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Machine Learning · head to head

Ollama vs Seldon

Ollama logo

Ollama

Machine Learning

Open-source tool for running LLMs locally on desktop and servers

From
Free
Rated
-
Full reviewWebsite
Seldon logo

Seldon

Machine Learning

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

From
Free
Rated
-
Full reviewWebsite

The short version

  • Each has a real cost: Ollama requires user to provide computational hardware; no free cloud compute; models may not fit in available RAM on typical machines; 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.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which Ollama and Seldon actually diverge.

Attributes where Ollama and Seldon differ
AttributeOllamaSeldon
PlatformsmacOS, Windows, Linux, Cloud (AWS, Google Cloud, Azure, self-hosted)Linux
FoundedUnknown2014

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 Ollama

Nothing recorded that Seldon does not also cover.

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.

Ollama

  • Local development and testing without API costs or rate limitsnot Seldon
  • Privacy-sensitive applications requiring data to remain on-devicenot Seldon
  • Cost-sensitive deployments where computational resources are already availablenot Seldon
  • Fully offline environments or air-gapped networksnot Seldon

Seldon

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

Where each one falls short

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

Ollama

  • Requires user to provide computational hardware; no free cloud compute; models may not fit in available RAM on typical machines
  • No hosted service option for inference; all computational burden falls to user
  • Limited to open-weight models; cannot run proprietary models like GPT-4 or Claude locally
  • Performance depends entirely on user's hardware; no SLAs or guarantees on speed

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

Ollama

Free
  • FreeFree
    • CLI, API, desktop apps
    • Unlimited public models
    • 40,000+ community integrations
  • Pro$20/month
    • Access to larger, more powerful cloud models
    • Run 3 concurrent cloud models
    • 50x more usage than Free
  • Max$100/month
    • Run 10 concurrent cloud models
    • 5x more usage than Pro
  • Team$25/month
    • Per seat pricing (5-seat minimum = $125/month)
    • Shared billing
    • Zero data retention

Seldon

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

Which should you pick?

Choose Ollama if

  • You want to start without paying.
  • You work on macOS, Windows, Linux, Cloud (AWS, Google Cloud, Azure, self-hosted).
Ollama 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 Ollama or Seldon better?
Neither clearly leads. Ollama 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, Ollama or Seldon?
Ollama starts at Free and Seldon at Free.
Does Ollama or Seldon run on more platforms?
Ollama runs on macOS, Windows, Linux, Cloud (AWS, Google Cloud, Azure, self-hosted). Seldon runs on Linux.
Can I use Ollama for free?
Both have a free tier, so you can try either at no cost before committing.
What is Ollama best used for?
Ollama is most often used for local development and testing without api costs or rate limits, privacy-sensitive applications requiring data to remain on-device, cost-sensitive deployments where computational resources are already available, fully offline environments or air-gapped networks. Of those, local development and testing without api costs or rate limits and privacy-sensitive applications requiring data to remain on-device are not what Seldon is typically brought in for.
What can Ollama do that Seldon cannot?
Seldon covers Kubernetes custom resources, Inference graphs, Traffic strategies, Open Inference Protocol.

Answered from the vendors’ own pages

Ollama: How much does Ollama cost?

Ollama is free to use with unlimited public models. Pro paid plans start at $20/month for 3 concurrent cloud models, or $100/month for Max with 10 concurrent models. Team plans cost $25/seat/month with a 5-seat minimum.

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.

Ollama: What does the Ollama free tier include?

The free tier includes CLI and API access, unlimited public models, 40,000+ community integrations, and private data retention, though limited to 1 concurrent cloud model.

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.

Ollama: How much usage is included with each Ollama plan?

Pro includes 50x more usage than Free, and Max includes 5x more usage than Pro. Session limits reset every 5 hours and weekly limits reset every 7 days across all tiers.

Source
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.

Ollama: Does Ollama log or train on user data?

No, Ollama explicitly states that prompt or response data is never logged or trained on, protecting user privacy across all plans.

Source
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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