Machine Learning & Data Science · head to head
MLflow vs Seldon
MLflow
Machine Learning & Data Science
Open source platform for managing the ML lifecycle
- From
- Free
- Rated
- -
Seldon
Machine Learning & Data Science
Deploy, scale, and monitor machine learning models
- From
- Free
- Rated
- -
The short version
- Each has a real cost: MLflow requires infrastructure setup: teams must manage MLflow tracking server, database, and artifact storage themselves; Seldon production deployment requires a Kubernetes cluster, whether managed such as GKE, EKS or AKS, or on-premises such as OpenShift
- They diverge on capability: MLflow covers Experiment tracking, Seldon covers Model serving.
Where they differ
Only the attributes on which MLflow and Seldon actually diverge.
Identical on both: starting price (Free), free tier (Yes), user rating (Not yet rated), category (Machine Learning & Data Science).
What each one covers
Drawn from each product's published feature list. An absence here means we hold no record of it - not that the product lacks it.
Only in MLflow
- Experiment tracking
- Model registry
- Model packaging
- Deployment
- Project organization
- TensorFlow
- PyTorch
- scikit-learn
Only in Seldon
- Model serving
- A/B testing
- Canary deployments
- Outlier detection
- Model explainability
- Istio
- Prometheus
- Grafana
Both cover
- Kubernetes
- Linux support
What people use each for
The jobs each tool is most often brought in to do.
MLflow
- Machine learningnot Seldon
- Data analysisnot Seldon
- Model trainingnot Seldon
- Predictive analyticsnot Seldon
Seldon
- Serving and routing machine learning models on Kubernetesnot MLflow
- Building multi-step inference pipelines with A/B tests and explainersnot MLflow
Where each one falls short
Documented limitations, not opinions. Every one is a constraint you would hit in normal use.
MLflow
- Requires infrastructure setup: teams must manage MLflow tracking server, database, and artifact storage themselves
- Basic UI and visualization: lacks rich interactive dashboards and real-time monitoring compared to commercial platforms
- Limited collaboration: no built-in role-based access control or multi-user management features
- Production monitoring gaps: drift detection, explainability, and alerting require separate dedicated tools
Seldon
- Production deployment requires a Kubernetes cluster, whether managed such as GKE, EKS or AKS, or on-premises such as OpenShift
- The documented components carry both minimum and maximum supported versions, so newer Kubernetes and dependency versions are not automatically supported
- Dataflow Pipelines need an additional component that the docs recommend avoiding installing when pipelines are not used
- The Docker Compose install is offered as a lightweight alternative for environments without Kubernetes rather than as a production path
Pricing, plan by plan
MLflow
Free- Open SourceFree
- Experiment tracking
- Model registry
- Deployment tools
Seldon
Free- Seldon CoreFree
- Open source
- Kubernetes deployment
- Model serving
- Seldon DeployFree
- Enterprise features
- GUI
- Monitoring
Which should you pick?
Choose MLflow if
- You need experiment tracking.
- You want to start without paying.
- You work on Web, Python API, REST API.
- You also want model registry.
Choose Seldon if
- You need model serving.
- You want to start without paying.
- You work on Linux.
- You also want a/b testing.
Questions people ask
- Is MLflow or Seldon better?
- Neither clearly leads. MLflow starts at Free and Seldon at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, MLflow or Seldon?
- MLflow starts at Free and Seldon at Free.
- Does MLflow or Seldon run on more platforms?
- MLflow runs on Web, Python API, REST API. Seldon runs on Linux.
- Can I use MLflow for free?
- Both have a free tier, so you can try either at no cost before committing.
- What is MLflow best used for?
- MLflow is most often used for machine learning, data analysis, model training, predictive analytics. Of those, machine learning and data analysis are not what Seldon is typically brought in for.
- What can MLflow do that Seldon cannot?
- MLflow covers Experiment tracking, Model registry, Model packaging, Deployment. Seldon covers Model serving, A/B testing, Canary deployments, Outlier detection. Both handle Kubernetes, Linux support.
Answered from the vendors’ own pages
MLflow: Is MLflow free to use?
Yes, MLflow is completely open-source and free. However, teams typically incur infrastructure costs for hosting and maintaining the MLflow tracking server. Databricks offers Managed MLflow as a commercial option for cloud deployment.
SourceMLflow: Can MLflow track experiments for different ML frameworks?
Yes, MLflow is framework-agnostic and works with TensorFlow, PyTorch, scikit-learn, XGBoost, and any other ML framework. This flexibility is a core design principle allowing teams to use diverse tools.
SourceMLflow: Does MLflow include a model registry?
Yes, MLflow Model Registry (added in 2018) provides a central model store with versioning, stage transitions, and deployment tracking. This enables production model governance and lineage tracking.
SourceMLflow: What are MLflow's main limitations?
MLflow requires significant infrastructure setup and maintenance. The UI is basic compared to commercial tools, collaboration is limited without third-party RBAC solutions, and production monitoring requires separate tools for drift detection and alerting.
SourceMLflow: Can MLflow handle LLM and agent tracing?
MLflow added LLM and agent tracing capabilities in recent versions, though the native support is limited compared to specialized LLM observability platforms that replaced weak LLM tracing.
Source