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Machine Learning · head to head

ClearML vs StarRocks

ClearML logo

ClearML

Machine Learning

Open-source MLOps platform for experiment tracking and orchestration

From
Free
Rated
-
Full reviewWebsite
StarRocks logo

StarRocks

Databases

Apache 2.0 MPP analytical database built for joins on open table formats

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; StarRocks self-hosting is a genuine operations job: frontend and backend node roles, tablet distribution, compaction and materialised view refresh all need an owner, and there is no small-team-friendly single-binary mode.
  • They diverge on capability: ClearML covers Experiment tracking, StarRocks covers Cost-based optimiser.
  • Prices and features above were last checked on 31 August 2026.

Where they differ

Only the attributes on which ClearML and StarRocks actually diverge.

Attributes where ClearML and StarRocks differ
AttributeClearMLStarRocks
Pricing modelOpen-source self-hosted, with paid hosted and enterprise tiersOpen source, no licence fee
PlatformsLinux, macOS, Windows, Docker, KubernetesLinux, Docker, Kubernetes
CategoryMachine LearningDatabases

Identical on both: starting price (Free), free tier (Yes), 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 StarRocks

  • Cost-based optimiser
  • Lakehouse query engine
  • Primary key tables
  • Materialised views
  • Shared-data mode
  • MySQL wire protocol

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

StarRocks

  • Customer-facing analytics where queries join a fact table to several dimensions and must return in well under a secondnot ClearML
  • Querying an Iceberg lakehouse directly without copying data into a proprietary warehouse formatnot ClearML
  • Replacing a ClickHouse deployment that has become unmanageable because every new question needs another denormalised tablenot ClearML
  • Real-time analytics fed by change data capture where rows must be updated in place rather than appendednot 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

StarRocks

  • Self-hosting is a genuine operations job: frontend and backend node roles, tablet distribution, compaction and materialised view refresh all need an owner, and there is no small-team-friendly single-binary mode.
  • CelerData is by far the dominant contributor despite Linux Foundation stewardship, so the practical roadmap risk is the same as any single-vendor open source project.
  • It inherits a MySQL-flavoured SQL dialect from its Doris ancestry, so queries written for PostgreSQL, Snowflake or Trino need rewriting rather than porting.
  • Ecosystem support is thinner than ClickHouse or Trino: fewer client libraries, fewer managed hosting options and a much smaller pool of engineers who have run it in production.
  • Memory pressure under concurrent large joins is a common production failure, and the tuning knobs for query memory limits are unforgiving compared with a cloud warehouse that just scales.

Pricing, plan by plan

ClearML

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

StarRocks

Free
  • StarRocksFree
    • Apache 2.0 licence
    • Linux Foundation governance
    • No usage or node limits
  • CelerData Cloud$undefined/year
    • Managed StarRocks from the primary contributor
    • BYOC and serverless deployment options
    • Enterprise support and 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 StarRocks if

  • You need cost-based optimiser.
  • You want to start without paying.
  • You work on Linux, Docker, Kubernetes.
  • You also want lakehouse query engine.
StarRocks review

Questions people ask

Is ClearML or StarRocks better?
Neither clearly leads. ClearML starts at Free and StarRocks at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, ClearML or StarRocks?
ClearML starts at Free and StarRocks at Free.
Does ClearML or StarRocks run on more platforms?
ClearML runs on Linux, macOS, Windows, Docker, Kubernetes. StarRocks runs on Linux, Docker, Kubernetes.
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 StarRocks is typically brought in for.
What can ClearML do that StarRocks cannot?
ClearML covers Experiment tracking, Remote execution, Data versioning, Pipelines. StarRocks covers Cost-based optimiser, Lakehouse query engine, Primary key tables, Materialised views.

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.

StarRocks: Is StarRocks open source?

Yes, Apache 2.0, governed under the Linux Foundation since 2023.

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.

StarRocks: How does it differ from ClickHouse?

StarRocks is built for joins across a star schema with a cost-based optimiser; ClickHouse is fastest on denormalised single tables.

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.

StarRocks: Who maintains it?

CelerData, formerly StarRocks Inc, is the dominant contributor and sells the managed service.

StarRocks: Can it query Iceberg tables directly?

Yes, along with Hudi, Delta Lake, Hive and Paimon, with a local cache for repeat queries.

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