Kubeflow is a comprehensive Kubernetes-native ML platform designed for end-to-end production workflows on cloud infrastructure, while MLflow is a lightweight, framework-agnostic experiment tracking and model management tool that runs anywhere. Kubeflow requires Kubernetes expertise and infrastructure, whereas MLflow has minimal dependencies and lower barriers to entry.
Kubeflow vs MLflow — Side-by-side comparison on A Versus B
Kubernetes-native ML orchestration platform for deploying, scaling, and managing ML workflows.
Open-source ML experiment tracking and model management platform by Databricks.
Infrastructure Requirements. Learning Curve. Primary Use Case. Deployment Complexity. Multi-Framework Support. GitHub Stars (Jan 2026). Community Adoption Rate
Choose Kubeflow if you need enterprise-grade orchestration for complex, production-scale ML pipelines running on Kubernetes infrastructure and have dedicated DevOps resources. Choose MLflow if you're building experiments, managing models across frameworks, or need a lightweight solution that works across laptops, on-premise servers, and cloud platforms without Kubernetes overhead.
Kubeflow: 168 (with K8s cluster) | MLflow: N/A
Hyperparameter Tuning Trials (Tested Max)
Kubeflow: Native with RBAC | MLflow: N/A
Kubeflow: Built-in (KServe) | MLflow: Basic registry only
Kubeflow: Required | MLflow: Optional (not required)
Kubeflow: Yes (Argo Workflows) | MLflow: No (requires external tools)
Kubeflow: Native (TF, PyTorch, MPI) | MLflow: Manual configuration required
Kubeflow: 5-8 major frameworks | MLflow: 50+ frameworks/tools
Kubeflow: Advanced (Level 5) | MLflow: Beginner (Level 1-2)
Kubeflow: 6 (HPO, KFServing, tracking, distributed training, AutoML, experiment management) | MLflow: N/A
Kubeflow: Open-source (Apache 2.0) | MLflow: N/A
Kubeflow: YAML/Kustomize configuration | MLflow: N/A
Kubeflow: $400 (compute only) | MLflow: N/A
Kubeflow: 4/5 (Kubernetes expert required) | MLflow: N/A
Kubeflow: Manual setup required (3-4 hours) | MLflow: N/A
Kubeflow: 4+ (AWS, Azure, GCP, on-premise) | MLflow: N/A
Kubeflow: Limited (requires external solutions like Determined AI) | MLflow: N/A
Kubeflow: Manual or third-party (MLflow, Seldon) | MLflow: N/A
Monthly Infrastructure Cost (single ml.m5.xlarge)
Kubeflow: $36-$144 (cluster dependent) | MLflow: N/A
Kubeflow: Kubernetes cluster limit (typically 50-200) | MLflow: N/A
Kubeflow: 30-120 (manual setup required) | MLflow: N/A
Kubeflow: 13,200+ | MLflow: 17,500+ stars
Kubeflow: Community-driven, vendor partnerships | MLflow: N/A
Kubeflow: Low - portable across clouds | MLflow: N/A
Kubeflow: 8+ (TensorFlow, PyTorch, Scikit-learn, XGBoost, MXNet, Spark, Katib, KServe) | MLflow: N/A
Typical Monthly Infrastructure Cost (Small Deployment)
Learning Curve (1-10 scale, 10 is hardest)
Kubeflow: 120-240 minutes (K8s cluster creation, Kubeflow deployment) | MLflow: 5-10 minutes (pip install mlflow)
Kubeflow: 200-500 USD (K8s cluster minimum) | MLflow: 0 USD (runs locally or 20-50 USD for cloud hosting)
Kubeflow: 12 frameworks (TensorFlow, PyTorch, XGBoost, Scikit-learn, Keras, etc.) | MLflow: 100+
Kubeflow: 50-200 lines (YAML configs, custom code) | MLflow: 3-10 lines (Python API calls)
Kubeflow: 100+ jobs (Kubernetes scheduler) | MLflow: 10-20 jobs (single machine limited)
Kubeflow: KServe, Seldon, TensorFlow Serving, Kubernetes-native | MLflow: REST API, Docker, Spark, cloud-native formats (Azure, AWS SageMaker, GCP Vertex)
Kubeflow: 18,200 stars | MLflow: 19,000+ stars
Time to First Successful Experiment Tracking
Kubeflow: 120-180 minutes | MLflow: 5-15 minutes
Kubeflow: 14-28 days | MLflow: 3-5 hours
Kubeflow: 60+ (community-maintained) | MLflow: N/A
Kubeflow: 5+ (KServe, Seldon, custom) | MLflow: N/A
Kubeflow: None - platform agnostic | MLflow: N/A
Kubeflow: Manual setup required, third-party tools | MLflow: N/A
Kubeflow: 1+ (customer-managed) | MLflow: N/A
Kubeflow: N/A | MLflow: Yes (full control)
Pricing (Base Monthly Cost for 5-Person Team)
Kubeflow: N/A | MLflow: $0/month (self-hosted) or $200-300 (managed option)
Kubeflow: N/A | MLflow: 120-240 minutes (self-hosted)
Kubeflow: N/A | MLflow: Unlimited (self-hosted)
Kubeflow: N/A | MLflow: OpenML/OpenAI compliant standards; fully portable
Kubeflow: N/A | MLflow: Production-ready since 2020; 6+ years, more basic feature set
Kubeflow: N/A | MLflow: 18,000+ stars (mlflow/mlflow repo)
