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Machine Learning · head to head

Groq vs Seldon

Groq logo

Groq

Machine Learning

Fast inference provider using proprietary LPU hardware for low-latency serving

From
On request
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

  • Only Seldon has a free tier, so it costs nothing to try first.
  • Each has a real cost: Groq pricing is not published and is sold entirely by quote, making cost comparison difficult; 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 Groq and Seldon actually diverge.

Attributes where Groq and Seldon differ
AttributeGroqSeldon
Starting priceOn requestFree
Pricing modelquotefreemium
Free tierNoYes
PlatformsAPI, CloudLinux
FoundedUnknown2014

Identical on both: 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 Groq

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.

Groq

  • Latency-sensitive applications requiring sub-second inference response timesnot Seldon
  • High-volume inference workloads where cost per inference matters at scalenot Seldon
  • Custom model deployment with performance guaranteesnot Seldon
  • Enterprise applications seeking inference-specific infrastructurenot Seldon

Seldon

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

Where each one falls short

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

Groq

  • Pricing is not published and is sold entirely by quote, making cost comparison difficult
  • Limited to open-weight models; no proprietary model access through the platform
  • Not widely integrated into third-party AI platforms compared to OpenAI or Anthropic

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

Groq

On request

No published plan breakdown. See the Groq review.

Seldon

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

Which should you pick?

Choose Groq if

  • You work on API, Cloud.
Groq 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 Groq or Seldon better?
Neither clearly leads. Groq starts at On request and Seldon at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Groq or Seldon?
Seldon has a free tier; the other does not. Paid plans start at On request for Groq and Free for Seldon.
Does Groq or Seldon run on more platforms?
Groq runs on API, Cloud. Seldon runs on Linux.
Can I use Seldon for free?
Yes. Seldon has a free tier, so you can try it without paying. Groq starts at On request.
What is Groq best used for?
Groq is most often used for latency-sensitive applications requiring sub-second inference response times, high-volume inference workloads where cost per inference matters at scale, custom model deployment with performance guarantees, enterprise applications seeking inference-specific infrastructure. Of those, latency-sensitive applications requiring sub-second inference response times and high-volume inference workloads where cost per inference matters at scale are not what Seldon is typically brought in for.
What can Groq do that Seldon cannot?
Seldon covers Kubernetes custom resources, Inference graphs, Traffic strategies, Open Inference Protocol.

Answered from the vendors’ own pages

Groq: Is Groq free or paid?

Pricing details are not published on the main website. To explore Groq's service and pricing, visit their console at console.groq.com/home.

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.

Groq: Does Groq offer a free tier or free credits?

Free tier availability is not documented on the public site. Check the Groq console for current free tier or trial options.

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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