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OpenSearch vs Apache Spark MLlib

OpenSearch logo

OpenSearch

Databases

Open-source search and analytics suite forked from Elasticsearch

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: OpenSearch diverged from Elasticsearch since 7.10, so clients, plugins and features no longer map one to one; 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: OpenSearch covers Full-text search, 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 OpenSearch and Apache Spark MLlib actually diverge.

Attributes where OpenSearch and Apache Spark MLlib differ
AttributeOpenSearchApache Spark MLlib
Pricing modelOpen source, no licence fee; managed services billed separatelyopen-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 OpenSearch

  • Full-text search
  • OpenSearch Dashboards
  • Log analytics
  • Vector search

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.

OpenSearch

  • Log and observability storage where an Apache-2.0 licence is a requirementnot Apache Spark MLlib
  • Replacing Elasticsearch after the licence change without changing architecturenot Apache Spark MLlib
  • Search plus analytics on one cluster rather than two systemsnot 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 OpenSearch
  • Feature engineering and model fitting in one job over tables already in the lake, avoiding an extract and a second copy of sensitive datanot OpenSearch
  • Batch scoring of hundreds of millions of rows on a schedule, where throughput matters and per-request latency does notnot OpenSearch
  • Organisations that already run and pay for Spark, where adding a modelling step is cheaper than introducing a second platformnot OpenSearch

Where each one falls short

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

OpenSearch

  • Diverged from Elasticsearch since 7.10, so clients, plugins and features no longer map one to one
  • Operationally heavy in the way Elasticsearch is: cluster sizing, shard strategy and JVM tuning are ongoing work
  • Smaller ecosystem of third-party tooling than Elasticsearch, which most integrations still target first
  • Overkill for plain application search, where a dedicated search engine is far simpler

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

OpenSearch

Free
  • OpenSearchFree
    • Full functionality
    • Self-hosted
    • No usage limits

Apache Spark MLlib

Free

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

Which should you pick?

Choose OpenSearch if

  • You need full-text search.
  • You want to start without paying.
  • You work on Linux, Docker, Kubernetes, Self-hosted.
  • You also want opensearch dashboards.
OpenSearch 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 OpenSearch or Apache Spark MLlib better?
Neither clearly leads. OpenSearch 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, OpenSearch or Apache Spark MLlib?
OpenSearch starts at Free and Apache Spark MLlib at Free.
Does OpenSearch or Apache Spark MLlib run on more platforms?
OpenSearch runs on Linux, Docker, Kubernetes, Self-hosted. Apache Spark MLlib runs on Linux, macOS, Windows.
Can I use OpenSearch for free?
Both have a free tier, so you can try either at no cost before committing.
What is OpenSearch best used for?
OpenSearch is most often used for log and observability storage where an apache-2.0 licence is a requirement, replacing elasticsearch after the licence change without changing architecture, search plus analytics on one cluster rather than two systems. Of those, log and observability storage where an apache-2.0 licence is a requirement and replacing elasticsearch after the licence change without changing architecture are not what Apache Spark MLlib is typically brought in for.
What can OpenSearch do that Apache Spark MLlib cannot?
OpenSearch covers Full-text search, OpenSearch Dashboards, Log analytics, Vector search. Apache Spark MLlib covers DataFrame-based pipelines, Distributed algorithms, Alternating least squares, Feature transformers.

Answered from the vendors’ own pages

OpenSearch: Is OpenSearch free?

Yes, Apache 2.0 licensed under the Linux Foundation. Amazon OpenSearch Service is a paid managed option.

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.

OpenSearch: Why does OpenSearch exist?

Elastic moved Elasticsearch off the Apache 2.0 licence in 2021. AWS forked the last Apache-licensed version, and the project now sits under the Linux Foundation.

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.

OpenSearch: Is OpenSearch compatible with Elasticsearch?

It was at the 7.10 fork point. Both have developed independently since, so compatibility weakens with every release and should be verified for the features you use.

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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