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Machine Learning · head to head

BentoML vs Stata

BentoML logo

BentoML

Machine Learning

Open source Python framework that packages models into deployable inference services

From
Free
Rated
-
Full reviewWebsite
Stata logo

Stata

Machine Learning

Data science software for research professionals

From
$48/year
Rated
-
Full reviewWebsite

The short version

  • Only BentoML has a free tier, so it costs nothing to try first.
  • Each has a real cost: 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.; Stata the entry Stata/BE edition is capped at 2,048 variables and 798 independent variables in a model
  • They diverge on capability: BentoML covers Bento packaging format, Stata covers Statistical analysis.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which BentoML and Stata actually diverge.

Attributes where BentoML and Stata differ
AttributeBentoMLStata
Starting priceFree$48/year
Pricing modelfreemiumsubscription
Free tierYesNo
Founded20191985

Identical on both: platforms (Linux, Mac, Windows), user rating (Not yet rated), category (Machine Learning).

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 BentoML

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

Only in Stata

  • Statistical analysis
  • Data management
  • Graphics
  • Econometrics
  • Survey analysis
  • Python
  • ODBC
  • Excel

What people use each for

The jobs each tool is most often brought in to do.

BentoML

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

Stata

  • Statistical analysis and data analysisnot BentoML
  • Econometric modelingnot BentoML
  • Biostatistics and epidemiologynot BentoML
  • Academic and research data analysisnot BentoML

Where each one falls short

Documented limitations, not opinions. Every one is a constraint you would hit in normal use.

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.

Stata

  • The entry Stata/BE edition is capped at 2,048 variables and 798 independent variables in a model
  • Raising the variable limit to 32,767 requires Stata/SE and 120,000 requires Stata/MP
  • Stata/MP is licensed by core count, so 2 core and 4 core licences are priced separately
  • Student licences require proof of enrolment at a degree granting institution
  • Stata/MP is not sold on a 6 month student term
  • Perpetual student licences cost several times the annual price, for example $298 against $94 for Stata/BE

Pricing, plan by plan

BentoML

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

Stata

$48/year
  • Stata/BE$48/year
    • Basic edition
    • Core features
  • Stata/SE$295/year
    • Standard edition
    • Larger datasets

Which should you pick?

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

Choose Stata if

  • You need statistical analysis.
  • You work on Linux, Mac, Windows.
  • You also want data management.
Stata review

Questions people ask

Is BentoML or Stata better?
Neither clearly leads. BentoML starts at Free and Stata at $48/year, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, BentoML or Stata?
BentoML has a free tier; the other does not. Paid plans start at Free for BentoML and $48/year for Stata.
Does BentoML or Stata run on more platforms?
Both run on Linux, Mac, Windows, so platform support will not decide this one for you.
Can I use BentoML for free?
Yes. BentoML has a free tier, so you can try it without paying. Stata starts at $48/year.
What is BentoML best used for?
BentoML is most often used for standardising how a team ships models, so every service has the same structure, the same health checks and the same build process, serving a model on a gpu where request batching is the difference between one accelerator and several, composing preprocessing, one or more models and postprocessing into a single deployable unit rather than a chain of services, handing a model from a data science group to a platform team as a container image without either side learning the other's tooling. Of those, standardising how a team ships models, so every service has the same structure, the same health checks and the same build process and serving a model on a gpu where request batching is the difference between one accelerator and several are not what Stata is typically brought in for.
What can BentoML do that Stata cannot?
BentoML covers Bento packaging format, Container image build, Adaptive batching, HTTP and gRPC serving. Stata covers Statistical analysis, Data management, Graphics, Econometrics.

Answered from the vendors’ own pages

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.

Stata: How much does Stata cost?

Stata does not publish specific pricing on its website. Customers must use the 'Order Stata' or 'Request a quote' functions to obtain pricing. StataNow is available as a subscription option, but specific monthly or annual costs are not displayed publicly.

Source
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.

Stata: What are the differences between Stata editions?

Stata offers multiple editions including Stata/BE and Stata/MP, with different capabilities and performance characteristics. Edition selection affects pricing, but specific comparisons and costs require requesting a quote.

Source
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.

Stata: Does Stata offer a subscription model?

Yes, StataNow is offered as a subscription option that delivers new features immediately upon release. However, specific pricing for StataNow subscriptions is not published on the website.

Source
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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