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SingleStore vs Apache Spark MLlib

SingleStore logo

SingleStore

Databases

The real-time distributed SQL database for data-intensive 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: SingleStore high licensing costs that increase with data scale and cluster size; 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: SingleStore covers Real-time Analytics, 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 SingleStore and Apache Spark MLlib actually diverge.

Attributes where SingleStore and Apache Spark MLlib differ
AttributeSingleStoreApache Spark MLlib
Pricing modelUnknownopen-source
PlatformsCloud (SingleStoreDB Cloud), Self-ManagedLinux, macOS, Windows
CategoryDatabasesMachine Learning
Founded20111999

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 SingleStore

  • Real-time Analytics
  • Fast Data Ingest
  • In-memory Processing
  • Distributed Architecture
  • MySQL Compatible
  • Columnar Storage
  • Vector Search
  • Kafka

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.

SingleStore

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

Where each one falls short

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

SingleStore

  • High licensing costs that increase with data scale and cluster size
  • Eventual consistency in replication: secondary replicas may lag during high write loads
  • Complex operational setup requiring specialized knowledge for optimization
  • Vendor lock-in due to proprietary technology without open-source alternatives
  • Disorganized documentation and lack of online training resources

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

SingleStore

Free
  • Free Tier$0.99/month
    • Usage-based pricing
    • Limited resources

Apache Spark MLlib

Free

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

Which should you pick?

Choose SingleStore if

  • You need real-time analytics.
  • You want to start without paying.
  • You work on Cloud (SingleStoreDB Cloud), Self-Managed.
  • You also want fast data ingest.
SingleStore 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 SingleStore or Apache Spark MLlib better?
Neither clearly leads. SingleStore 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, SingleStore or Apache Spark MLlib?
SingleStore starts at Free and Apache Spark MLlib at Free.
Does SingleStore or Apache Spark MLlib run on more platforms?
SingleStore runs on Cloud (SingleStoreDB Cloud), Self-Managed. Apache Spark MLlib runs on Linux, macOS, Windows.
Can I use SingleStore for free?
Both have a free tier, so you can try either at no cost before committing.
What is SingleStore best used for?
SingleStore is most often used for transaction processing, data storage, application backend, reporting. Of those, transaction processing and data storage are not what Apache Spark MLlib is typically brought in for.
What can SingleStore do that Apache Spark MLlib cannot?
SingleStore covers Real-time Analytics, Fast Data Ingest, In-memory Processing, Distributed Architecture. Apache Spark MLlib covers DataFrame-based pipelines, Distributed algorithms, Alternating least squares, Feature transformers.

Answered from the vendors’ own pages

SingleStore: Does SingleStore offer a free tier?

Yes, SingleStore offers a free tier starting from $0.99/month with usage-based pricing. The free tier allows developers to evaluate the platform with limited resources before scaling to production workloads.

Source
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.

SingleStore: Can SingleStore handle both transactional and analytical workloads?

Yes, SingleStore is a hybrid transactional/analytical processing (HTAP) database that combines operational (OLTP) and analytical (OLAP) workloads in a single unified engine, eliminating the need for separate systems.

Source
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.

SingleStore: Does SingleStore integrate with Apache Spark?

Yes, SingleStore provides the Spark Connector 3.0 for bidirectional data integration with Apache Spark. The connector supports SQL, Python, Scala, Java, and R for data loading and extraction.

Source
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.

SingleStore: Can SingleStore ingest data from Kafka?

Yes, SingleStore supports high-throughput streaming ingestion from Apache Kafka and other sources, enabling millions of events per second without requiring ETL pipelines or data movement.

Source
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.

SingleStore: Is SingleStore available as cloud or self-managed?

SingleStore offers both deployment options: SingleStoreDB Cloud (managed service) and SingleStore Self-Managed for on-premises or private cloud deployments. The managed service handles infrastructure, scaling, and maintenance automatically.

Source
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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