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AI · head to head

CoreWeave vs Seldon

CoreWeave logo

CoreWeave

AI

Specialized cloud for GPU compute

From
$0.35/per-hour
Free plan
No
Full reviewWebsite
Seldon logo

Seldon

Machine Learning

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

From
Free
Free plan
Yes
Full reviewWebsite

The short version

  • Only Seldon has a free tier, so it costs nothing to try first.
  • Each has a real cost: CoreWeave gPU nodes are sold as full 8 GPU instances rather than single cards, so the entry cost for an H100 node is $49.24 an hour on demand; 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: CoreWeave covers NVIDIA H100/A100, Seldon covers Kubernetes custom resources.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which CoreWeave and Seldon actually diverge.

Attributes where CoreWeave and Seldon differ
AttributeCoreWeaveSeldon
Starting price$0.35/per-hourFree
Pricing modelusage-basedfreemium
Free tierNoYes
PlatformsCloudLinux
CategoryAIMachine Learning
Founded20172014

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 CoreWeave

  • NVIDIA H100/A100
  • Kubernetes native
  • High bandwidth
  • Object storage
  • Kubernetes
  • Terraform
  • Cloud APIs
  • Cloud support

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.

CoreWeave

  • Renting GPU compute for model training and inferencenot Seldon
  • Running large scale AI workloads without buying hardwarenot Seldon

Seldon

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

Where each one falls short

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

CoreWeave

  • GPU nodes are sold as full 8 GPU instances rather than single cards, so the entry cost for an H100 node is $49.24 an hour on demand
  • Spot pricing is roughly 40% of on demand, at $19.71 an hour for the same H100 node, so predictable capacity carries a large premium
  • The newest hardware carries no published price and requires contacting sales
  • Discounts of up to 60% require committed usage agreements negotiated with sales
  • Only the GH200 is offered as a single GPU instance

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

CoreWeave

$0.35/per-hour
  • Standard$0.35/per-hour
    • Various GPU types
    • Kubernetes
  • EnterpriseFree
    • Dedicated clusters
    • Custom solutions

Seldon

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

Which should you pick?

Choose CoreWeave if

  • You need nvidia h100/a100.
  • You work on Cloud.
  • You also want kubernetes native.
CoreWeave review

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

Questions people ask

Is CoreWeave or Seldon better?
Neither clearly leads. CoreWeave starts at $0.35/per-hour and Seldon at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, CoreWeave or Seldon?
Seldon has a free tier; the other does not. Paid plans start at $0.35/per-hour for CoreWeave and Free for Seldon.
Does CoreWeave or Seldon run on more platforms?
CoreWeave runs on Cloud. Seldon runs on Linux.
Can I use Seldon for free?
Yes. Seldon has a free tier, so you can try it without paying. CoreWeave starts at $0.35/per-hour.
What is CoreWeave best used for?
CoreWeave is most often used for renting gpu compute for model training and inference, running large scale ai workloads without buying hardware. Of those, renting gpu compute for model training and inference and running large scale ai workloads without buying hardware are not what Seldon is typically brought in for.
What can CoreWeave do that Seldon cannot?
CoreWeave covers NVIDIA H100/A100, Kubernetes native, High bandwidth, Object storage. Seldon covers Kubernetes custom resources, Inference graphs, Traffic strategies, Open Inference Protocol.

Answered from the vendors’ own pages

CoreWeave: How much does CoreWeave cost?

CoreWeave does not publish pricing on its website. The company uses a quote-based pricing model and directs customers to contact their sales team directly to discuss pricing options and customized solutions.

Source
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.

CoreWeave: How can I get a quote from CoreWeave?

To obtain CoreWeave pricing, you must contact their sales team directly through the Contact Us option on their website. They will provide a customized quote based on your specific compute and infrastructure requirements.

Source
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.

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.

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.

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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