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Duplicati vs Apache Spark MLlib

Duplicati logo

Duplicati

File Storage

Free open-source backup with encryption

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: Duplicati no managed service or commercial support; 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: Duplicati covers AES-256 encryption, 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 Duplicati and Apache Spark MLlib actually diverge.

Attributes where Duplicati and Apache Spark MLlib differ
AttributeDuplicatiApache Spark MLlib
Pricing modelUnknownopen-source
PlatformsWindows, macOS, LinuxLinux, macOS, Windows
CategoryFile StorageMachine Learning
Founded20081999

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 Duplicati

  • AES-256 encryption
  • Incremental backup
  • Deduplication
  • Multiple cloud backends
  • Compression
  • Web interface
  • AWS S3
  • Azure

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.

Duplicati

  • Data protectionnot Apache Spark MLlib
  • Disaster recoverynot Apache Spark MLlib
  • Business continuitynot Apache Spark MLlib
  • Ransomware protectionnot Apache Spark MLlib
  • Compliancenot 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 Duplicati
  • Feature engineering and model fitting in one job over tables already in the lake, avoiding an extract and a second copy of sensitive datanot Duplicati
  • Batch scoring of hundreds of millions of rows on a schedule, where throughput matters and per-request latency does notnot Duplicati
  • Organisations that already run and pay for Spark, where adding a modelling step is cheaper than introducing a second platformnot Duplicati

Where each one falls short

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

Duplicati

  • No managed service or commercial support
  • Relies on community support
  • No enterprise features

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

Duplicati

Free

No published plan breakdown. See the Duplicati review.

Apache Spark MLlib

Free

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

Which should you pick?

Choose Duplicati if

  • You need aes-256 encryption.
  • You want to start without paying.
  • You work on Windows, macOS, Linux.
  • You also want incremental backup.
Duplicati 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 Duplicati or Apache Spark MLlib better?
Neither clearly leads. Duplicati 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, Duplicati or Apache Spark MLlib?
Duplicati starts at Free and Apache Spark MLlib at Free.
Does Duplicati or Apache Spark MLlib run on more platforms?
Duplicati runs on Windows, macOS, Linux. Apache Spark MLlib runs on Linux, macOS, Windows.
Can I use Duplicati for free?
Both have a free tier, so you can try either at no cost before committing.
What is Duplicati best used for?
Duplicati is most often used for data protection, disaster recovery, business continuity, ransomware protection. Of those, data protection and disaster recovery are not what Apache Spark MLlib is typically brought in for.
What can Duplicati do that Apache Spark MLlib cannot?
Duplicati covers AES-256 encryption, Incremental backup, Deduplication, Multiple cloud backends. Apache Spark MLlib covers DataFrame-based pipelines, Distributed algorithms, Alternating least squares, Feature transformers.

Answered from the vendors’ own pages

Duplicati: Is Duplicati free?

Yes. Duplicati is completely free and open-source under the LGPL license. There are no premium tiers, trials, or limitations.

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.

Duplicati: What does Duplicati support?

Duplicati supports zero-trust, fully encrypted backups to local storage, network drives, and cloud services. It includes deduplication and incremental backups.

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.

Duplicati: What are the platforms?

Duplicati runs on Windows, macOS, and Linux. It can back up data to local storage, network drives, or cloud providers.

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.

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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