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Databases · head to head

Presto vs scikit-learn

Presto logo

Presto

Databases

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

From
Free
Rated
-
Full reviewWebsite
scikit-learn logo

scikit-learn

Machine Learning

Machine learning in Python

From
Free
Rated
-
Full reviewWebsite

The short version

  • Each has a real cost: 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.; scikit-learn no GPU acceleration by default; limited optional GPU support requires external arrays
  • They diverge on capability: Presto covers Federated querying, scikit-learn covers Classification algorithms.
  • Prices and features above were last checked on 31 August 2026.

Where they differ

Only the attributes on which Presto and scikit-learn actually diverge.

Attributes where Presto and scikit-learn differ
AttributePrestoscikit-learn
Pricing modelOpen source, no licence feeUnknown
PlatformsLinux, Docker, KubernetesPython, Linux, macOS, Windows
CategoryDatabasesMachine Learning
FoundedUnknown2007

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 Presto

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

Only in scikit-learn

  • Classification algorithms
  • Regression models
  • Clustering methods
  • Dimensionality reduction
  • Model selection
  • NumPy
  • SciPy
  • Pandas

What people use each for

The jobs each tool is most often brought in to do.

Presto

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

scikit-learn

  • Machine learningnot Presto
  • Data analysisnot Presto
  • Model trainingnot Presto
  • Predictive analyticsnot Presto

Where each one falls short

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

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.

scikit-learn

  • No GPU acceleration by default; limited optional GPU support requires external arrays
  • Single-machine only; no built-in distributed computing across clusters
  • All datasets must fit entirely in RAM; no out-of-core learning
  • No production-grade deep learning; neural network support limited to basic multilayer perceptron
  • No reinforcement learning algorithms

Pricing, plan by plan

Presto

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

scikit-learn

Free

No published plan breakdown. See the scikit-learn review.

Which should you pick?

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

Choose scikit-learn if

  • You need classification algorithms.
  • You want to start without paying.
  • You work on Python, Linux, macOS, Windows.
  • You also want regression models.
scikit-learn review

Questions people ask

Is Presto or scikit-learn better?
Neither clearly leads. Presto starts at Free and scikit-learn at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Presto or scikit-learn?
Presto starts at Free and scikit-learn at Free.
Does Presto or scikit-learn run on more platforms?
Presto runs on Linux, Docker, Kubernetes. scikit-learn runs on Python, Linux, macOS, Windows.
Can I use Presto for free?
Both have a free tier, so you can try either at no cost before committing.
What is Presto best used for?
Presto is most often used for an existing prestodb estate that needs continued upgrades rather than a migration to trino, a team buying ibm watsonx.data, where presto is the underlying query engine, joining a hive or iceberg lake to an operational postgresql database in one query without an etl step, very large scale interactive sql where the meta-tested branch is a specific requirement. Of those, an existing prestodb estate that needs continued upgrades rather than a migration to trino and a team buying ibm watsonx.data, where presto is the underlying query engine are not what scikit-learn is typically brought in for.
What can Presto do that scikit-learn cannot?
Presto covers Federated querying, In-memory execution, Open table format support, Presto C++ workers. scikit-learn covers Classification algorithms, Regression models, Clustering methods, Dimensionality reduction.

Answered from the vendors’ own pages

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.

scikit-learn: Does scikit-learn support GPU acceleration?

Scikit-learn has no native GPU support by design to keep installation simple and cross-platform. Since 2023, a limited number of estimators can run on GPUs if input data is provided as PyTorch or CuPy arrays, but this requires additional setup.

Source
Presto: Which should I choose for a new project?

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

scikit-learn: Can scikit-learn handle datasets larger than RAM?

No. Scikit-learn is built on NumPy which requires all data to fit in memory, and NumPy operates on single-machine CPUs only. For very large datasets, consider Spark MLlib or distributed alternatives.

Source
Presto: Who maintains Presto now?

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

scikit-learn: Is scikit-learn free to use commercially?

Yes. Scikit-learn is open source under the BSD license, which allows free commercial use, modification, and distribution.

Source
Presto: Is it still actively released?

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

scikit-learn: What neural network capabilities does scikit-learn have?

Scikit-learn includes only a basic multilayer perceptron (MLPClassifier and MLPRegressor) for simple feedforward networks. For serious deep learning, use PyTorch, TensorFlow, or Keras instead.

Source
scikit-learn: Does scikit-learn include natural language processing?

Scikit-learn has minimal NLP support limited to basic text feature extraction and vectorization. For comprehensive text processing, use spaCy or NLTK instead.

Source
scikit-learn: When was scikit-learn first released?

Scikit-learn's first public release was February 1, 2010, following its start as a Google Summer of Code project in 2007.

Source
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