MLflow vs. Kubeflow: The Ultimate MLOps Showdown

Introduction to MLflow and Kubeflow

As machine learning becomes more complex, MLOps tools like MLflow and Kubeflow help manage the ML lifecycle. But which one is right for your needs?

Key Differences at a Glance

Feature	MLflow	Kubeflow
Primary Focus	Experiment tracking, model registry	End-to-end ML pipelines on Kubernetes
Deployment	Lightweight, standalone	Kubernetes-native
Best For	Small to medium teams	Enterprise-scale ML
License	Open-source (Apache 2.0)	Open-source (Apache 2.0)
Cloud Support	All major clouds + on-prem	Kubernetes-based (GKE, EKS, AKS)

1. Performance & Scalability

(Based on MLflow Benchmarks and Kubeflow Docs)

Metric	MLflow	Kubeflow
Max Concurrent Experiments	~10,000 (SQL backend)	100,000+ (K8s scaling)
Pipeline Execution Time	Fast (local runs)	Slower (container orchestration)
Auto-Scaling	❌ No	✅ Yes (K8s pods)

Why Kubeflow Scales Better?

• Uses Kubernetes for distributed workloads.
• Supports multi-node training (TFJob, PyTorchJob).

Why MLflow is Faster for Tracking?

• Lightweight Python-first design.
• No container overhead.

2. Cost Comparison

Factor	MLflow	Kubeflow
License Cost	Free	Free
Infrastructure Cost	Low (runs anywhere)	High (K8s cluster needed)
Managed Services	Databricks MLflow ($)	Google Vertex AI, AWS SageMaker

Pricing Examples:

• MLflow on Databricks: Starts at $0.07/DBU (~$500/month for small teams).
• Kubeflow on GKE: ~$300/month (3-node cluster).

3. When to Use Each?

Use MLflow If:

✔ You need experiment tracking & model registry.
✔ Your team uses Python-heavy workflows.
✔ You want quick setup (no Kubernetes).

Use Kubeflow If:

✔ You need large-scale distributed training.
✔ Your org already uses Kubernetes.
✔ You want end-to-end pipelines (data → deploy).

4. Deployment Options

Environment	MLflow	Kubeflow
Public Cloud	✅ (AWS, Azure, GCP)	✅ (EKS, GKE, AKS)
On-Premise	✅ (Docker, VM)	✅ (K8s cluster)
Hybrid	✅	✅

5. Big Companies Using Them

MLflow Users

• Uber (Experiment tracking)
• LinkedIn (Model versioning)
• Comcast (Reproducible ML workflows)

Kubeflow Users

• Spotify (Recommendation systems)
• Lyft (Autonomous vehicle ML)
• Intel (Chip design optimization)

Sources: MLflow Case Studies, Kubeflow Adopters

6. Key Takeaways

• Choose MLflow for:
  • Simple tracking & deployment
  • Small/medium teams
  • Non-Kubernetes environments
• Choose Kubeflow for:
  • Enterprise-scale ML
  • Existing Kubernetes infrastructure
  • Complex pipelines

Hybrid Approach? Some companies use MLflow for tracking + Kubeflow for orchestration!

Which tool does your team use? Share your experience below!

In Tlatoanix, we can help your company to decide and implement the best tools for AI/ML workflows.

#MLOps #MachineLearning #Kubeflow #MLflow #AI #Tlatoanix

References

1. MLflow Official Docs
2. Kubeflow Official Docs
3. Databricks MLflow Pricing