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Databases · head to head

Apache Pulsar vs Apache Spark MLlib

Apache Pulsar logo

Apache Pulsar

Databases

Cloud-native messaging and streaming with separated storage

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: Apache Pulsar more components than Kafka: brokers, BookKeeper and ZooKeeper each need operating; 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: Apache Pulsar covers Separated storage, Apache Spark MLlib covers DataFrame-based pipelines.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

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

Attributes where Apache Pulsar and Apache Spark MLlib differ
AttributeApache PulsarApache Spark MLlib
Pricing modelOpen source, no licence feeopen-source
PlatformsLinux, Docker, Kubernetes, Self-hostedLinux, 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 Apache Pulsar

  • Separated storage
  • Queuing and streaming
  • Built-in multi-tenancy
  • Geo-replication

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.

Apache Pulsar

  • Platforms needing both work queues and replayable streams without running two systemsnot Apache Spark MLlib
  • Multi-tenant messaging where isolation between teams is a requirementnot Apache Spark MLlib
  • Deployments where storage and traffic grow at genuinely different ratesnot 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 Apache Pulsar
  • Feature engineering and model fitting in one job over tables already in the lake, avoiding an extract and a second copy of sensitive datanot Apache Pulsar
  • Batch scoring of hundreds of millions of rows on a schedule, where throughput matters and per-request latency does notnot Apache Pulsar
  • Organisations that already run and pay for Spark, where adding a modelling step is cheaper than introducing a second platformnot Apache Pulsar

Where each one falls short

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

Apache Pulsar

  • More components than Kafka: brokers, BookKeeper and ZooKeeper each need operating
  • Correspondingly harder to run well, and the expertise is rarer than Kafka expertise
  • A much smaller ecosystem of connectors, tooling and hiring pool than Kafka
  • The architectural advantages only pay off at a scale most deployments never reach

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

Apache Pulsar

Free
  • Apache PulsarFree
    • Full functionality
    • No usage limits
    • Community support

Apache Spark MLlib

Free

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

Which should you pick?

Choose Apache Pulsar if

  • You need separated storage.
  • You want to start without paying.
  • You work on Linux, Docker, Kubernetes, Self-hosted.
  • You also want queuing and streaming.
Apache Pulsar 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 Apache Pulsar or Apache Spark MLlib better?
Neither clearly leads. Apache Pulsar 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, Apache Pulsar or Apache Spark MLlib?
Apache Pulsar starts at Free and Apache Spark MLlib at Free.
Does Apache Pulsar or Apache Spark MLlib run on more platforms?
Apache Pulsar runs on Linux, Docker, Kubernetes, Self-hosted. Apache Spark MLlib runs on Linux, macOS, Windows.
Can I use Apache Pulsar for free?
Both have a free tier, so you can try either at no cost before committing.
What is Apache Pulsar best used for?
Apache Pulsar is most often used for platforms needing both work queues and replayable streams without running two systems, multi-tenant messaging where isolation between teams is a requirement, deployments where storage and traffic grow at genuinely different rates. Of those, platforms needing both work queues and replayable streams without running two systems and multi-tenant messaging where isolation between teams is a requirement are not what Apache Spark MLlib is typically brought in for.
What can Apache Pulsar do that Apache Spark MLlib cannot?
Apache Pulsar covers Separated storage, Queuing and streaming, Built-in multi-tenancy, Geo-replication. Apache Spark MLlib covers DataFrame-based pipelines, Distributed algorithms, Alternating least squares, Feature transformers.

Answered from the vendors’ own pages

Apache Pulsar: Is Apache Pulsar free?

Yes, open source under the Apache Software Foundation.

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.

Apache Pulsar: Pulsar or Kafka?

Pulsar separates storage from compute and covers queuing and streaming in one system. Kafka has a far larger ecosystem and hiring pool. Most teams should have a specific reason before choosing Pulsar.

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.

Apache Pulsar: Why does separated storage matter?

Brokers hold no data, so adding or replacing one requires no rebalancing, and storage can grow without adding serving capacity.

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.

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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