Machine Learning · head to head
Comet ML vs Pachyderm
Comet ML
Machine Learning
Platform for tracking, comparing, and optimizing ML experiments
- From
- Free
- Rated
- -
Pachyderm
Machine Learning
Data versioning and container pipelines that run on your Kubernetes cluster
- 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; Pachyderm it runs only on Kubernetes, so operating it means someone who can debug pods, storage classes and node pressure, and on a team without that person a cluster problem and an ML outage are the same event.
- They diverge on capability: Comet ML covers Experiment tracking, Pachyderm covers Versioned file system.
- Prices and features above were last checked on 30 August 2026.
Where they differ
Only the attributes on which Comet ML and Pachyderm actually diverge.
Identical on both: starting price (Free), pricing model (freemium), 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 Comet ML
- Experiment tracking
- Code versioning
- Model registry
- Hyperparameter optimization
- Production monitoring
- PyTorch
- TensorFlow
- Keras
Only in Pachyderm
- Versioned file system
- Datum-based incremental processing
- Container pipelines
- Automatic provenance
- Parallel execution
- S3 gateway
- Enterprise authentication
- Object storage backends
What people use each for
The jobs each tool is most often brought in to do.
Comet ML
- LLM observability and monitoringnot Pachyderm
- AI agent testing and debuggingnot Pachyderm
- Experiment tracking for machine learningnot Pachyderm
- Model registry and version managementnot Pachyderm
- ML model training monitoringnot Pachyderm
Pachyderm
- Reprocessing a growing archive of images or documents where a full pass every night would be wasteful and only the new files matternot Comet ML
- Regulated pipelines where an auditor will ask which exact input files and which code version produced a given resultnot Comet ML
- Genomics and scientific workflows built from existing command line tools that are easier to containerise than to rewritenot Comet ML
- Teams that already run Kubernetes and want data lineage without adopting a full commercial ML platformnot 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
Pachyderm
- It runs only on Kubernetes, so operating it means someone who can debug pods, storage classes and node pressure, and on a team without that person a cluster problem and an ML outage are the same event.
- Data is held in Pachyderm's content-addressed repositories rather than as plain files in a bucket, so every other tool reaches it through the client or the S3 gateway and migrating away is a full export rather than a redirect.
- The glob pattern that decides the unit of parallel work is the most consequential line in a pipeline specification, and getting it wrong produces either one enormous serial job or millions of tiny ones whose container start-up dominates the runtime.
- Compute is billed by your cloud provider, not by Pachyderm, so a platform that looks inexpensive on the licence line runs on a cluster that has to be sized for peak pipeline load and, for training work, carries GPU nodes.
- The project's direction now sits inside a large hardware vendor's portfolio following the 2023 acquisition, and a team adopting the community edition has no contractual claim on its continued development.
Pricing, plan by plan
Comet ML
Free- Free CloudFree
- Up to 10 team members
- 25,000 spans per month
- 60-day data retention
- Pro Cloud$19/month
- Up to 50 team members
- 100,000 spans per month
- 60-day data retention
- MLOps FreeFree
- 1 user with fair usage policy
- Experiment tracking
- Dataset management
- MLOps Pro$19/user/month
- Up to 10 users
- 1,500 training hours included
- 500GB storage included
Pachyderm
Free- CommunityFree
- Core features
- Community support
- EnterpriseFree
- Advanced security
- Premium support
- SLAs
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 Pachyderm if
- You need versioned file system.
- You want to start without paying.
- You work on Linux.
- You also want datum-based incremental processing.
Questions people ask
- Is Comet ML or Pachyderm better?
- Neither clearly leads. Comet ML starts at Free and Pachyderm at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, Comet ML or Pachyderm?
- Comet ML starts at Free and Pachyderm at Free.
- Does Comet ML or Pachyderm run on more platforms?
- Comet ML runs on Web, Linux, Mac, Windows. Pachyderm 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 llm observability and monitoring, ai agent testing and debugging, experiment tracking for machine learning, model registry and version management. Of those, llm observability and monitoring and ai agent testing and debugging are not what Pachyderm is typically brought in for.
- What can Comet ML do that Pachyderm cannot?
- Comet ML covers Experiment tracking, Code versioning, Model registry, Hyperparameter optimization. Pachyderm covers Versioned file system, Datum-based incremental processing, Container pipelines, Automatic provenance.
Answered from the vendors’ own pages
Comet ML: Does Comet.ml offer a free plan?
Yes, Comet.ml offers free tiers for both Opik (cloud observability) and MLOps platforms. Free Cloud Opik includes up to 10 team members and 25,000 spans/month. Free MLOps tier is limited to 1 user.
SourcePachyderm: Is Pachyderm open source?
The community edition is, under Apache 2.0. Authentication, role-based access control, the console and multi-tenancy sit behind an enterprise licence key, which is the set of features most organisations need once more than one team uses it.
Comet ML: How many team members can use the free Comet.ml tier?
Free Cloud supports up to 10 team members. The Pro Cloud plan supports up to 50 team members at $19/month.
SourcePachyderm: Do I need Kubernetes to run it?
Yes. There is no non-Kubernetes deployment. A local single-node install exists for evaluation, but anything real is a cluster with object storage behind it.
Comet ML: What is a span in Comet.ml pricing?
A span represents a single tracked operation such as model requests or function calls. Free Cloud tier includes 25,000 spans per month.
SourcePachyderm: How is it different from DVC?
DVC is a command line tool a person runs alongside Git, with no server. Pachyderm is a server that owns the data and schedules the work centrally. DVC records what you did; Pachyderm does it and records it.
Comet ML: Does Comet.ml offer academic pricing?
Yes, a free Pro plan is available for academic users; verification is required via signup.
SourcePachyderm: What does it actually cost to run?
The licence is separate from the infrastructure. You pay your cloud provider for the Kubernetes nodes that run every pipeline pod and for the object storage holding every version of every data set, and that bill grows with history as well as with size.
Pachyderm: Can I serve models with it?
No. It is a batch data and training pipeline system. Serving is a separate tool and a separate deployment.
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