Machine Learning · head to head
BentoML vs Seldon
BentoML
Machine Learning
Open source Python framework that packages models into deployable inference services
- From
- Free
- Rated
- -
Seldon
Machine Learning
Kubernetes model serving whose current version is licensed under the Business Source Licence
- From
- Free
- Rated
- -
The short version
- 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.; Seldon seldon Core v2 is under the Business Source Licence rather than Apache 2.0, so production use requires a commercial agreement, and a team that evaluated it believing it was open source discovers the licence is the blocker exactly when the project is ready to ship.
- They diverge on capability: BentoML covers Bento packaging format, Seldon covers Kubernetes custom resources.
- Prices and features above were last checked on 30 August 2026.
Where they differ
Only the attributes on which BentoML and Seldon actually diverge.
Identical on both: starting price (Free), pricing model (freemium), free tier (Yes), 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 Seldon
- Kubernetes custom resources
- Inference graphs
- Traffic strategies
- Open Inference Protocol
- Alibi Explain
- Alibi Detect
- Kafka-backed pipelines in v2
- Commercial control plane
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 Seldon
- Serving a model on a GPU where request batching is the difference between one accelerator and severalnot Seldon
- Composing preprocessing, one or more models and postprocessing into a single deployable unit rather than a chain of servicesnot Seldon
- Handing a model from a data science group to a platform team as a container image without either side learning the other's toolingnot Seldon
Seldon
- Serving an ensemble or a multi-stage inference path as one versioned deployment rather than as a chain of separate servicesnot BentoML
- Running genuine production experiments where a share of live traffic goes to a candidate model and the results are comparednot BentoML
- Regulated environments needing explanations and drift monitoring attached to the served model rather than bolted on laternot BentoML
- Organisations with an established Kubernetes platform team who want serving expressed as manifests under existing deployment controlsnot 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.
Seldon
- Seldon Core v2 is under the Business Source Licence rather than Apache 2.0, so production use requires a commercial agreement, and a team that evaluated it believing it was open source discovers the licence is the blocker exactly when the project is ready to ship.
- Core v1 remains Apache 2.0 but is in maintenance, so taking the free route means running software that receives no new development while the architecture it belongs to moves on without it.
- Version 2 is a different system rather than a newer release, with different custom resources, a scheduler component and a Kafka-based pipeline model, so migrating from v1 is a re-implementation of every deployment manifest rather than an upgrade.
- Kafka is a dependency for v2 pipelines, so an organisation that does not already operate it takes on a distributed log with its own storage, retention, rebalancing and failure modes purely in order to serve models.
- Everything assumes Kubernetes fluency and the failure modes are Kubernetes failure modes, custom resource version mismatches, an operator that will not reconcile, admission webhooks and resource limits terminating an inference pod mid-request, so it needs a platform engineer rather than a data scientist.
Pricing, plan by plan
BentoML
Free- Open SourceFree
- Model packaging
- API creation
- Local serving
- BentoCloudFree
- Managed deployment
- Auto-scaling
- Monitoring
Seldon
Free- Seldon CoreFree
- Open source
- Kubernetes deployment
- Model serving
- Seldon DeployFree
- Enterprise features
- GUI
- Monitoring
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.
Choose Seldon if
- You need kubernetes custom resources.
- You want to start without paying.
- You work on Linux.
- You also want inference graphs.
Questions people ask
- Is BentoML or Seldon better?
- Neither clearly leads. BentoML starts at Free and Seldon at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, BentoML or Seldon?
- BentoML starts at Free and Seldon at Free.
- Does BentoML or Seldon run on more platforms?
- BentoML runs on Linux, Mac, Windows. Seldon runs on Linux.
- Can I use BentoML for free?
- Both have a free tier, so you can try either at no cost before committing.
- 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 Seldon is typically brought in for.
- What can BentoML do that Seldon cannot?
- BentoML covers Bento packaging format, Container image build, Adaptive batching, HTTP and gRPC serving. Seldon covers Kubernetes custom resources, Inference graphs, Traffic strategies, Open Inference Protocol.
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.
Seldon: Is Seldon open source?
Partly, and this is the thing to check before you build on it. Core v1 is Apache 2.0 but in maintenance. Core v2 was moved to the Business Source Licence in 2024, which allows evaluation but not unlicensed production use. Verify the current licence of each component you intend to run, including MLServer and the Alibi libraries.
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.
Seldon: What is the difference between v1 and v2?
Architecture, not just version number. v2 introduces a scheduler, a different set of custom resources and Kafka-backed pipelines. Manifests, mental model and operations all change, so treat a move as a project.
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.
Seldon: Do I need Kubernetes?
Yes. It is a Kubernetes-native system and there is no meaningful deployment without a cluster and someone competent to run it.
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.
Seldon: What is MLServer?
Seldon's Python inference server implementing the Open Inference Protocol, usable inside Seldon deployments or on its own. Check its current licence alongside Core's, since the company has moved projects onto the Business Source Licence.
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.
Seldon: Do I have to run Kafka?
For v2 pipelines, yes. If you only need single models served, that dependency is a large amount of infrastructure for the benefit, and a simpler serving layer may be the better answer.
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