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Databases · head to head

Cockroach Labs vs Apache Spark MLlib

Cockroach Labs logo

Cockroach Labs

Databases

The cloud-native distributed SQL database

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: Cockroach Labs basic and Standard tiers limited to AWS and GCP (select regions only); 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: Cockroach Labs covers Distributed SQL, 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 Cockroach Labs and Apache Spark MLlib actually diverge.

Attributes where Cockroach Labs and Apache Spark MLlib differ
AttributeCockroach LabsApache Spark MLlib
Pricing modelfreemiumopen-source
PlatformsAWS, GCP, AzureLinux, macOS, Windows
CategoryDatabasesMachine Learning
Founded20151999

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 Cockroach Labs

  • Distributed SQL
  • Automatic Sharding
  • Multi-region Replication
  • Geo-partitioning
  • ACID Transactions
  • Horizontal Scaling
  • Survivability
  • PostgreSQL Compatibility

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.

Cockroach Labs

  • Distributed SQL database for scalable applicationsnot Apache Spark MLlib
  • Multi-region deployment and failovernot 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 Cockroach Labs
  • Feature engineering and model fitting in one job over tables already in the lake, avoiding an extract and a second copy of sensitive datanot Cockroach Labs
  • Batch scoring of hundreds of millions of rows on a schedule, where throughput matters and per-request latency does notnot Cockroach Labs
  • Organisations that already run and pay for Spark, where adding a modelling step is cheaper than introducing a second platformnot Cockroach Labs

Where each one falls short

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

Cockroach Labs

  • Basic and Standard tiers limited to AWS and GCP (select regions only)
  • Azure support restricted to Advanced tier only
  • Basic tier limited to 50 million RUs and 10 GiB storage per month
  • Advanced tier starts at $0.60/hour for 4 vCPUs minimum
  • 3 TiB maximum storage on Basic and Standard tiers, 10 TiB per node on Advanced

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

Cockroach Labs

Free

No published plan breakdown. See the Cockroach Labs review.

Apache Spark MLlib

Free

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

Which should you pick?

Choose Cockroach Labs if

  • You need distributed sql.
  • You want to start without paying.
  • You work on AWS, GCP, Azure.
  • You also want automatic sharding.
Cockroach Labs 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 Cockroach Labs or Apache Spark MLlib better?
Neither clearly leads. Cockroach Labs 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, Cockroach Labs or Apache Spark MLlib?
Cockroach Labs starts at Free and Apache Spark MLlib at Free.
Does Cockroach Labs or Apache Spark MLlib run on more platforms?
Cockroach Labs runs on AWS, GCP, Azure. Apache Spark MLlib runs on Linux, macOS, Windows.
Can I use Cockroach Labs for free?
Both have a free tier, so you can try either at no cost before committing.
What is Cockroach Labs best used for?
Cockroach Labs is most often used for distributed sql database for scalable applications, multi-region deployment and failover. Of those, distributed sql database for scalable applications and multi-region deployment and failover are not what Apache Spark MLlib is typically brought in for.
What can Cockroach Labs do that Apache Spark MLlib cannot?
Cockroach Labs covers Distributed SQL, Automatic Sharding, Multi-region Replication, Geo-partitioning. Apache Spark MLlib covers DataFrame-based pipelines, Distributed algorithms, Alternating least squares, Feature transformers.

Answered from the vendors’ own pages

Cockroach Labs: What does the free Basic tier include?

The Basic tier is free and includes 50 million RUs and 10 GiB storage per month, with a maximum compute of 30K RU/sec. No credit card is required to start.

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.

Cockroach Labs: How much do the paid tiers cost?

Standard tier starts at $0.18 per hour for 2 vCPUs, and Advanced tier starts at $0.60 per hour for 4 vCPUs. Both tiers support additional resources with higher costs for increased compute and storage.

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.

Cockroach Labs: What are the storage limits for each tier?

Basic plan supports up to 3 TiB storage, Standard tier up to 3 TiB, and Advanced tier up to 10 TiB per node with unlimited total storage.

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.

Cockroach Labs: Can I get trial credits before paying?

Yes, new users can try CockroachDB free with $400 in credits. The Basic and Standard plans do not require a credit card to start.

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.

Cockroach Labs: Do legacy contracts have different pricing?

Customers with annual or multi-year contracts entered before December 1, 2024 can view legacy pricing terms on a separate page, which apply until their contract renewal date.

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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