Cybersecurity · head to head
authentik vs MLflow
authentik
Cybersecurity
Open-source identity provider with flexible authentication flows
- From
- Free
- Rated
- -
MLflow
Machine Learning
Open source platform for managing the ML lifecycle
- From
- Free
- Rated
- -
The short version
- Each has a real cost: authentik smaller project than Keycloak, with a correspondingly smaller community and fewer integration guides; MLflow requires infrastructure setup: teams must manage MLflow tracking server, database, and artifact storage themselves
- They diverge on capability: authentik covers Configurable flows, MLflow covers Experiment tracking.
- Prices and features above were last checked on 30 August 2026.
Where they differ
Only the attributes on which authentik and MLflow actually diverge.
Identical on both: starting price (Free), free tier (Yes), user rating (Not yet rated).
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 authentik
- Configurable flows
- Protocol support
- Application proxy
- Modern admin interface
Only in MLflow
- Experiment tracking
- Model registry
- Model packaging
- Deployment
- Project organization
- TensorFlow
- PyTorch
- scikit-learn
What people use each for
The jobs each tool is most often brought in to do.
authentik
- Self-hosted SSO across internal services without commercial identity pricingnot MLflow
- Putting authentication in front of applications that have none, via the proxynot MLflow
- Teams who tried Keycloak and wanted something less heavynot MLflow
MLflow
- Machine learningnot authentik
- Data analysisnot authentik
- Model trainingnot authentik
- Predictive analyticsnot authentik
Where each one falls short
Documented limitations, not opinions. Every one is a constraint you would hit in normal use.
authentik
- Smaller project than Keycloak, with a correspondingly smaller community and fewer integration guides
- The flow model is flexible but conceptually unfamiliar, and simple setups can feel over-abstracted
- Enterprise support and some governance features sit behind the paid tier
- Self-hosted identity is still yours to secure, patch and keep available
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
Pricing, plan by plan
authentik
Free- Open sourceFree
- Full identity provider
- All protocols
- Community support
MLflow
Free- Open SourceFree
- Experiment tracking
- Model registry
- Deployment tools
Which should you pick?
Choose authentik if
- You need configurable flows.
- You want to start without paying.
- You work on Docker, Kubernetes, Linux, Self-hosted.
- You also want protocol support.
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.
Questions people ask
- Is authentik or MLflow better?
- Neither clearly leads. authentik starts at Free and MLflow at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, authentik or MLflow?
- authentik starts at Free and MLflow at Free.
- Does authentik or MLflow run on more platforms?
- authentik runs on Docker, Kubernetes, Linux, Self-hosted. MLflow runs on Web, Python API, REST API.
- Can I use authentik for free?
- Both have a free tier, so you can try either at no cost before committing.
- What is authentik best used for?
- authentik is most often used for self-hosted sso across internal services without commercial identity pricing, putting authentication in front of applications that have none, via the proxy, teams who tried keycloak and wanted something less heavy. Of those, self-hosted sso across internal services without commercial identity pricing and putting authentication in front of applications that have none, via the proxy are not what MLflow is typically brought in for.
- What can authentik do that MLflow cannot?
- authentik covers Configurable flows, Protocol support, Application proxy, Modern admin interface. MLflow covers Experiment tracking, Model registry, Model packaging, Deployment.
Answered from the vendors’ own pages
authentik: Is authentik free?
The open-source edition is free and complete for most use. An enterprise tier adds support and additional features.
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.
Sourceauthentik: authentik or Keycloak?
authentik is generally reported as easier to run and administer; Keycloak is more established with a larger community and Red Hat behind it.
MLflow: 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.
Sourceauthentik: Can authentik protect apps with no login of their own?
Yes. Its application proxy places authentication in front of services that have no built-in authentication.
MLflow: 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.
SourceRelated pages
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- MLflow vs scikit-learn
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