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Machine Learning · head to head

ClearML vs NATS

ClearML logo

ClearML

Machine Learning

Open-source MLOps platform for experiment tracking and orchestration

From
Free
Rated
-
Full reviewWebsite
NATS logo

NATS

Databases

High-performance messaging system for cloud-native applications

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; NATS core NATS has no persistence at all, so messages are lost if no subscriber is listening
  • They diverge on capability: ClearML covers Experiment tracking, NATS covers Very low latency.
  • Prices and features above were last checked on 29 August 2026.

Where they differ

Only the attributes on which ClearML and NATS actually diverge.

Attributes where ClearML and NATS differ
AttributeClearMLNATS
Pricing modelOpen-source self-hosted, with paid hosted and enterprise tiersOpen source, no licence fee
CategoryMachine LearningDatabases

Identical on both: starting price (Free), free tier (Yes), platforms (Linux, macOS, Windows, Docker, Kubernetes), user rating (Not yet rated).

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 NATS

  • Very low latency
  • JetStream
  • Single binary
  • Request-reply

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 NATS
  • Moving training from laptops to shared GPU hardware without repackagingnot NATS
  • Versioning datasets alongside the experiments that consumed themnot NATS

NATS

  • Service-to-service messaging where latency is the binding constraintnot ClearML
  • Edge and IoT messaging where a lightweight broker mattersnot ClearML
  • Replacing a heavier broker when the workload does not need its guaranteesnot 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

NATS

  • Core NATS has no persistence at all, so messages are lost if no subscriber is listening
  • JetStream adds the durability but also the operational complexity NATS is chosen to avoid
  • A much smaller ecosystem than Kafka or RabbitMQ, with fewer connectors and integrations
  • Fewer people know it, so hiring and existing organisational knowledge favour the alternatives

Pricing, plan by plan

ClearML

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

NATS

Free
  • NATSFree
    • Full functionality
    • No usage limits
    • Community support

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 NATS if

  • You need very low latency.
  • You want to start without paying.
  • You work on Linux, macOS, Windows, Docker, Kubernetes.
  • You also want jetstream.
NATS review

Questions people ask

Is ClearML or NATS better?
Neither clearly leads. ClearML starts at Free and NATS at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, ClearML or NATS?
ClearML starts at Free and NATS at Free.
Does ClearML or NATS run on more platforms?
Both run on Linux, macOS, Windows, Docker, Kubernetes, so platform support will not decide this one for you.
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 NATS is typically brought in for.
What can ClearML do that NATS cannot?
ClearML covers Experiment tracking, Remote execution, Data versioning, Pipelines. NATS covers Very low latency, JetStream, Single binary, Request-reply.

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.

NATS: Is NATS free?

Yes, open source and CNCF-graduated. Synadia sells a managed service.

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.

NATS: Does NATS persist messages?

Core NATS does not — it is fire-and-forget. JetStream adds persistence, streaming and replay when you need them.

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.

NATS: NATS or Kafka?

NATS is far lighter and lower latency, and much simpler to run. Kafka is the answer when you need a durable replayable log and a large connector ecosystem.

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