Here’s a detailed parameter-wise comparison of MLflow vs Kubeflow — two popular tools in the MLOps ecosystem, often used in production-grade ML pipelines.
🔍 MLflow vs Kubeflow: Side-by-Side Comparison
| Parameter | MLflow | Kubeflow |
|---|---|---|
| 🧠 Primary Focus | End-to-end ML lifecycle: tracking, registry, model serving | Complete ML platform for building, training, and deploying models on Kubernetes |
| 🏢 Developed By | Databricks | Originally developed by Google |
| 📦 Architecture | Lightweight, can run locally or on any cloud | Complex, Kubernetes-native, cloud-oriented |
| 🔍 Experiment Tracking | ✅ Yes — via mlflow tracking | ✅ Yes — with KFP (Kubeflow Pipelines) and ML Metadata |
| 🔁 Pipeline Orchestration | ❌ Limited — pipelines support only in MLflow 2.x as simplified templates | ✅ Robust — full DAG pipelines with KFP |
| 🧪 Model Registry | ✅ Yes — versioning, stages (Staging, Prod, Archived) | ❌ Not built-in — needs third-party (e.g., MLflow Registry or Seldon) |
| 🚀 Model Deployment | ✅ Built-in serving using CLI / REST API / Docker | ✅ via KFServing, Seldon, or custom Kubernetes deployments |
| 🔄 CI/CD Integration | ✅ Easy with GitHub Actions, Jenkins, etc. | ✅ Possible via Argo Workflows, Tekton, or CI/CD tools |
| 📁 Artifact Logging | ✅ Yes — stores models, plots, images, files | ✅ Yes — via pipeline output artifacts and metadata tracking |
| 📈 Monitoring | ❌ Needs integration (Prometheus, Evidently, etc.) | ✅ Better monitoring with integrations (Kiali, Grafana, Prometheus) |
| 🧩 Extensibility | ✅ Supports plugins and REST APIs | ✅ Highly extensible with custom components |
| ⚙️ Infrastructure Requirement | Low — can run on a laptop or single VM | High — needs Kubernetes cluster |
| 📚 Ease of Use | ✅ Beginner-friendly | ❌ Steeper learning curve (requires K8s knowledge) |
| 🌐 UI/UX | Clean web UI for experiments & registry | Dashboard for pipelines, notebooks, artifacts |
| 🔌 Framework Support | Framework-agnostic: TensorFlow, PyTorch, Sklearn, etc. | Framework-agnostic: supports TensorFlow, PyTorch, XGBoost via components |
| ☁️ Cloud Compatibility | Any cloud or local | Cloud-native (GKE, EKS, AKS), best on Kubernetes |
| 🛠️ Installation | pip install mlflow or Docker | Helm, Kustomize, or Kubeflow manifests (complex setup) |
| 👨👩👧👦 Collaboration | Good for teams via MLflow Tracking Server | Stronger for large teams with full pipeline visibility |
| 🧠 Best For | Lightweight MLOps, quick setup, fast tracking | Production-grade pipelines, enterprise MLOps, scalable training |
✅ Summary: When to Use What?
| Use Case | Recommended Tool |
|---|---|
| You need quick experiment tracking & model versioning | MLflow |
| You want a complete end-to-end pipeline with orchestration | Kubeflow |
| Your team is small or just getting started with MLOps | MLflow |
| You already use Kubernetes or want heavy automation | Kubeflow |
| You want to deploy models via REST APIs | MLflow |
| You want full DAG support, distributed training, monitoring | Kubeflow |
🧪 Real-World Combo: Use MLflow inside Kubeflow
Many teams use MLflow for tracking + model registry, and Kubeflow Pipelines for orchestration. They complement each other well when integrated smartly.