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Machine Learning · head to head

Apache Spark MLlib vs VerneMQ

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
VerneMQ logo

VerneMQ

Databases

Erlang MQTT broker whose source is Apache 2.0 but whose official binaries need a paid subscription

From
Free
Rated
-
Full reviewWebsite

The short version

  • Each has a real cost: 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.; VerneMQ the official binaries and Docker images are not Apache 2.0 but sit under a EULA requiring a yearly commercial subscription, a distinction easy to miss and awkward to discover during a licence audit.
  • They diverge on capability: Apache Spark MLlib covers DataFrame-based pipelines, VerneMQ covers Erlang/OTP clustering.
  • Prices and features above were last checked on 31 August 2026.

Where they differ

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

Attributes where Apache Spark MLlib and VerneMQ differ
AttributeApache Spark MLlibVerneMQ
Pricing modelopen-sourcequote
PlatformsLinux, macOS, WindowsLinux, Docker, macOS, Kubernetes
CategoryMachine LearningDatabases
Founded1999Unknown

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 Apache Spark MLlib

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

Only in VerneMQ

  • Erlang/OTP clustering
  • MQTT 5.0 support
  • Plugin system
  • Backpressure handling
  • Bridge support
  • Metrics export
  • MQTT over WebSockets
  • Pluggable auth backends

What people use each for

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

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 VerneMQ
  • Feature engineering and model fitting in one job over tables already in the lake, avoiding an extract and a second copy of sensitive datanot VerneMQ
  • Batch scoring of hundreds of millions of rows on a schedule, where throughput matters and per-request latency does notnot VerneMQ
  • Organisations that already run and pay for Spark, where adding a modelling step is cheaper than introducing a second platformnot VerneMQ

VerneMQ

  • An industrial operator that wants an MQTT broker with predictable memory behaviour and no data integration features it will not usenot Apache Spark MLlib
  • A team building from source to stay strictly under Apache 2.0 terms with no vendor licence entanglementnot Apache Spark MLlib
  • A deployment needing custom authentication logic implemented as a plugin in Lua or over a webhooknot Apache Spark MLlib
  • An organisation that wants a broker maintained by a small European company rather than by a vendor that keeps changing licencesnot Apache Spark MLlib

Where each one falls short

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

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.

VerneMQ

  • The official binaries and Docker images are not Apache 2.0 but sit under a EULA requiring a yearly commercial subscription, a distinction easy to miss and awkward to discover during a licence audit.
  • Octavo Labs is a very small company, so support depth, response times and the bus factor on the codebase are materially thinner than at HiveMQ or EMQ.
  • There is no data integration or rule engine layer, so routing messages into a database means writing and operating your own consumer service.
  • Operating an Erlang cluster requires runtime knowledge that most teams do not have and will use for nothing else in their stack.
  • There is no vendor-managed cloud offering, so every deployment is self-operated with the infrastructure and on-call cost that implies.

Pricing, plan by plan

Apache Spark MLlib

Free

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

VerneMQ

Free
  • Source buildFree
    • Apache 2.0 licensed source from GitHub
    • Full clustering and plugin capability
    • You compile and package it yourself
  • Binary packages and Docker images$undefined/year
    • Covered by the VerneMQ EULA, not Apache 2.0
    • Yearly usage subscription expected for commercial use
    • Official builds and Docker images
  • Commercial support$undefined/year
    • Evaluation, customisation and operations assistance
    • Custom development
    • Long-term maintenance agreements

Which should you pick?

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

Choose VerneMQ if

  • You need erlang/otp clustering.
  • You want to start without paying.
  • You work on Linux, Docker, macOS, Kubernetes.
  • You also want mqtt 5.0 support.
VerneMQ review

Questions people ask

Is Apache Spark MLlib or VerneMQ better?
Neither clearly leads. Apache Spark MLlib starts at Free and VerneMQ at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Apache Spark MLlib or VerneMQ?
Apache Spark MLlib starts at Free and VerneMQ at Free.
Does Apache Spark MLlib or VerneMQ run on more platforms?
Apache Spark MLlib runs on Linux, macOS, Windows. VerneMQ runs on Linux, Docker, macOS, Kubernetes.
Can I use Apache Spark MLlib for free?
Both have a free tier, so you can try either at no cost before committing.
What is Apache Spark MLlib best used for?
Apache Spark MLlib is most often used for training on a data set too large to hold on one machine, where sampling down would lose the rare events you care about, feature engineering and model fitting in one job over tables already in the lake, avoiding an extract and a second copy of sensitive data, batch scoring of hundreds of millions of rows on a schedule, where throughput matters and per-request latency does not, organisations that already run and pay for spark, where adding a modelling step is cheaper than introducing a second platform. Of those, training on a data set too large to hold on one machine, where sampling down would lose the rare events you care about and feature engineering and model fitting in one job over tables already in the lake, avoiding an extract and a second copy of sensitive data are not what VerneMQ is typically brought in for.
What can Apache Spark MLlib do that VerneMQ cannot?
Apache Spark MLlib covers DataFrame-based pipelines, Distributed algorithms, Alternating least squares, Feature transformers. VerneMQ covers Erlang/OTP clustering, MQTT 5.0 support, Plugin system, Backpressure handling.

Answered from the vendors’ own pages

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.

VerneMQ: Is VerneMQ free?

The source is Apache 2.0 and free. The official binary packages and Docker images are covered by a separate EULA that expects a yearly fee for commercial use.

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.

VerneMQ: Is the project still maintained?

Yes. Octavo Labs AG in Zurich continues to publish 2.x releases, most recently in 2026.

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.

VerneMQ: Does it have a managed cloud?

No. Every deployment is self-hosted, with commercial support available from Octavo Labs.

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.

VerneMQ: How does it compare to EMQX?

Narrower in features and without a rule engine, but with a simpler licence story for source builds after EMQX moved to BSL.

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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