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Cybersecurity · head to head

Authelia vs BentoML

Authelia logo

Authelia

Cybersecurity

Open-source authentication and two-factor portal for reverse proxies

From
Free
Rated
-
Full reviewWebsite
BentoML logo

BentoML

Machine Learning

Open source Python framework that packages models into deployable inference services

From
Free
Rated
-
Full reviewWebsite

The short version

  • Each has a real cost: Authelia depends on a reverse proxy: it is not a standalone identity provider; BentoML the service interface was reworked between major versions, with the Runner abstraction of the 1.0 and 1.1 line replaced by the service decorator style in 1.2, so older internal services and the majority of tutorials found through search do not run unmodified against a current install.
  • They diverge on capability: Authelia covers Reverse proxy integration, BentoML covers Bento packaging format.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which Authelia and BentoML actually diverge.

Attributes where Authelia and BentoML differ
AttributeAutheliaBentoML
Pricing modelOpen source, no licence feefreemium
PlatformsDocker, Kubernetes, Linux, Self-hostedLinux, Mac, Windows
CategoryCybersecurityMachine Learning
FoundedUnknown2019

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 BentoML

  • Bento packaging format
  • Container image build
  • Adaptive batching
  • HTTP and gRPC serving
  • Multi-model composition
  • Model store
  • Framework support
  • Managed platform option

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 BentoML
  • Adding SSO across a small set of internal tools without a full identity platformnot BentoML
  • Home and small-team infrastructure behind a single reverse proxynot BentoML

BentoML

  • Standardising how a team ships models, so every service has the same structure, the same health checks and the same build processnot Authelia
  • Serving a model on a GPU where request batching is the difference between one accelerator and severalnot Authelia
  • Composing preprocessing, one or more models and postprocessing into a single deployable unit rather than a chain of servicesnot Authelia
  • Handing a model from a data science group to a platform team as a container image without either side learning the other's toolingnot 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

BentoML

  • The service interface was reworked between major versions, with the Runner abstraction of the 1.0 and 1.1 line replaced by the service decorator style in 1.2, so older internal services and the majority of tutorials found through search do not run unmodified against a current install.
  • It is Python only, so a model that has to be served from Go, Java or C++, or embedded directly inside an existing application process, falls outside what the framework does.
  • The framework is free but inference is not, and an accelerator held by a service receiving one request a minute costs the same as one running flat out, so utilisation is a problem the packaging layer does not solve for you.
  • Self-hosting at scale means Kubernetes, an autoscaler, a container registry and someone who maintains them, so a small team either takes on that operational load or moves to the vendor's managed platform, where the commercial relationship begins.
  • Batch size, worker count and concurrency limits are tuning parameters with real throughput consequences, and getting them wrong appears as tail latency under load rather than as an error, so it needs someone who will actually run a load test before launch.

Pricing, plan by plan

Authelia

Free
  • AutheliaFree
    • Full functionality
    • No usage limits
    • Community support

BentoML

Free
  • Open SourceFree
    • Model packaging
    • API creation
    • Local serving
  • BentoCloudFree
    • Managed deployment
    • Auto-scaling
    • Monitoring

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.
Authelia review

Choose BentoML if

  • You need bento packaging format.
  • You want to start without paying.
  • You work on Linux, Mac, Windows.
  • You also want container image build.
BentoML review

Questions people ask

Is Authelia or BentoML better?
Neither clearly leads. Authelia starts at Free and BentoML at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Authelia or BentoML?
Authelia starts at Free and BentoML at Free.
Does Authelia or BentoML run on more platforms?
Authelia runs on Docker, Kubernetes, Linux, Self-hosted. BentoML runs on Linux, Mac, Windows.
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 BentoML is typically brought in for.
What can Authelia do that BentoML cannot?
Authelia covers Reverse proxy integration, Two-factor authentication, Access control rules, Lightweight backends. BentoML covers Bento packaging format, Container image build, Adaptive batching, HTTP and gRPC serving.

Answered from the vendors’ own pages

Authelia: Is Authelia free?

Yes, open source with no licence fee.

BentoML: Is BentoML free?

The framework is, under Apache 2.0, and you can run it entirely on your own infrastructure. BentoCloud, the managed platform run by the company, is a paid service billed on the compute it runs for you.

Authelia: 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.

BentoML: Do I need Kubernetes?

Not for a single service, which is just a container. You need it once you want autoscaling, multiple models and rolling deployments on your own infrastructure, which is the point at which the managed option starts to look attractive.

Authelia: 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.

BentoML: How is this different from just writing a FastAPI app?

For one model it is not very different and FastAPI is simpler. The difference is at four or ten models, where you would otherwise be maintaining ten sets of the same Dockerfile, batching logic, dependency pinning and health check code.

BentoML: Can it serve large language models?

Yes, and the project publishes tooling aimed at that specifically, but the constraints are the usual ones: accelerator memory, batching strategy and the cost of holding a GPU that is idle between requests.

BentoML: What actually is a Bento?

A directory, versioned and archivable, containing your service code, the model files it needs, the exact Python dependencies and instructions for running it. It is the unit you build into an image and deploy.

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