MLflow vs Kubeflow: Choosing the Right Orchestration Framework for Your MLOps Stack

When building an MLOps stack, one of the most fundamental decisions teams face is selecting an orchestration and lifecycle framework. MLflow and Kubeflow are two of the most widely used open-source tools — but they solve different problems. Choosing the right one (or sometimes both) depends on your team’s maturity, infrastructure, and priorities.

This blog dives deep into how MLflow and Kubeflow differ, where each shines (and where it struggles), and provides guidance for enterprise teams making this trade-off — supported by real adoption data and user survey insights.

1. Core Philosophy: Tracking vs Orchestration

At its heart, MLflow is a highly modular platform focused on experiment tracking, model versioning, model registry, and deployment. It was designed to be lightweight, infrastructure-agnostic, and easy to set up. As GeeksforGeeks explains, MLflow works across any environment — local, cloud, or hybrid — and is ideal for managing metrics, artifacts, and reproducible runs.

Kubeflow, on the other hand, is built for full-scale, Kubernetes-native orchestration. It provides deep integration for training, pipelines, hyperparameter tuning (via Katib), and serving. As noted by AI Ops School, Kubeflow is more complex to install, but excels in scalable, production-grade workflows.

In simple terms:

• MLflow = experiment-centric + model management
  
• Kubeflow = workflow-centric + scalable orchestration on Kubernetes
  

2. Adoption & Scale: What the Numbers Say

Enterprise usage data provides real insight into how these tools are used in production:

• According to the Kubeflow User Survey 2023, 84% of users deploy more than one Kubeflow component, and 49% are running Kubeflow in production. Kubeflow The top-used component was Pipelines (90%), highlighting how critical orchestration is for Kubeflow users. Kubeflow
  
• From a broader MLOps landscape perspective, a 2025 review by the Artificial Intelligence Review showed Kubeflow scoring high in orchestration, distributed training, and model inference, while MLflow led in experiment tracking and metadata storage. KITopen
  
• In the CNCF Tech Radar (Q3 2024), Kubeflow was rated highly among batch/AI compute technologies, indicating strong recognition in cloud-native MLOps. CNCF
  

These data points suggest that Kubeflow is often chosen for more mature, scalable workloads, whereas MLflow remains hugely popular for experimentation and model lifecycle management.

3. Feature Comparison: Side by Side

Here’s a feature-wise comparison to highlight how MLflow and Kubeflow diverge and overlap:

Capability	MLflow	Kubeflow
Experiment Tracking	Very strong — sleek UI, Python API, metrics, artifacts	Yes (via Kubeflow Pipelines + Metadata), but more infrastructure required
Model Registry	Built-in registry with stages (staging, production)	Less mature registry; often integrated with other tools like MLflow or seldon
Workflow Orchestration	Limited; supports simple templates with MLflow 2.x	Full DAG orchestration, parallelism, scheduling, retries
Hyperparameter Tuning	No built-in tuning; users typically run custom loops	Katib for automated, scalable tuning
Serving / Deployment	Can package and serve via MLflow Models (e.g. Docker, REST)	Kubernetes-native serving (KFServing, KServe) or TF Serving
Scalability	Lightweight; easy to run on a VM or managed service	Designed for Kubernetes – scales horizontally with cluster resources
Infrastructure Overhead	Low — pip install mlflow, minimal setup	High — requires K8s expertise, setup complexity, Helm or manifests

4. Pros, Cons & Technical Trade-offs

MLflow Pros:

• Quick to get started — minimal infrastructure
  
• Extremely flexible and agnostic—works with any ML library or deployment environment
  
• Great for experiment tracking, reproducibility, and model registry.
  

MLflow Cons:

• Not built for orchestration — its pipeline support is limited
  
• At large scale, metadata tracking or serving may require additional tooling or infrastructure.
  

Kubeflow Pros:

• Full orchestration support: pipelines, tuning, serving, and more
  
• Kubernetes-native: scalable, portable, and integrates with cloud-native infrastructure.
  
• Excellent for production-grade workflows and large, distributed ML training.
  

Kubeflow Cons:

• High setup and operational complexity — needs Kubernetes experts
  
• More resource-intensive (clusters, storage, permissions).
  
• Some users report documentation and upgrade challenges. In the Kubeflow 2023 user survey, 55% cited documentation as a pain point. Kubeflow
  

5. Real-World Use Cases & Architectures

Use Case 1: Early-Stage Experimentation

• Teams building models in notebooks or doing hyperparameter research often use MLflow for tracking experiments, logging metrics, and comparing runs.
  
• Their infrastructure may be a single VM, a small server, or a shared code repo.
  

Use Case 2: Enterprise-Scale Production MLOps

• A large company running Kubernetes clusters on GCP, AWS, or Azure may use Kubeflow to:
  
  • Orchestrate pipelines with multiple steps (ingestion → feature store → training → tuning → validation → deployment)
    
  • Perform distributed training jobs using GPU nodes
    
  • Serve models using KServe or TF Serving
    
  • Track lineage and metadata via Kubeflow Metadata and Pipeline artifacts
    

Use Case 3: Hybrid Approach (“Best of Both Worlds”)

• Many enterprise teams combine MLflow + Kubeflow:
  
  • Use MLflow for experiment tracking and model registry
    
  • Use Kubeflow for pipeline orchestration and distributed training
    
  • This hybrid pattern is supported in community forums and used by practitioners
    

6. Strategic Decision Guide: Which One to Use (or When to Use Both)

Here’s a simple decision framework for enterprise MLOps teams:

• If you are just starting out or doing rapid prototyping, go for MLflow — low overhead, high agility, and great visibility for data scientists.
  
• If you have Kubernetes maturity, need scalable orchestration, and want full pipeline automation, choose Kubeflow — ideal for production systems.
  
• If you want both:
  
  • Use MLflow for experiment tracking and registry
    
  • Use Kubeflow for orchestration and production pipelines
    
  • That hybrid approach gives you best-in-class capabilities without reinventing either tool
    

Ask yourself:

1. Do we have Kubernetes expertise in-house?
   
2. How complex are our ML workflows?
   
3. What scale of model training / retraining do we run?
   
4. Does reproducibility or orchestration matter more for our short-term roadmap?
   
5. How important is portability across cloud or on-prem?
   

7. Future Outlook & Trends

• With MLflow 3.0, community feedback suggests the platform is shifting toward supporting GenAI workloads, adding more robust metadata and model lineage capabilities
  
• Kubeflow 1.10 and later have improved IAM, multi-tenant notebooks, and pipeline stability — making it increasingly attractive for regulated industries.
• According to the CNCF Tech Radar (2024), Kubeflow continues to be a leading choice for batch / AI compute, especially in Kubernetes-first environments
  

8. Conclusion: Not a One-Size-Fits-All

Ultimately, MLflow and Kubeflow serve different but complementary use cases in an MLOps architecture:

• MLflow is your lightweight, flexible, experiment-focused tool — perfect for small teams, rapid prototyping, and model registry.
  
• Kubeflow is your full-scale orchestration framework — ideal for enterprise-scale pipelines, Kubernetes-native workloads, and production-grade MLOps.
  
• A hybrid architecture (MLflow + Kubeflow) often offers the best of both worlds, combining tracking simplicity with orchestration power.
  

By choosing the right tool (or combination), teams can optimize for productivity, cost, reliability, and scale — turning ML experimentation into sustainable, governed, production-ready systems.