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Machine Learning · head to head

Seldon vs Skaffold

Seldon logo

Seldon

Machine Learning

Kubernetes model serving whose current version is licensed under the Business Source Licence

From
Free
Rated
-
Full reviewWebsite
Skaffold logo

Skaffold

Software Development

Command-line inner loop for Kubernetes: watch, build, push and deploy on every file save

From
Free
Rated
-
Full reviewWebsite

The short version

  • Each has a real cost: 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.; Skaffold the skaffold.yaml apiVersion changes with releases and old configs stop being accepted, so upgrading is not a no-op; you run skaffold fix and re-test your profiles.
  • They diverge on capability: Seldon covers Kubernetes custom resources, Skaffold covers Dev loop.
  • Prices and features above were last checked on 31 August 2026.

Where they differ

Only the attributes on which Seldon and Skaffold actually diverge.

Attributes where Seldon and Skaffold differ
AttributeSeldonSkaffold
Pricing modelfreemiumOpen source, no licence fee
PlatformsLinuxLinux, macOS, Windows
CategoryMachine LearningSoftware Development
Founded2014Unknown

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 Seldon

  • Kubernetes custom resources
  • Inference graphs
  • Traffic strategies
  • Open Inference Protocol
  • Alibi Explain
  • Alibi Detect
  • Kafka-backed pipelines in v2
  • Commercial control plane

Only in Skaffold

  • Dev loop
  • File sync
  • Pluggable builders
  • Pluggable deployers
  • Profiles
  • Render and apply split
  • Debug mode
  • Automatic port forwarding

What people use each for

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

Seldon

  • Serving an ensemble or a multi-stage inference path as one versioned deployment rather than as a chain of separate servicesnot Skaffold
  • Running genuine production experiments where a share of live traffic goes to a candidate model and the results are comparednot Skaffold
  • Regulated environments needing explanations and drift monitoring attached to the served model rather than bolted on laternot Skaffold
  • Organisations with an established Kubernetes platform team who want serving expressed as manifests under existing deployment controlsnot Skaffold

Skaffold

  • A microservices team whose application cannot be run meaningfully outside a cluster and needs a fast local edit-to-running cyclenot Seldon
  • Standardising build and deploy commands across many repositories so onboarding is one command rather than a wiki pagenot Seldon
  • Producing rendered manifests in CI for a GitOps controller such as Argo CD to applynot Seldon
  • Giving developers on underpowered laptops a remote build path via Cloud Build while keeping the same local workflownot Seldon

Where each one falls short

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

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.

Skaffold

  • The skaffold.yaml apiVersion changes with releases and old configs stop being accepted, so upgrading is not a no-op; you run skaffold fix and re-test your profiles.
  • It is a Google project outside CNCF governance, and the Google Cloud Build and Cloud Deploy integrations are noticeably better developed than anything for AWS or Azure, which shapes the tool toward one cloud.
  • File sync only helps when the runtime picks up changed files without a restart; for compiled languages you are back to a full image rebuild each save and the headline speed advantage largely disappears.
  • It solves the inner loop and stops there. There is no approval workflow, no environment promotion and no audit trail, so you still need a separate delivery tool and now maintain two definitions of how deployment works.
  • Debugging a failing Skaffold run means understanding your builder, your deployer, your cluster and Skaffold's own layering all at once, and its error output often surfaces the underlying tool's message without saying which stage produced it.

Pricing, plan by plan

Seldon

Free
  • Seldon CoreFree
    • Open source
    • Kubernetes deployment
    • Model serving
  • Seldon DeployFree
    • Enterprise features
    • GUI
    • Monitoring

Skaffold

Free
  • SkaffoldFree
    • Apache 2.0 licensed
    • No paid tier or hosted service
    • Support via GitHub issues

Which should you pick?

Choose Seldon if

  • You need kubernetes custom resources.
  • You want to start without paying.
  • You work on Linux.
  • You also want inference graphs.
Seldon review

Choose Skaffold if

  • You need dev loop.
  • You want to start without paying.
  • You work on Linux, macOS, Windows.
  • You also want file sync.
Skaffold review

Questions people ask

Is Seldon or Skaffold better?
Neither clearly leads. Seldon starts at Free and Skaffold at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Seldon or Skaffold?
Seldon starts at Free and Skaffold at Free.
Does Seldon or Skaffold run on more platforms?
Seldon runs on Linux. Skaffold runs on Linux, macOS, Windows.
Can I use Seldon for free?
Both have a free tier, so you can try either at no cost before committing.
What is Seldon best used for?
Seldon is most often used for serving an ensemble or a multi-stage inference path as one versioned deployment rather than as a chain of separate services, running genuine production experiments where a share of live traffic goes to a candidate model and the results are compared, regulated environments needing explanations and drift monitoring attached to the served model rather than bolted on later, organisations with an established kubernetes platform team who want serving expressed as manifests under existing deployment controls. Of those, serving an ensemble or a multi-stage inference path as one versioned deployment rather than as a chain of separate services and running genuine production experiments where a share of live traffic goes to a candidate model and the results are compared are not what Skaffold is typically brought in for.
What can Seldon do that Skaffold cannot?
Seldon covers Kubernetes custom resources, Inference graphs, Traffic strategies, Open Inference Protocol. Skaffold covers Dev loop, File sync, Pluggable builders, Pluggable deployers.

Answered from the vendors’ own pages

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.

Skaffold: Is Skaffold a continuous delivery system?

No. It automates build and deploy for developers and CI, but it has no promotion, approval or rollback governance. Pair it with Argo CD or Cloud Deploy for that.

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.

Skaffold: Does Skaffold require Google Cloud?

No, it works against any Kubernetes cluster including local kind or minikube. Google Cloud integrations are optional but are the best-supported ones.

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.

Skaffold: Is the project still maintained?

Yes. Releases continued through 2026, with v2.24.0 published in July 2026.

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.

Skaffold: Can it deploy Helm charts?

Yes, Helm is a first-class deployer alongside kubectl, Kustomize and kpt, and you can mix builders and deployers in one config.

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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