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CloudCompare vs Apache Spark MLlib

CloudCompare logo

CloudCompare

CAD

Open source point cloud comparison and processing maintained largely by one person in their spare time

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: CloudCompare the project is administered by its creator in his spare time while he holds a full-time job elsewhere, so an organisation building a monitoring programme on it depends on one person with no obligation to continue; 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: CloudCompare covers Cloud to cloud distance, Apache Spark MLlib covers DataFrame-based pipelines.
  • Prices and features above were last checked on 1 September 2026.

Where they differ

Only the attributes on which CloudCompare and Apache Spark MLlib actually diverge.

Attributes where CloudCompare and Apache Spark MLlib differ
AttributeCloudCompareApache Spark MLlib
Pricing modelOpen source, no licence feeopen-source
PlatformsWindows, macOS, LinuxLinux, macOS, Windows
CategoryCADMachine 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 CloudCompare

  • Cloud to cloud distance
  • Cloud to mesh distance
  • Registration
  • Segmentation and cleaning
  • Plugin architecture
  • Format support
  • Command line mode

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.

CloudCompare

  • A monitoring engineer comparing quarterly laser scans of a retaining wall to quantify movement without buying a proprietary deformation packagenot Apache Spark MLlib
  • A heritage team registering dozens of terrestrial scans of a building into a single cloud before meshingnot Apache Spark MLlib
  • A geomorphologist measuring erosion between two drone-derived surfaces of a river banknot Apache Spark MLlib
  • A survey technician cleaning and subsampling a scan before delivering it to a client whose software cannot handle the full densitynot 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 CloudCompare
  • Feature engineering and model fitting in one job over tables already in the lake, avoiding an extract and a second copy of sensitive datanot CloudCompare
  • Batch scoring of hundreds of millions of rows on a schedule, where throughput matters and per-request latency does notnot CloudCompare
  • Organisations that already run and pay for Spark, where adding a modelling step is cheaper than introducing a second platformnot CloudCompare

Where each one falls short

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

CloudCompare

  • The project is administered by its creator in his spare time while he holds a full-time job elsewhere, so an organisation building a monitoring programme on it depends on one person with no obligation to continue
  • There is no commercial support contract from anyone, so a defect that blocks a deliverable is resolved by a GitHub issue and community goodwill rather than by an agreement
  • Processing is single machine and memory bound, so very large aerial lidar collections must be tiled manually and large jobs are limited by the workstation rather than scaled out
  • The interface is unforgiving and organised around the underlying data structures rather than around tasks, so competent surveyors routinely take weeks to become productive
  • Plugins vary widely in maintenance, and several useful ones originated in research projects that have since ended, so a workflow built around a specific plugin can break at the next release

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

CloudCompare

Free
  • CloudCompareFree
    • GNU General Public Licence
    • No licence fee and no usage limits
    • No commercial support contract exists

Apache Spark MLlib

Free

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

Which should you pick?

Choose CloudCompare if

  • You need cloud to cloud distance.
  • You want to start without paying.
  • You work on Windows, macOS, Linux.
  • You also want cloud to mesh distance.
CloudCompare 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 CloudCompare or Apache Spark MLlib better?
Neither clearly leads. CloudCompare 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, CloudCompare or Apache Spark MLlib?
CloudCompare starts at Free and Apache Spark MLlib at Free.
Does CloudCompare or Apache Spark MLlib run on more platforms?
CloudCompare runs on Windows, macOS, Linux. Apache Spark MLlib runs on Linux, macOS, Windows.
Can I use CloudCompare for free?
Both have a free tier, so you can try either at no cost before committing.
What is CloudCompare best used for?
CloudCompare is most often used for a monitoring engineer comparing quarterly laser scans of a retaining wall to quantify movement without buying a proprietary deformation package, a heritage team registering dozens of terrestrial scans of a building into a single cloud before meshing, a geomorphologist measuring erosion between two drone-derived surfaces of a river bank, a survey technician cleaning and subsampling a scan before delivering it to a client whose software cannot handle the full density. Of those, a monitoring engineer comparing quarterly laser scans of a retaining wall to quantify movement without buying a proprietary deformation package and a heritage team registering dozens of terrestrial scans of a building into a single cloud before meshing are not what Apache Spark MLlib is typically brought in for.
What can CloudCompare do that Apache Spark MLlib cannot?
CloudCompare covers Cloud to cloud distance, Cloud to mesh distance, Registration, Segmentation and cleaning. Apache Spark MLlib covers DataFrame-based pipelines, Distributed algorithms, Alternating least squares, Feature transformers.

Answered from the vendors’ own pages

CloudCompare: Who maintains CloudCompare?

Daniel Girardeau-Montaut, its creator, administers it in his own time alongside a full-time engineering job, with contributions from a community of users and research groups.

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.

CloudCompare: Can I buy support?

No. There is no vendor and no commercial support offering. Some geospatial consultancies know it well and can be hired, but they are not contracted to support the software itself.

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.

CloudCompare: Is it suitable for commercial deliverables?

It is widely used for them. The licence permits it and the algorithms are well regarded. The risk is operational, not legal: no support and no roadmap commitment.

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.

CloudCompare: Can it handle a full aerial lidar survey?

Not in one piece. It is memory bound on a single machine, so large collections need tiling or a dedicated lidar pipeline such as PDAL.

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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