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NATS vs Apache Spark MLlib

NATS logo

NATS

Databases

High-performance messaging system for cloud-native applications

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: NATS core NATS has no persistence at all, so messages are lost if no subscriber is listening; 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: NATS covers Very low latency, 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 NATS and Apache Spark MLlib actually diverge.

Attributes where NATS and Apache Spark MLlib differ
AttributeNATSApache Spark MLlib
Pricing modelOpen source, no licence feeopen-source
PlatformsLinux, macOS, Windows, 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 NATS

  • Very low latency
  • JetStream
  • Single binary
  • Request-reply

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.

NATS

  • Service-to-service messaging where latency is the binding constraintnot Apache Spark MLlib
  • Edge and IoT messaging where a lightweight broker mattersnot Apache Spark MLlib
  • Replacing a heavier broker when the workload does not need its guaranteesnot 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 NATS
  • Feature engineering and model fitting in one job over tables already in the lake, avoiding an extract and a second copy of sensitive datanot NATS
  • Batch scoring of hundreds of millions of rows on a schedule, where throughput matters and per-request latency does notnot NATS
  • Organisations that already run and pay for Spark, where adding a modelling step is cheaper than introducing a second platformnot NATS

Where each one falls short

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

NATS

  • Core NATS has no persistence at all, so messages are lost if no subscriber is listening
  • JetStream adds the durability but also the operational complexity NATS is chosen to avoid
  • A much smaller ecosystem than Kafka or RabbitMQ, with fewer connectors and integrations
  • Fewer people know it, so hiring and existing organisational knowledge favour the alternatives

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

NATS

Free
  • NATSFree
    • 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 NATS if

  • You need very low latency.
  • You want to start without paying.
  • You work on Linux, macOS, Windows, Docker, Kubernetes.
  • You also want jetstream.
NATS 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 NATS or Apache Spark MLlib better?
Neither clearly leads. NATS 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, NATS or Apache Spark MLlib?
NATS starts at Free and Apache Spark MLlib at Free.
Does NATS or Apache Spark MLlib run on more platforms?
NATS runs on Linux, macOS, Windows, Docker, Kubernetes. Apache Spark MLlib runs on Linux, macOS, Windows.
Can I use NATS for free?
Both have a free tier, so you can try either at no cost before committing.
What is NATS best used for?
NATS is most often used for service-to-service messaging where latency is the binding constraint, edge and iot messaging where a lightweight broker matters, replacing a heavier broker when the workload does not need its guarantees. Of those, service-to-service messaging where latency is the binding constraint and edge and iot messaging where a lightweight broker matters are not what Apache Spark MLlib is typically brought in for.
What can NATS do that Apache Spark MLlib cannot?
NATS covers Very low latency, JetStream, Single binary, Request-reply. Apache Spark MLlib covers DataFrame-based pipelines, Distributed algorithms, Alternating least squares, Feature transformers.

Answered from the vendors’ own pages

NATS: Is NATS free?

Yes, open source and CNCF-graduated. Synadia sells a managed service.

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.

NATS: Does NATS persist messages?

Core NATS does not — it is fire-and-forget. JetStream adds persistence, streaming and replay when you need them.

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.

NATS: NATS or Kafka?

NATS is far lighter and lower latency, and much simpler to run. Kafka is the answer when you need a durable replayable log and a large connector ecosystem.

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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