MLOps Platform Selector

Compare open-source and commercial MLOps platforms for your production needs

Open-Source Platforms

MLflow

Open Source

Industry-standard experiment tracking and model registry with broad ecosystem support

✓ Experiment tracking and metrics logging
✓ Model registry and versioning
✓ Multiple framework support (TF, PyTorch, SKlearn)
✓ REST API and Python SDK
Pricing
Free (self-hosted)
Infrastructure costs: ~$200-500/month (AWS/GCP)
Best for:
Teams starting with MLOps, broad framework compatibility needed

Kubeflow

Open Source

Kubernetes-native ML platform for end-to-end orchestration and deployment

✓ Pipeline orchestration (Kubeflow Pipelines)
✓ Distributed training (TFJob, PyTorchJob)
✓ Multi-tenant notebook servers
✓ KServe for model serving
Pricing
Free (self-hosted on K8s)
Infrastructure costs: ~$800-2000/month (K8s cluster)
Best for:
Kubernetes-native teams, complex distributed training workflows

Metaflow

Open Source

Netflix's ML infrastructure framework focused on data science workflow productivity

✓ Python-first API design
✓ Built-in versioning and artifact storage
✓ AWS Batch, Kubernetes, and local execution
✓ Automatic dependency management
Pricing
Free (open source)
Infrastructure costs: ~$300-800/month (compute)
Best for:
Data scientists who prefer code-first workflows, AWS environments

Commercial Platforms

Databricks ML

Commercial

Unified analytics platform with integrated MLflow and collaborative notebooks

✓ Managed MLflow with auto-logging
✓ Feature store and model serving
✓ AutoML and hyperparameter tuning
✓ Unity Catalog for governance
Pricing
$0.40-0.75/DBU + compute costs
Typical cost: ~$3,000-8,000/month for small teams
Best for:
Data + ML teams on same platform, Spark-based workflows

Weights & Biases

Commercial

Developer-first MLOps platform with exceptional experiment tracking and visualization

✓ Interactive experiment dashboards
✓ Hyperparameter sweeps and optimization
✓ Model and dataset versioning
✓ Team collaboration and reports
Pricing
Free tier available
Team plan: $50/user/month, Enterprise: Custom pricing
Best for:
Research teams, deep learning projects, visual experiment tracking

Vertex AI

Commercial

Google Cloud's fully managed ML platform with AutoML and custom training

✓ Managed notebooks (JupyterLab, Workbench)
✓ Vertex Pipelines (Kubeflow-based)
✓ Feature Store and Model Monitoring
✓ Integrated with GCP services
Pricing
Pay-per-use (compute + storage)
Training: $0.40-3.00/hour, Prediction: $0.08-0.50/hour/node
Best for:
GCP-native teams, AutoML needs, managed infrastructure

Feature Comparison Matrix

Feature MLflow Kubeflow Databricks W&B Vertex AI
Experiment Tracking ✓ ✗ ✓ ✓ ✓
Model Registry ✓ ✗ ✓ ✓ ✓
Pipeline Orchestration ✗ ✓ ✓ ✗ ✓
Distributed Training ✗ ✓ ✓ ✗ ✓
Feature Store ✗ ✗ ✓ ✗ ✓
Model Serving ✓ ✓ ✓ ✗ ✓
AutoML ✗ ✗ ✓ ✗ ✓
Model Monitoring ✗ ✗ ✓ ✓ ✓
Setup Complexity Low High Low Very Low Low