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Machine Learning · head to head

Cohere vs Seldon

Cohere logo

Cohere

Machine Learning

Enterprise AI platform for NLP

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: Cohere aPI-only service with no self-hosted options for most users; 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: Cohere covers Generate, Seldon covers Kubernetes custom resources.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which Cohere and Seldon actually diverge.

Attributes where Cohere and Seldon differ
AttributeCohereSeldon
Pricing modelusage-basedfreemium
PlatformsApi, CloudLinux
Founded20192014

Identical on both: starting price (Free), 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 Cohere

  • Generate
  • Embed
  • Rerank
  • Classify
  • REST API
  • SDKs
  • Cloud deployment
  • Api 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.

Cohere

  • ai tools managementnot Seldon
  • Workflow automationnot Seldon
  • Reportingnot Seldon

Seldon

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

Where each one falls short

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

Cohere

  • API-only service with no self-hosted options for most users
  • Trial tier severely limited at 1,000 calls per month
  • Smaller context window compared to some competing APIs
  • Less emphasis on safety and alignment compared to competing APIs

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

Cohere

Free
  • Free TrialFree
    • Rate limited
    • Evaluation
  • Production$0.4/per-million-tokens
    • Full access
    • SLA

Seldon

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

Which should you pick?

Choose Cohere if

  • You need generate.
  • You want to start without paying.
  • You work on Api, Cloud.
  • You also want embed.
Cohere 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 Cohere or Seldon better?
Neither clearly leads. Cohere 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, Cohere or Seldon?
Cohere starts at Free and Seldon at Free.
Does Cohere or Seldon run on more platforms?
Cohere runs on Api, Cloud. Seldon runs on Linux.
Can I use Cohere for free?
Both have a free tier, so you can try either at no cost before committing.
What is Cohere best used for?
Cohere is most often used for ai tools management, workflow automation, reporting. Of those, ai tools management and workflow automation are not what Seldon is typically brought in for.
What can Cohere do that Seldon cannot?
Cohere covers Generate, Embed, Rerank, Classify. Seldon covers Kubernetes custom resources, Inference graphs, Traffic strategies, Open Inference Protocol.

Answered from the vendors’ own pages

Cohere: Does Cohere offer a free tier?

Yes. Cohere provides Trial API keys that allow 1,000 free API calls per month across all models and endpoints. Trial keys are rate-limited to 20 requests per minute for Chat endpoints and 5-10 requests per minute for other endpoints, and cannot be used for production or commercial purposes.

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.

Cohere: What is the cost structure for production use?

Cohere uses pay-as-you-go pricing based on tokens consumed. Costs vary by model: Command costs from 0.15 to 2.50 USD per 1M input tokens, with output tokens priced higher. Embed models cost 0.10 USD per 1M input tokens. Production keys have monthly billing with invoices at month-end or when charges reach 250 USD.

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.

Cohere: Can I self-host Cohere models?

No. Cohere operates as an API-only platform. However, enterprise customers can arrange dedicated or managed deployments through the Model Vault platform starting at 4.00 USD per hour with custom pricing for dedicated instances.

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.

Cohere: What are the main differences between Cohere and Claude API?

Cohere excels in cost-effective NLP applications and retrieval-augmented generation (RAG) capabilities. Claude API emphasizes reasoning and safety with Constitutional AI training. Cohere's Command R+ offers similar performance to GPT-4 at 40-50 percent lower cost, while Claude focuses on factual accuracy and transparency.

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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