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Cybersecurity · head to head

Passbolt vs Apache Spark MLlib

Passbolt logo

Passbolt

Cybersecurity

Open-source password manager for teams with self-hosted or cloud deployment

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: Passbolt pro Edition requires a minimum of 10 users, making it costlier for very small teams.; 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: Passbolt covers Password sharing and folders, 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 Passbolt and Apache Spark MLlib actually diverge.

Attributes where Passbolt and Apache Spark MLlib differ
AttributePassboltApache Spark MLlib
Platformsweb, windows, mac, linux, apiLinux, macOS, Windows
CategoryCybersecurityMachine Learning
FoundedUnknown1999

Identical on both: starting price (Free), pricing model (open-source), 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 Passbolt

  • Password sharing and folders
  • Groups and role-based access
  • Browser extensions and CLI
  • Open API
  • LDAP provisioning and SSO
  • Activity audit log

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.

Passbolt

  • Self-hosting a team password manager for data sovereigntynot Apache Spark MLlib
  • Provisioning vault access from an existing LDAP/AD directorynot Apache Spark MLlib
  • Sharing credentials across engineering or IT teams via folders and groupsnot Apache Spark MLlib
  • Automating credential retrieval through the CLI or open APInot 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 Passbolt
  • Feature engineering and model fitting in one job over tables already in the lake, avoiding an extract and a second copy of sensitive datanot Passbolt
  • Batch scoring of hundreds of millions of rows on a schedule, where throughput matters and per-request latency does notnot Passbolt
  • Organisations that already run and pay for Spark, where adding a modelling step is cheaper than introducing a second platformnot Passbolt

Where each one falls short

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

Passbolt

  • Pro Edition requires a minimum of 10 users, making it costlier for very small teams.
  • Community Edition lacks SSO and LDAP provisioning, which many organizations need for onboarding at scale.
  • Self-hosting the Community Edition requires infrastructure and maintenance effort compared to fully managed competitors.
  • Enterprise-tier support and HA consulting require custom, quote-based pricing rather than transparent rates.

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

Passbolt

Free
  • Community EditionFree
    • Unlimited users
    • Password sharing and folders
    • Role-based access control
  • Pro Edition$4.9/month
    • Minimum 10 users, billed annually
    • LDAP/AD provisioning
    • Single sign-on
  • Enterprise Edition$undefined/month
    • High availability and disaster recovery consulting
    • White-glove migration
    • Custom feature development

Apache Spark MLlib

Free

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

Which should you pick?

Choose Passbolt if

  • You need password sharing and folders.
  • You want to start without paying.
  • You work on web, windows, mac, linux, api.
  • You also want groups and role-based access.
Passbolt 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 Passbolt or Apache Spark MLlib better?
Neither clearly leads. Passbolt 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, Passbolt or Apache Spark MLlib?
Passbolt starts at Free and Apache Spark MLlib at Free.
Does Passbolt or Apache Spark MLlib run on more platforms?
Passbolt runs on web, windows, mac, linux, api. Apache Spark MLlib runs on Linux, macOS, Windows.
Can I use Passbolt for free?
Both have a free tier, so you can try either at no cost before committing.
What is Passbolt best used for?
Passbolt is most often used for self-hosting a team password manager for data sovereignty, provisioning vault access from an existing ldap/ad directory, sharing credentials across engineering or it teams via folders and groups, automating credential retrieval through the cli or open api. Of those, self-hosting a team password manager for data sovereignty and provisioning vault access from an existing ldap/ad directory are not what Apache Spark MLlib is typically brought in for.
What can Passbolt do that Apache Spark MLlib cannot?
Passbolt covers Password sharing and folders, Groups and role-based access, Browser extensions and CLI, Open API. Apache Spark MLlib covers DataFrame-based pipelines, Distributed algorithms, Alternating least squares, Feature transformers.

Answered from the vendors’ own pages

Passbolt: Is Passbolt free to use?

Yes. The Community Edition is free forever with unlimited users, including password sharing, folders, groups, role-based access, browser extensions, a CLI, and an open API.

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.

Passbolt: What does the Pro Edition cost and add?

Pro Edition costs $4.90 per user per month billed annually, with a 10-user minimum, and adds LDAP/AD provisioning, single sign-on, account recovery escrow, activity audit logs, and next-business-day support.

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.

Passbolt: What license is Passbolt distributed under?

All Passbolt editions, including Pro and Enterprise, are distributed under the AGPL v3 open-source license.

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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