment. MLflow, on the other hand, is more lightweight and focused on experiment tracking, model versioning, and packaging, making it a great tool for individual developers or teams looking for a simpler entry point into MLOps.

Complexity: When it comes to complexity, Kubeflow has a steeper learning curve due to its reliance on Kubernetes and its broader feature set. MLflow is much simpler to set up and use, making it more accessible for teams that don’t need a full orchestration layer. For model deployment, Kubeflow provides built-in serving capabilities, especially for TensorFlow and custom Docker containers. MLflow supports deployment too, but typically through integration with external tools or services rather than offering it out of the box.

How to Choose Between Kubeflow and MLflow

If you’re evaluating which to adopt, consider your team’s current needs and infrastructure maturity: 

Use Kubeflow if:

  • You’re already using Kubernetes or want to scale ML pipelines.

  • You need complex, automated workflows with reproducibility.

  • You manage multi-stage pipelines for training, validation, and deployment. 

  • Your team is large or already working in cloud-native environments. 

Use MLflow if:

  • You want to start small with fast setup and minimal dependencies.

  • You focus more on experiment tracking, comparisons, and model versioning.

  • You’re working solo or in a small team and need something flexible.

  • You’re looking for a tool that works anywhere, not just on Kubernetes.

Can Kubeflow and MLflow Work Together?

Many teams use them together for the best of both worlds. Here’s how integration typically looks:

Use MLflow within Kubeflow pipelines to track experiments, log metrics, and capture artifacts.

Use Kubeflow to orchestrate large-scale training workflows, and MLflow to register models once the training is complete.

Combine Kubeflow’s orchestration with MLflow’s model registry for version control and staged deployment.

Example: Using Both in Practice

Imagine a team building a fraud detection system. They build the pipeline with Kubeflow (data preprocessing → model training → validation). Inside the training step, use MLflow Tracking to log metrics, parameters, and artifacts. Once the pipeline completes, use MLflow’s Model Registry to manage models, push them to production, and handle version rollbacks.

This hybrid setup brings automation, scalability, and visibility without compromising on simplicity. 

Conclusion

You don’t always have to choose between Kubeflow and MLflow. Sometimes the right choice is both. But if you do need to pick, match the tool to your current maturity:

  • Starting small? Go with MLflow.

  • Scaling fast? Choose Kubeflow.

  • Building for both today and tomorrow? Combine them.

Both are open-source, widely adopted, and improving rapidly. Whichever path you take, you’re building on strong foundations.

At CodeAcme, we help teams build scalable, secure, and production-ready ML systems. Whether you’re just starting or scaling, we’d love to support your journey.

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