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Machine Learning · head to head

ClearML vs Presto

ClearML logo

ClearML

Machine Learning

Open-source MLOps platform for experiment tracking and orchestration

From
Free
Rated
-
Full reviewWebsite
Presto logo

Presto

Databases

The Meta-lineage distributed SQL query engine, distinct from the Trino fork

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; Presto the original creators and most of the active contributor base left for Trino in 2020, so Presto has the smaller community, fewer connectors and slower feature delivery of the two branches.
  • They diverge on capability: ClearML covers Experiment tracking, Presto covers Federated querying.
  • Prices and features above were last checked on 31 August 2026.

Where they differ

Only the attributes on which ClearML and Presto actually diverge.

Attributes where ClearML and Presto differ
AttributeClearMLPresto
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 Presto

  • Federated querying
  • In-memory execution
  • Open table format support
  • Presto C++ workers
  • ANSI SQL
  • Pluggable connectors

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

Presto

  • An existing PrestoDB estate that needs continued upgrades rather than a migration to Trinonot ClearML
  • A team buying IBM watsonx.data, where Presto is the underlying query enginenot ClearML
  • Joining a Hive or Iceberg lake to an operational PostgreSQL database in one query without an ETL stepnot ClearML
  • Very large scale interactive SQL where the Meta-tested branch is a specific requirementnot 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

Presto

  • The original creators and most of the active contributor base left for Trino in 2020, so Presto has the smaller community, fewer connectors and slower feature delivery of the two branches.
  • Documentation, tutorials and Stack Overflow answers for the two projects are frequently mixed up, and a solution written for Trino often does not apply, which costs real debugging time.
  • It is a query engine with no storage of its own, so query performance is dictated by your file layout, partitioning and statistics, and a badly organised lake makes Presto look slow.
  • Memory-bound execution means a single large join can fail the whole query rather than spilling gracefully, and tuning cluster memory settings is a persistent operational chore.
  • Commercial support has consolidated into IBM since the Ahana acquisition, so the independent vendor market that once existed around Presto is largely gone.

Pricing, plan by plan

ClearML

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

Presto

Free
  • PrestoFree
    • Apache 2.0 licence
    • Presto Foundation governance under the Linux Foundation
    • No node or query limits

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 Presto if

  • You need federated querying.
  • You want to start without paying.
  • You work on Linux, Docker, Kubernetes.
  • You also want in-memory execution.
Presto review

Questions people ask

Is ClearML or Presto better?
Neither clearly leads. ClearML starts at Free and Presto at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, ClearML or Presto?
ClearML starts at Free and Presto at Free.
Does ClearML or Presto run on more platforms?
ClearML runs on Linux, macOS, Windows, Docker, Kubernetes. Presto 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 Presto is typically brought in for.
What can ClearML do that Presto cannot?
ClearML covers Experiment tracking, Remote execution, Data versioning, Pipelines. Presto covers Federated querying, In-memory execution, Open table format support, Presto C++ workers.

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.

Presto: Is this Presto or Trino?

This is PrestoDB, the branch that stayed at Facebook and moved to the Linux Foundation. Trino is the 2020 fork by the original creators.

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.

Presto: Which should I choose for a new project?

Trino, in most cases. It has the larger community, more connectors and more commercial options.

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.

Presto: Who maintains Presto now?

Principally Meta, Uber and IBM, which acquired the Presto vendor Ahana in 2023.

Presto: Is it still actively released?

Yes, releases continue on a regular cadence under the Presto Foundation.

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