Softwr

Machine Learning · head to head

ClearML vs Pachyderm

ClearML logo

ClearML

Machine Learning

Open-source MLOps platform for experiment tracking and orchestration

From
Free
Rated
-
Full reviewWebsite
P

Pachyderm

Machine Learning

Data versioning and container pipelines that run on your Kubernetes cluster

From
Free
Rated
-
Full reviewWebsite

The short version

  • Each has a real cost: ClearML broad scope means more to learn and more to run than a focused tracking tool; 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: ClearML 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 ClearML and Pachyderm actually diverge.

Attributes where ClearML and Pachyderm differ
AttributeClearMLPachyderm
Pricing modelOpen-source self-hosted, with paid hosted and enterprise tiersfreemium
PlatformsLinux, macOS, Windows, Docker, KubernetesLinux
FoundedUnknown2014

Identical on both: starting price (Free), 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 ClearML

  • Experiment tracking
  • Remote execution
  • Data versioning
  • Pipelines

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.

ClearML

  • Tracking experiments across a team so results are reproduciblenot Pachyderm
  • Moving training from laptops to shared GPU hardware without repackagingnot Pachyderm
  • Versioning datasets alongside the experiments that consumed themnot 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 ClearML
  • Regulated pipelines where an auditor will ask which exact input files and which code version produced a given resultnot ClearML
  • Genomics and scientific workflows built from existing command line tools that are easier to containerise than to rewritenot ClearML
  • Teams that already run Kubernetes and want data lineage without adopting a full commercial ML platformnot ClearML

Where each one falls short

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

ClearML

  • Broad scope means more to learn and more to run than a focused tracking tool
  • Self-hosting the server is real infrastructure — database, file storage and web server
  • Documentation quality is uneven across the newer parts of the platform
  • Smaller community than the most popular tracking tools, so fewer worked examples exist

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

ClearML

Free
  • Open sourceFree
    • Experiment tracking
    • Pipelines
    • Self-hosted server

Pachyderm

Free
  • CommunityFree
    • Core features
    • Community support
  • EnterpriseFree
    • Advanced security
    • Premium support
    • SLAs

Which should you pick?

Choose ClearML if

  • You need experiment tracking.
  • You want to start without paying.
  • You work on Linux, macOS, Windows, Docker, Kubernetes.
  • You also want remote execution.
ClearML review

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.
Pachyderm review

Questions people ask

Is ClearML or Pachyderm better?
Neither clearly leads. ClearML 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, ClearML or Pachyderm?
ClearML starts at Free and Pachyderm at Free.
Does ClearML or Pachyderm run on more platforms?
ClearML runs on Linux, macOS, Windows, Docker, Kubernetes. Pachyderm runs on Linux.
Can I use ClearML for free?
Both have a free tier, so you can try either at no cost before committing.
What is ClearML best used for?
ClearML is most often used for tracking experiments across a team so results are reproducible, moving training from laptops to shared gpu hardware without repackaging, versioning datasets alongside the experiments that consumed them. Of those, tracking experiments across a team so results are reproducible and moving training from laptops to shared gpu hardware without repackaging are not what Pachyderm is typically brought in for.
What can ClearML do that Pachyderm cannot?
ClearML covers Experiment tracking, Remote execution, Data versioning, Pipelines. Pachyderm covers Versioned file system, Datum-based incremental processing, Container pipelines, Automatic provenance.

Answered from the vendors’ own pages

ClearML: Is ClearML free?

The open-source version is free and self-hostable. Hosted and enterprise tiers are paid.

Pachyderm: 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.

ClearML: How much code does tracking require?

Very little — adding a couple of lines to an existing training script captures parameters, metrics and environment automatically.

Pachyderm: 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.

ClearML: Does ClearML replace MLflow?

It covers MLflow’s tracking and adds orchestration, remote execution and data versioning. Whether that breadth is an advantage or extra weight depends on whether you need the rest.

Pachyderm: 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.

Pachyderm: 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.

Share

Related pages

More on ClearML

More on Pachyderm

Keep looking

Other head to heads