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Databases · head to head

Couchbase vs Apache Spark MLlib

Couchbase logo

Couchbase

Databases

The modern database for enterprise 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: Couchbase the free tier is a single node with 8 GB of storage and forum support 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: Couchbase covers JSON Document Model, 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 Couchbase and Apache Spark MLlib actually diverge.

Attributes where Couchbase and Apache Spark MLlib differ
AttributeCouchbaseApache Spark MLlib
Pricing modelfreemiumopen-source
PlatformsLinux, Windows, Mac, Docker, WebLinux, 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 Couchbase

  • JSON Document Model
  • SQL++ Query
  • Full-text Search
  • Eventing
  • Analytics
  • Mobile Sync
  • Multi-dimensional Scaling
  • 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.

Couchbase

  • Running a distributed NoSQL document database as a managed servicenot Apache Spark MLlib
  • Mobile sync and offline first applications backed by a cloud databasenot 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 Couchbase
  • Feature engineering and model fitting in one job over tables already in the lake, avoiding an extract and a second copy of sensitive datanot Couchbase
  • Batch scoring of hundreds of millions of rows on a schedule, where throughput matters and per-request latency does notnot Couchbase
  • Organisations that already run and pay for Spark, where adding a modelling step is cheaper than introducing a second platformnot Couchbase

Where each one falls short

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

Couchbase

  • The free tier is a single node with 8 GB of storage and forum support only
  • Node rates are hourly and quoted as starting figures, from $0.15 an hour on Basic to $0.49 on Enterprise
  • The Developer Pro and Enterprise plans require 3 nodes, so the hourly rate multiplies before any usage
  • Backup storage is billed separately at $0.07 per GB a month, and analytics backups at $0.14
  • Support response time is a plan feature, at 8 hours on Developer Pro against 30 minutes on Enterprise
  • AI and analytics run as separately priced planes at up to $0.86 an hour per node

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

Couchbase

Free
  • CommunityFree
    • Full features
    • Community support
    • Self-managed
  • Capella FreeFree
    • Managed service
    • Limited resources
    • Cloud hosted

Apache Spark MLlib

Free

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

Which should you pick?

Choose Couchbase if

  • You need json document model.
  • You want to start without paying.
  • You work on Linux, Windows, Mac, Docker, Web.
  • You also want sql++ query.
Couchbase 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 Couchbase or Apache Spark MLlib better?
Neither clearly leads. Couchbase 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, Couchbase or Apache Spark MLlib?
Couchbase starts at Free and Apache Spark MLlib at Free.
Does Couchbase or Apache Spark MLlib run on more platforms?
Couchbase runs on Linux, Windows, Mac, Docker, Web. Apache Spark MLlib runs on Linux, macOS, Windows.
Can I use Couchbase for free?
Both have a free tier, so you can try either at no cost before committing.
What is Couchbase best used for?
Couchbase is most often used for running a distributed nosql document database as a managed service, mobile sync and offline first applications backed by a cloud database. Of those, running a distributed nosql document database as a managed service and mobile sync and offline first applications backed by a cloud database are not what Apache Spark MLlib is typically brought in for.
What can Couchbase do that Apache Spark MLlib cannot?
Couchbase covers JSON Document Model, SQL++ Query, Full-text Search, Eventing. Apache Spark MLlib covers DataFrame-based pipelines, Distributed algorithms, Alternating least squares, Feature transformers.

Answered from the vendors’ own pages

Couchbase: Does Couchbase offer a free tier?

Yes, Couchbase offers a free tier option. Users can start for free from the main website.

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.

Couchbase: How do I access Couchbase pricing details?

Couchbase maintains a dedicated pricing page, but detailed tier information and costs are not available on the homepage. You can visit the pricing page or contact their sales team.

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.

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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