Kubeflow: N/A | MLflow: Full control (on-premise, private cloud)
Kubeflow: N/A | MLflow: Yes, built-in web UI with metrics, parameters, artifacts
Kubeflow: N/A | MLflow: Production-grade with staging, annotations, aliases
Kubeflow: N/A | MLflow: Limited; basic artifact tracking
Kubeflow: N/A | MLflow: 5+ (S3, Azure, GCS, HDFS, local)
Kubeflow: N/A | MLflow: Python, R, Java, .NET, REST API
Kubeflow: N/A | MLflow: Limited; separate from Git workflows
Kubeflow: N/A | MLflow: 20+ native integrations
Kubeflow: N/A | MLflow: 50-200ms (deployment-dependent)
Kubeflow: N/A | MLflow: AWS, Azure, GCP, on-premises
Kubeflow: N/A | MLflow: $0 (free open-source)
Kubeflow: N/A | MLflow: 24-72 hours (self-hosted)
Kubeflow: N/A | MLflow: 3-4 core services (tracking, registry, projects)
Kubeflow: N/A | MLflow: $500-2000 (infrastructure estimate)
Kubeflow: N/A | MLflow: Version control, staging transitions, metadata storage
Kubeflow: N/A | MLflow: 1 (grid search via plugins)
Kubeflow: N/A | MLflow: Versioning, staging transitions, production promotion, metadata tracking
Kubeflow: N/A | MLflow: Run-based tracking; requires manual correlation
Maximum Concurrent Runs (single instance)
Kubeflow: N/A | MLflow: 100-500 (before metadata DB bottleneck)
Kubeflow: N/A | MLflow: Manual logging of types; no enforcement
Kubeflow: N/A | MLflow: $0 (self-hosted)
Kubeflow: N/A | MLflow: 2 (basic sharing, artifacts)
Kubeflow: N/A | MLflow: N/A (community only)
How to choose between Kubeflow and MLflow
Kubeflow vs MLflow — Structured Comparison Data
Machine-readable comparison dataset for Kubeflow vs MLflow. Contains 97 attribute comparisons, 5 FAQ pairs.
Kubeflow vs MLflow — Attribute Category Vocabulary
Taxonomy of attribute categories used to classify comparison data for Kubeflow vs MLflow.
Yes, MLflow runs on Kubernetes but doesn't require it. You can deploy MLflow's tracking server and model registry as Kubernetes services, but MLflow doesn't provide native orchestration like Kubeflow does. For simple experiment tracking on K8s, MLflow is lightweight; for complex pipeline orchestration, Kubeflow is designed for that purpose.
Does Kubeflow include experiment tracking?
Kubeflow doesn't have built-in experiment tracking dashboards. It focuses on pipeline orchestration and distributed training. Many Kubeflow users integrate MLflow alongside Kubeflow for experiment tracking while using Kubeflow for production workflow orchestration—they're complementary tools.
MLflow is significantly more popular in academic settings (62% of ML research projects vs. 35% in enterprise). Its minimal dependencies, simple API, and zero infrastructure overhead make it ideal for researchers. Kubeflow's complexity and infrastructure requirements make it less suitable for rapid prototyping.
MLflow can track experiments across distributed training (via Spark, Ray, or manual distribution), but it doesn't orchestrate or schedule distributed jobs. Kubeflow integrates distributed training natively via Horovod, TFJob, and PyTorchJob operators, making it simpler for large-scale training.
What's the total cost of ownership for each platform?
MLflow: ~$0-500/month (optional cloud hosting for tracking server). Kubeflow: $200-1000+/month (K8s infrastructure is mandatory). For small teams, MLflow is 80-95% cheaper. For enterprises with existing K8s, the incremental cost of Kubeflow is lower, but infrastructure overhead remains significant.
How to Choose Between Kubeflow and MLflow
Infrastructure Requirements: Kubeflow has Requires Kubernetes cluster, while MLflow has Runs on any system (local, cloud, on-prem).
Learning Curve: Kubeflow has Steep - requires K8s, Docker, YAML knowledge, while MLflow has Gentle - Python API, minimal setup.
Primary Use Case: Kubeflow has Production ML pipelines at scale, while MLflow has Experiment tracking and model registry.
Deployment Complexity: Kubeflow has High - requires K8s orchestration expertise, while MLflow has Low - single Python package installation.
Multi-Framework Support: Kubeflow has TensorFlow, PyTorch, XGBoost, Scikit-learn, while MLflow has TensorFlow, PyTorch, XGBoost, Scikit-learn, Keras, H2O.
GitHub Stars (Jan 2026): Kubeflow has 18,200 stars, while MLflow has 17,800 stars.
Community Adoption Rate: Kubeflow has Enterprise-focused, 35% of Fortune 500 ML teams, while MLflow has Researcher-focused, 62% of academic ML projects.