Software · head to head
Seldon vs Groq
Groq
Software
Fast inference provider using proprietary LPU hardware for low-latency serving
- From
- On request
- Rated
- -
The short version
- Only Seldon has a free tier, so it costs nothing to try first.
- Each has a real cost: Seldon production deployment requires a Kubernetes cluster, whether managed such as GKE, EKS or AKS, or on-premises such as OpenShift; Groq pricing is not published and is sold entirely by quote, making cost comparison difficult
Where they differ
Only the attributes on which Seldon and Groq actually diverge.
Identical on both: user rating (Not yet rated), category (Unknown).
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
- Model serving
- A/B testing
- Canary deployments
- Outlier detection
- Model explainability
- Kubernetes
- Istio
- Prometheus
Only in Groq
Nothing recorded that Seldon does not also cover.
What people use each for
The jobs each tool is most often brought in to do.
Seldon
- Serving and routing machine learning models on Kubernetesnot Groq
- Building multi-step inference pipelines with A/B tests and explainersnot Groq
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
Where each one falls short
Documented limitations, not opinions. Every one is a constraint you would hit in normal use.
Seldon
- Production deployment requires a Kubernetes cluster, whether managed such as GKE, EKS or AKS, or on-premises such as OpenShift
- The documented components carry both minimum and maximum supported versions, so newer Kubernetes and dependency versions are not automatically supported
- Dataflow Pipelines need an additional component that the docs recommend avoiding installing when pipelines are not used
- The Docker Compose install is offered as a lightweight alternative for environments without Kubernetes rather than as a production path
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
Pricing, plan by plan
Seldon
Free- Seldon CoreFree
- Open source
- Kubernetes deployment
- Model serving
- Seldon DeployFree
- Enterprise features
- GUI
- Monitoring
Groq
On requestNo published plan breakdown. See the Groq review.
Which should you pick?
Choose Seldon if
- You need model serving.
- You want to start without paying.
- You work on Linux.
- You also want a/b testing.
Questions people ask
- Is Seldon or Groq better?
- Neither clearly leads. Seldon starts at Free and Groq at On request, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, Seldon or Groq?
- Seldon has a free tier; the other does not. Paid plans start at Free for Seldon and On request for Groq.
- Does Seldon or Groq run on more platforms?
- Seldon runs on Linux. Groq runs on API, Cloud.
- 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 Seldon best used for?
- Seldon is most often used for serving and routing machine learning models on kubernetes, building multi-step inference pipelines with a/b tests and explainers. Of those, serving and routing machine learning models on kubernetes and building multi-step inference pipelines with a/b tests and explainers are not what Groq is typically brought in for.
- What can Seldon do that Groq cannot?
- Seldon covers Model serving, A/B testing, Canary deployments, Outlier detection.