Software · head to head
Seldon vs Comet ML
Comet ML
Software
Platform for tracking, comparing, and optimizing ML experiments
- From
- Free
- Rated
- -
The short version
- 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; Comet ML the free cloud tier caps data at 25,000 spans a month with 60 day retention
- They diverge on capability: Seldon covers Model serving, Comet ML covers Experiment tracking.
Where they differ
Only the attributes on which Seldon and Comet ML actually diverge.
Identical on both: starting price (Free), pricing model (freemium), free tier (Yes), 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 Comet ML
- Experiment tracking
- Code versioning
- Model registry
- Hyperparameter optimization
- Production monitoring
- PyTorch
- TensorFlow
- Keras
Both cover
- Linux support
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 Comet ML
- Building multi-step inference pipelines with A/B tests and explainersnot Comet ML
Comet ML
- Tracking machine learning experiments, metrics and model versionsnot Seldon
- Monitoring and evaluating LLM applications with tracingnot 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
Comet ML
- The free cloud tier caps data at 25,000 spans a month with 60 day retention
- Retention stays at 60 days even on the paid Pro plan, and extending it is a $29 per 100k spans add on
- Overage on Pro is $5 per additional 100,000 spans
- The free MLOps tier is a single user with 100 GB of storage and training hours governed by a fair usage policy
- Pro MLOps is $19 per user per month and caps the team at 10 users
Pricing, plan by plan
Seldon
Free- Seldon CoreFree
- Open source
- Kubernetes deployment
- Model serving
- Seldon DeployFree
- Enterprise features
- GUI
- Monitoring
Comet ML
Free- FreeFree
- 100 experiments
- Basic features
- Community support
- Team$179/month
- Unlimited experiments
- Team collaboration
- Priority support
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.
Choose Comet ML if
- You need experiment tracking.
- You want to start without paying.
- You work on Web, Linux, Mac, Windows.
- You also want code versioning.
Questions people ask
- Is Seldon or Comet ML better?
- Neither clearly leads. Seldon starts at Free and Comet ML at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, Seldon or Comet ML?
- Seldon starts at Free and Comet ML at Free.
- Does Seldon or Comet ML run on more platforms?
- Seldon runs on Linux. Comet ML runs on Web, Linux, Mac, Windows.
- Can I use Seldon for free?
- Both have a free tier, so you can try either at no cost before committing.
- 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 Comet ML is typically brought in for.
- What can Seldon do that Comet ML cannot?
- Seldon covers Model serving, A/B testing, Canary deployments, Outlier detection. Comet ML covers Experiment tracking, Code versioning, Model registry, Hyperparameter optimization. Both handle Linux support.
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