Software · head to head
Comet ML vs Seldon

Comet ML
Software
Platform for tracking, comparing, and optimizing ML experiments
- From
- Free
- Rated
- -
The short version
- Each has a real cost: Comet ML the free cloud tier caps data at 25,000 spans a month with 60 day retention; Seldon production deployment requires a Kubernetes cluster, whether managed such as GKE, EKS or AKS, or on-premises such as OpenShift
- They diverge on capability: Comet ML covers Experiment tracking, Seldon covers Model serving.
Where they differ
Only the attributes on which Comet ML and Seldon 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 Comet ML
- Experiment tracking
- Code versioning
- Model registry
- Hyperparameter optimization
- Production monitoring
- PyTorch
- TensorFlow
- Keras
Only in Seldon
- Model serving
- A/B testing
- Canary deployments
- Outlier detection
- Model explainability
- Kubernetes
- Istio
- Prometheus
Both cover
- Linux support
What people use each for
The jobs each tool is most often brought in to do.
Comet ML
- Tracking machine learning experiments, metrics and model versionsnot Seldon
- Monitoring and evaluating LLM applications with tracingnot Seldon
Seldon
- Serving and routing machine learning models on Kubernetesnot Comet ML
- Building multi-step inference pipelines with A/B tests and explainersnot Comet ML
Where each one falls short
Documented limitations, not opinions. Every one is a constraint you would hit in normal use.
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
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
Pricing, plan by plan
Comet ML
Free- FreeFree
- 100 experiments
- Basic features
- Community support
- Team$179/month
- Unlimited experiments
- Team collaboration
- Priority support
Seldon
Free- Seldon CoreFree
- Open source
- Kubernetes deployment
- Model serving
- Seldon DeployFree
- Enterprise features
- GUI
- Monitoring
Which should you pick?
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.
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 Comet ML or Seldon better?
- Neither clearly leads. Comet ML 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, Comet ML or Seldon?
- Comet ML starts at Free and Seldon at Free.
- Does Comet ML or Seldon run on more platforms?
- Comet ML runs on Web, Linux, Mac, Windows. Seldon runs on Linux.
- Can I use Comet ML for free?
- Both have a free tier, so you can try either at no cost before committing.
- What is Comet ML best used for?
- Comet ML is most often used for tracking machine learning experiments, metrics and model versions, monitoring and evaluating llm applications with tracing. Of those, tracking machine learning experiments, metrics and model versions and monitoring and evaluating llm applications with tracing are not what Seldon is typically brought in for.
- What can Comet ML do that Seldon cannot?
- Comet ML covers Experiment tracking, Code versioning, Model registry, Hyperparameter optimization. Seldon covers Model serving, A/B testing, Canary deployments, Outlier detection. Both handle Linux support.
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