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Machine Learning · head to head

Comet ML vs Apache Spark MLlib

Comet ML logo

Comet ML

Machine Learning

Platform for tracking, comparing, and optimizing ML experiments

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: Comet ML the free cloud tier caps data at 25,000 spans a month with 60 day retention; 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: Comet ML covers Experiment tracking, 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 Comet ML and Apache Spark MLlib actually diverge.

Attributes where Comet ML and Apache Spark MLlib differ
AttributeComet MLApache Spark MLlib
Pricing modelfreemiumopen-source
PlatformsWeb, Linux, Mac, WindowsLinux, macOS, Windows
Founded20171999

Identical on both: starting price (Free), free tier (Yes), user rating (Not yet rated), category (Machine Learning).

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 Comet ML

  • Experiment tracking
  • Code versioning
  • Model registry
  • Hyperparameter optimization
  • Production monitoring
  • PyTorch
  • TensorFlow
  • Keras

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.

Comet ML

  • LLM observability and monitoringnot Apache Spark MLlib
  • AI agent testing and debuggingnot Apache Spark MLlib
  • Experiment tracking for machine learningnot Apache Spark MLlib
  • Model registry and version managementnot Apache Spark MLlib
  • ML model training monitoringnot 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 Comet ML
  • Feature engineering and model fitting in one job over tables already in the lake, avoiding an extract and a second copy of sensitive datanot Comet ML
  • Batch scoring of hundreds of millions of rows on a schedule, where throughput matters and per-request latency does notnot Comet ML
  • Organisations that already run and pay for Spark, where adding a modelling step is cheaper than introducing a second platformnot Comet ML

Where each one falls short

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

Comet ML

  • The free cloud tier caps data at 25,000 spans a month with 60 day retention
  • Retention stays at 60 days even on the paid Pro plan, and extending it is a $29 per 100k spans add on
  • Overage on Pro is $5 per additional 100,000 spans
  • The free MLOps tier is a single user with 100 GB of storage and training hours governed by a fair usage policy
  • Pro MLOps is $19 per user per month and caps the team at 10 users

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

Comet ML

Free
  • Free CloudFree
    • Up to 10 team members
    • 25,000 spans per month
    • 60-day data retention
  • Pro Cloud$19/month
    • Up to 50 team members
    • 100,000 spans per month
    • 60-day data retention
  • MLOps FreeFree
    • 1 user with fair usage policy
    • Experiment tracking
    • Dataset management
  • MLOps Pro$19/user/month
    • Up to 10 users
    • 1,500 training hours included
    • 500GB storage included

Apache Spark MLlib

Free

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

Which should you pick?

Choose Comet ML if

  • You need experiment tracking.
  • You want to start without paying.
  • You work on Web, Linux, Mac, Windows.
  • You also want code versioning.
Comet ML 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 Comet ML or Apache Spark MLlib better?
Neither clearly leads. Comet ML 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, Comet ML or Apache Spark MLlib?
Comet ML starts at Free and Apache Spark MLlib at Free.
Does Comet ML or Apache Spark MLlib run on more platforms?
Comet ML runs on Web, Linux, Mac, Windows. Apache Spark MLlib runs on Linux, macOS, Windows.
Can I use Comet ML for free?
Both have a free tier, so you can try either at no cost before committing.
What is Comet ML best used for?
Comet ML is most often used for llm observability and monitoring, ai agent testing and debugging, experiment tracking for machine learning, model registry and version management. Of those, llm observability and monitoring and ai agent testing and debugging are not what Apache Spark MLlib is typically brought in for.
What can Comet ML do that Apache Spark MLlib cannot?
Comet ML covers Experiment tracking, Code versioning, Model registry, Hyperparameter optimization. Apache Spark MLlib covers DataFrame-based pipelines, Distributed algorithms, Alternating least squares, Feature transformers.

Answered from the vendors’ own pages

Comet ML: Does Comet.ml offer a free plan?

Yes, Comet.ml offers free tiers for both Opik (cloud observability) and MLOps platforms. Free Cloud Opik includes up to 10 team members and 25,000 spans/month. Free MLOps tier is limited to 1 user.

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.

Comet ML: How many team members can use the free Comet.ml tier?

Free Cloud supports up to 10 team members. The Pro Cloud plan supports up to 50 team members at $19/month.

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.

Comet ML: What is a span in Comet.ml pricing?

A span represents a single tracked operation such as model requests or function calls. Free Cloud tier includes 25,000 spans per month.

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.

Comet ML: Does Comet.ml offer academic pricing?

Yes, a free Pro plan is available for academic users; verification is required via signup.

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.

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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