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Machine Learning · head to head

BentoML vs Sourcegraph Cody

BentoML logo

BentoML

Machine Learning

Open source Python framework that packages models into deployable inference services

From
Free
Free plan
Yes
Full reviewWebsite
Sourcegraph Cody logo

Sourcegraph Cody

AI

Enterprise AI coding assistant with whole-codebase context

From
$59/month
Free plan
No
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.; Sourcegraph Cody no longer available to individual developers or small teams after free and Pro tiers were discontinued in mid-2025
  • They diverge on capability: BentoML covers Bento packaging format, Sourcegraph Cody covers Multi-repository context.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which BentoML and Sourcegraph Cody actually diverge.

Attributes where BentoML and Sourcegraph Cody differ
AttributeBentoMLSourcegraph Cody
Starting priceFree$59/month
Pricing modelfreemiumquote
Free tierYesNo
PlatformsLinux, Mac, Windowsweb, windows, mac, linux
CategoryMachine LearningAI
Founded20192013

Identical on both: 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 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 Sourcegraph Cody

  • Multi-repository context
  • Large context window
  • Context filters
  • Model flexibility
  • IDE integration

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 Sourcegraph Cody
  • Serving a model on a GPU where request batching is the difference between one accelerator and severalnot Sourcegraph Cody
  • Composing preprocessing, one or more models and postprocessing into a single deployable unit rather than a chain of servicesnot Sourcegraph Cody
  • Handing a model from a data science group to a platform team as a container image without either side learning the other's toolingnot Sourcegraph Cody

Sourcegraph Cody

  • Cross-repository code understanding for large engineering orgsnot BentoML
  • AI-assisted code review and refactoring at enterprise scalenot BentoML
  • Enforcing data-boundary controls when using third-party LLMsnot BentoML
  • Codebase-aware chat and autocomplete for developersnot 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.

Sourcegraph Cody

  • No longer available to individual developers or small teams after free and Pro tiers were discontinued in mid-2025
  • Priced substantially higher than many competing AI coding assistants, with Enterprise plans typically requiring annual contracts
  • Sourcegraph has shifted its individual-developer focus to a separate product, Amp, creating uncertainty about Cody's long-term product priority
  • Requires integration with Sourcegraph's broader code intelligence platform rather than working as a lightweight standalone extension

Pricing, plan by plan

BentoML

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

Sourcegraph Cody

$59/month
  • Enterprise$59/month
    • Whole-codebase AI context
    • Context filters for sensitive code
    • Multi-repository analysis

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 Sourcegraph Cody if

  • You need multi-repository context.
  • You work on web, windows, mac, linux.
  • You also want large context window.
Sourcegraph Cody review

Questions people ask

Is BentoML or Sourcegraph Cody better?
Neither clearly leads. BentoML starts at Free and Sourcegraph Cody at $59/month, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, BentoML or Sourcegraph Cody?
BentoML has a free tier; the other does not. Paid plans start at Free for BentoML and $59/month for Sourcegraph Cody.
Does BentoML or Sourcegraph Cody run on more platforms?
BentoML runs on Linux, Mac, Windows. Sourcegraph Cody runs on web, windows, mac, linux.
Can I use BentoML for free?
Yes. BentoML has a free tier, so you can try it without paying. Sourcegraph Cody starts at $59/month.
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 Sourcegraph Cody is typically brought in for.
What can BentoML do that Sourcegraph Cody cannot?
BentoML covers Bento packaging format, Container image build, Adaptive batching, HTTP and gRPC serving. Sourcegraph Cody covers Multi-repository context, Large context window, Context filters, Model flexibility.

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.

Sourcegraph Cody: Is Sourcegraph Cody still available to individual developers?

No, as of mid-2025 Cody Free and Pro plans were discontinued; Cody is now sold only as part of Sourcegraph's Enterprise plan, which requires an annual contract.

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.

Sourcegraph Cody: How much does Sourcegraph Enterprise cost?

Enterprise plans start around $16,000 and scale with team size and seat count, including bundled AI credits; exact pricing requires a sales 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.

Sourcegraph Cody: How does the AI credit system work on Enterprise plans?

Each plan includes a pooled allocation of AI credits shared across the organization; credits do not expire and roll over at renewal, with volume credit add-ons available.

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.

Sourcegraph Cody: What happens if my organization exceeds its credit allocation?

Sourcegraph's pricing page addresses overage and offers additional volume credit buckets for teams that need more than their committed allocation.

Source
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.

Sourcegraph Cody: Is a free trial available for Sourcegraph Enterprise?

Sourcegraph's pricing page lists free trial availability as one of its published FAQ topics for prospective Enterprise customers.

Source
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