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AI · head to head

Gumloop vs Apache Spark MLlib

Gumloop logo

Gumloop

AI

AI infrastructure platform for building and deploying autonomous agents

From
Free
Free plan
Yes
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
Free plan
Yes
Full reviewWebsite

The short version

  • Each has a real cost: Gumloop pro plan at $37/month may be limiting for enterprises considering spending caps; 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: Gumloop covers No-code agent builder, 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 Gumloop and Apache Spark MLlib actually diverge.

Attributes where Gumloop and Apache Spark MLlib differ
AttributeGumloopApache Spark MLlib
Pricing modelTiered subscription with usage creditsopen-source
PlatformsWeb, Slack, Microsoft Teams, GmailLinux, macOS, Windows
CategoryAIMachine 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 Gumloop

  • No-code agent builder
  • 300+ app integrations
  • Company Brain
  • Self-improving agents
  • Multi-channel deployment
  • Role-based access
  • Budget controls
  • Audit logging

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.

Gumloop

  • CRM management and sales pipeline analysisnot Apache Spark MLlib
  • Lead qualification and sales outreach automationnot Apache Spark MLlib
  • Meeting preparation and call analysisnot Apache Spark MLlib
  • Data analysis and automated reportingnot Apache Spark MLlib
  • Content creation and knowledge base updatesnot 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 Gumloop
  • Feature engineering and model fitting in one job over tables already in the lake, avoiding an extract and a second copy of sensitive datanot Gumloop
  • Batch scoring of hundreds of millions of rows on a schedule, where throughput matters and per-request latency does notnot Gumloop
  • Organisations that already run and pay for Spark, where adding a modelling step is cheaper than introducing a second platformnot Gumloop

Where each one falls short

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

Gumloop

  • Pro plan at $37/month may be limiting for enterprises considering spending caps
  • Requires enterprise plan for advanced security controls needed by large organizations
  • No permanent free tier beyond 14-day trial
  • Company Brain integration depends on having all tools connected

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

Gumloop

Free
  • Free TrialFree
    • 14-day free trial of Pro plan
  • Enterprise$undefined/custom
    • Custom credit allocation
    • 35+ models plus custom proxy
    • Org-wide security controls

Apache Spark MLlib

Free

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

Which should you pick?

Choose Gumloop if

  • You need no-code agent builder.
  • You want to start without paying.
  • You work on Web, Slack, Microsoft Teams, Gmail.
  • You also want 300+ app integrations.
Gumloop 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 Gumloop or Apache Spark MLlib better?
Neither clearly leads. Gumloop 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, Gumloop or Apache Spark MLlib?
Gumloop starts at Free and Apache Spark MLlib at Free.
Does Gumloop or Apache Spark MLlib run on more platforms?
Gumloop runs on Web, Slack, Microsoft Teams, Gmail. Apache Spark MLlib runs on Linux, macOS, Windows.
Can I use Gumloop for free?
Both have a free tier, so you can try either at no cost before committing.
What is Gumloop best used for?
Gumloop is most often used for crm management and sales pipeline analysis, lead qualification and sales outreach automation, meeting preparation and call analysis, data analysis and automated reporting. Of those, crm management and sales pipeline analysis and lead qualification and sales outreach automation are not what Apache Spark MLlib is typically brought in for.
What can Gumloop do that Apache Spark MLlib cannot?
Gumloop covers No-code agent builder, 300+ app integrations, Company Brain, Self-improving agents. Apache Spark MLlib covers DataFrame-based pipelines, Distributed algorithms, Alternating least squares, Feature transformers.

Answered from the vendors’ own pages

Gumloop: Can non-technical users build agents with Gumloop?

Yes, Gumloop is designed for domain experts without programming skills. The no-code interface enables anyone who understands a task to automate it without coding.

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.

Gumloop: How many applications can Gumloop integrate with?

Gumloop integrates with 300+ business tools including Salesforce, HubSpot, Slack, Gmail, GitHub, Jira, and others. The Company Brain feature unifies data from all connected applications.

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.

Gumloop: What security features does Enterprise include?

Enterprise plans offer SCIM and SAML support, SOC 2 Type II compliance, custom MCP server hosting, advanced admin features, audit logging, and VPC deployments for data isolation.

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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