Cybersecurity · head to head
Authelia vs MLflow
Authelia
Cybersecurity
Open-source authentication and two-factor portal for reverse proxies
- 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: Authelia depends on a reverse proxy: it is not a standalone identity provider; MLflow requires infrastructure setup: teams must manage MLflow tracking server, database, and artifact storage themselves
- They diverge on capability: Authelia covers Reverse proxy integration, MLflow covers Experiment tracking.
- Prices and features above were last checked on 30 August 2026.
Where they differ
Only the attributes on which Authelia 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 Authelia
- Reverse proxy integration
- Two-factor authentication
- Access control rules
- Lightweight backends
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.
Authelia
- Putting a login and 2FA in front of self-hosted services that have nonenot MLflow
- Adding SSO across a small set of internal tools without a full identity platformnot MLflow
- Home and small-team infrastructure behind a single reverse proxynot MLflow
MLflow
- Machine learningnot Authelia
- Data analysisnot Authelia
- Model trainingnot Authelia
- Predictive analyticsnot Authelia
Where each one falls short
Documented limitations, not opinions. Every one is a constraint you would hit in normal use.
Authelia
- Depends on a reverse proxy: it is not a standalone identity provider
- Configuration is YAML-first with no administrative interface, so changes mean editing files
- Not a full IAM: user management, provisioning and federation are limited compared with Keycloak
- Scales poorly as an organisation-wide identity solution, which is not its target
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
Authelia
Free- AutheliaFree
- Full functionality
- No usage limits
- Community support
MLflow
Free- Open SourceFree
- Experiment tracking
- Model registry
- Deployment tools
Which should you pick?
Choose Authelia if
- You need reverse proxy integration.
- You want to start without paying.
- You work on Docker, Kubernetes, Linux, Self-hosted.
- You also want two-factor authentication.
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 Authelia or MLflow better?
- Neither clearly leads. Authelia 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, Authelia or MLflow?
- Authelia starts at Free and MLflow at Free.
- Does Authelia or MLflow run on more platforms?
- Authelia runs on Docker, Kubernetes, Linux, Self-hosted. MLflow runs on Web, Python API, REST API.
- Can I use Authelia for free?
- Both have a free tier, so you can try either at no cost before committing.
- What is Authelia best used for?
- Authelia is most often used for putting a login and 2fa in front of self-hosted services that have none, adding sso across a small set of internal tools without a full identity platform, home and small-team infrastructure behind a single reverse proxy. Of those, putting a login and 2fa in front of self-hosted services that have none and adding sso across a small set of internal tools without a full identity platform are not what MLflow is typically brought in for.
- What can Authelia do that MLflow cannot?
- Authelia covers Reverse proxy integration, Two-factor authentication, Access control rules, Lightweight backends. MLflow covers Experiment tracking, Model registry, Model packaging, Deployment.
Answered from the vendors’ own pages
Authelia: Is Authelia free?
Yes, open source with no licence fee.
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.
SourceAuthelia: Does Authelia need a reverse proxy?
Yes. It integrates through forward authentication with Nginx, Traefik, Caddy or HAProxy rather than sitting in front of traffic itself.
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.
SourceAuthelia: Authelia or Keycloak?
Authelia is far lighter and aimed at protecting self-hosted services behind a proxy. Keycloak is a full identity and access management platform, and much more to run.
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 Dataiku
- MLflow vs Palantir Foundry
- MLflow vs Pinecone
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