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Databases · head to head

Presto vs Apache Spark MLlib

Presto logo

Presto

Databases

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

From
Free
Rated
-
Full reviewWebsite
Apache Spark MLlib logo

Apache Spark MLlib

Machine Learning

The machine learning library inside Apache Spark, for data that will not fit on one machine

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.; Apache Spark MLlib the algorithm set has grown slowly and its gradient boosting does not match XGBoost or LightGBM in accuracy or speed, so teams routinely do feature engineering in Spark and then train elsewhere, which undoes the argument for using it at all.
  • They diverge on capability: Presto covers Federated querying, Apache Spark MLlib covers DataFrame-based pipelines.
  • Prices and features above were last checked on 31 August 2026.

Where they differ

Only the attributes on which Presto and Apache Spark MLlib actually diverge.

Attributes where Presto and Apache Spark MLlib differ
AttributePrestoApache Spark MLlib
Pricing modelOpen source, no licence feeopen-source
PlatformsLinux, Docker, KubernetesLinux, macOS, Windows
CategoryDatabasesMachine Learning
FoundedUnknown1999

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 Apache Spark MLlib

  • DataFrame-based pipelines
  • Distributed algorithms
  • Alternating least squares
  • Feature transformers
  • Model selection
  • Pipeline persistence
  • Language bindings
  • Runs in existing Spark deployments

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 Apache Spark MLlib
  • A team buying IBM watsonx.data, where Presto is the underlying query enginenot Apache Spark MLlib
  • Joining a Hive or Iceberg lake to an operational PostgreSQL database in one query without an ETL stepnot Apache Spark MLlib
  • Very large scale interactive SQL where the Meta-tested branch is a specific requirementnot Apache Spark MLlib

Apache Spark MLlib

  • Training on a data set too large to hold on one machine, where sampling down would lose the rare events you care aboutnot Presto
  • Feature engineering and model fitting in one job over tables already in the lake, avoiding an extract and a second copy of sensitive datanot Presto
  • Batch scoring of hundreds of millions of rows on a schedule, where throughput matters and per-request latency does notnot Presto
  • Organisations that already run and pay for Spark, where adding a modelling step is cheaper than introducing a second platformnot 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.

Apache Spark MLlib

  • The algorithm set has grown slowly and its gradient boosting does not match XGBoost or LightGBM in accuracy or speed, so teams routinely do feature engineering in Spark and then train elsewhere, which undoes the argument for using it at all.
  • There is no deep learning in MLlib; neural network work on Spark requires a separate integration, and the DataFrame-centred interface is an awkward fit for it.
  • Fitted models serialise into Spark's own format, so low-latency serving needs either a Spark session in the request path, which is far too slow, or a conversion through ONNX or MLeap, and this is where most Spark ML projects stall.
  • Debugging is JVM cluster debugging: executor out-of-memory, shuffle spill, skewed partitions and serialisation failures, so an engineer without Spark operations experience spends more time tuning the cluster than improving the model.
  • The cluster is the real cost and Spark holds executors for the duration of a job, so a badly partitioned training run pays for idle cores across the whole fleet while one straggler task finishes.

Pricing, plan by plan

Presto

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

Apache Spark MLlib

Free

No published plan breakdown. See the Apache Spark MLlib 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 Apache Spark MLlib if

  • You need dataframe-based pipelines.
  • You want to start without paying.
  • You work on Linux, macOS, Windows.
  • You also want distributed algorithms.
Apache Spark MLlib review

Questions people ask

Is Presto or Apache Spark MLlib better?
Neither clearly leads. Presto starts at Free and Apache Spark MLlib at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Presto or Apache Spark MLlib?
Presto starts at Free and Apache Spark MLlib at Free.
Does Presto or Apache Spark MLlib run on more platforms?
Presto runs on Linux, Docker, Kubernetes. Apache Spark MLlib runs on 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 Apache Spark MLlib is typically brought in for.
What can Presto do that Apache Spark MLlib cannot?
Presto covers Federated querying, In-memory execution, Open table format support, Presto C++ workers. Apache Spark MLlib covers DataFrame-based pipelines, Distributed algorithms, Alternating least squares, Feature transformers.

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.

Apache Spark MLlib: What is the difference between spark.ml and spark.mllib?

spark.ml is the DataFrame-based interface and the one to use. spark.mllib is the older RDD-based package, kept for compatibility, in maintenance and receiving no new features.

Presto: Which should I choose for a new project?

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

Apache Spark MLlib: Do I need a cluster?

Spark runs in local mode on one machine, which is useful for development, but if you are running on one machine you would generally be better served by scikit-learn or XGBoost, which are faster and more capable at that scale.

Presto: Who maintains Presto now?

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

Apache Spark MLlib: Can I use scikit-learn on Spark instead?

Yes, and it is often the better answer. You can distribute independent model fits across the cluster, or use pandas user-defined functions to run per-group models, keeping Spark for the data and a mature library for the modelling.

Presto: Is it still actively released?

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

Apache Spark MLlib: How do I serve an MLlib model in real time?

Not directly. Either convert the pipeline to a portable format such as ONNX or MLeap, or reimplement the scoring path. Starting a Spark session per request adds seconds of overhead and is not a serving strategy.

Apache Spark MLlib: Is it free?

The library is Apache 2.0 and costs nothing. The cluster it runs on is billed by your cloud provider or by Databricks, and that is the actual expense.

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