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Novity vs scikit-learn

Novity logo

Novity

Energy

Hybrid physics and machine learning prognostics that estimate remaining useful life for process equipment

From
On request
Rated
-
Full reviewWebsite
scikit-learn logo

scikit-learn

Machine Learning

Machine learning in Python

From
Free
Rated
-
Full reviewWebsite

The short version

  • Only scikit-learn has a free tier, so it costs nothing to try first.
  • Each has a real cost: Novity novity is a small venture-backed company with a strategic investor rather than a profitable business, so continuity risk is real and the Tokyo Gas investment signals a likely eventual acquisition that would reset the roadmap.; scikit-learn no GPU acceleration by default; limited optional GPU support requires external arrays
  • They diverge on capability: Novity covers TruPrognostics engine, scikit-learn covers Classification algorithms.
  • Prices and features above were last checked on 1 September 2026.

Where they differ

Only the attributes on which Novity and scikit-learn actually diverge.

Attributes where Novity and scikit-learn differ
AttributeNovityscikit-learn
Starting priceOn requestFree
Pricing modelquoteUnknown
Free tierNoYes
PlatformsWeb, CloudPython, Linux, macOS, Windows
CategoryEnergyMachine Learning
FoundedUnknown2007

Identical on both: 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 Novity

  • TruPrognostics engine
  • Cold-start modelling
  • Fault mode diagnosis
  • Remaining useful life
  • Existing sensor reuse
  • Recommended actions
  • Historian connectors
  • Asset class libraries

Only in scikit-learn

  • Classification algorithms
  • Regression models
  • Clustering methods
  • Dimensionality reduction
  • Model selection
  • NumPy
  • SciPy
  • Pandas

What people use each for

The jobs each tool is most often brought in to do.

Novity

  • A gas processing plant that needs a defensible time-to-failure number before deferring a turnaroundnot scikit-learn
  • An LNG terminal with critical compressors and no run-to-failure history to train a conventional modelnot scikit-learn
  • A wastewater operator whose existing vibration alarms are ignored because they carry no severity or horizonnot scikit-learn
  • A generator operator supplying data centre load where an unplanned trip carries contractual penaltiesnot scikit-learn

scikit-learn

  • Machine learningnot Novity
  • Data analysisnot Novity
  • Model trainingnot Novity
  • Predictive analyticsnot Novity

Where each one falls short

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

Novity

  • Novity is a small venture-backed company with a strategic investor rather than a profitable business, so continuity risk is real and the Tokyo Gas investment signals a likely eventual acquisition that would reset the roadmap.
  • Physics-based models must be configured per equipment class, so each new asset type is an engineering engagement rather than a configuration screen, and rollout speed is limited by Novitys own capacity.
  • Prognostics depend on the quality and sampling rate of your historian data; plants recording ten-minute averages will not get useful remaining-useful-life estimates without new instrumentation.
  • Nothing about pricing is published and there is no self-service entry point, so evaluation always starts with a sales-led pilot on a handful of assets.
  • The deployment footprint is concentrated in oil and gas, LNG and water, so reference customers and pre-built asset models outside those industries are limited.

scikit-learn

  • No GPU acceleration by default; limited optional GPU support requires external arrays
  • Single-machine only; no built-in distributed computing across clusters
  • All datasets must fit entirely in RAM; no out-of-core learning
  • No production-grade deep learning; neural network support limited to basic multilayer perceptron
  • No reinforcement learning algorithms

Pricing, plan by plan

Novity

On request
  • TruPrognostics$undefined/year
    • Quoted per asset class and monitored equipment count
    • Model configuration and commissioning quoted as a project
    • Typically an annual subscription tied to a pilot then a rollout

scikit-learn

Free

No published plan breakdown. See the scikit-learn review.

Which should you pick?

Choose Novity if

  • You need truprognostics engine.
  • You work on Web, Cloud.
  • You also want cold-start modelling.
Novity review

Choose scikit-learn if

  • You need classification algorithms.
  • You want to start without paying.
  • You work on Python, Linux, macOS, Windows.
  • You also want regression models.
scikit-learn review

Questions people ask

Is Novity or scikit-learn better?
Neither clearly leads. Novity starts at On request and scikit-learn at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Novity or scikit-learn?
scikit-learn has a free tier; the other does not. Paid plans start at On request for Novity and Free for scikit-learn.
Does Novity or scikit-learn run on more platforms?
Novity runs on Web, Cloud. scikit-learn runs on Python, Linux, macOS, Windows.
Can I use scikit-learn for free?
Yes. scikit-learn has a free tier, so you can try it without paying. Novity starts at On request.
What is Novity best used for?
Novity is most often used for a gas processing plant that needs a defensible time-to-failure number before deferring a turnaround, an lng terminal with critical compressors and no run-to-failure history to train a conventional model, a wastewater operator whose existing vibration alarms are ignored because they carry no severity or horizon, a generator operator supplying data centre load where an unplanned trip carries contractual penalties. Of those, a gas processing plant that needs a defensible time-to-failure number before deferring a turnaround and an lng terminal with critical compressors and no run-to-failure history to train a conventional model are not what scikit-learn is typically brought in for.
What can Novity do that scikit-learn cannot?
Novity covers TruPrognostics engine, Cold-start modelling, Fault mode diagnosis, Remaining useful life. scikit-learn covers Classification algorithms, Regression models, Clustering methods, Dimensionality reduction.

Answered from the vendors’ own pages

Novity: What does Novity actually output?

A named failure mode and an estimated remaining useful life with a confidence band, not just an anomaly alert.

scikit-learn: Does scikit-learn support GPU acceleration?

Scikit-learn has no native GPU support by design to keep installation simple and cross-platform. Since 2023, a limited number of estimators can run on GPUs if input data is provided as PyTorch or CuPy arrays, but this requires additional setup.

Source
Novity: Do we need failure history to train it?

No. The physics component is what lets it produce useful prognostics on equipment with little or no run-to-failure data.

scikit-learn: Can scikit-learn handle datasets larger than RAM?

No. Scikit-learn is built on NumPy which requires all data to fit in memory, and NumPy operates on single-machine CPUs only. For very large datasets, consider Spark MLlib or distributed alternatives.

Source
Novity: Do we need new sensors?

Often not. It reads from your existing historian, but low sampling rates or missing measurements can require additional instrumentation.

scikit-learn: Is scikit-learn free to use commercially?

Yes. Scikit-learn is open source under the BSD license, which allows free commercial use, modification, and distribution.

Source
Novity: Who backs the company?

It was spun out of Xerox PARC and took a strategic investment from Acario Innovation, the venture arm of Tokyo Gas, in 2026.

scikit-learn: What neural network capabilities does scikit-learn have?

Scikit-learn includes only a basic multilayer perceptron (MLPClassifier and MLPRegressor) for simple feedforward networks. For serious deep learning, use PyTorch, TensorFlow, or Keras instead.

Source
scikit-learn: Does scikit-learn include natural language processing?

Scikit-learn has minimal NLP support limited to basic text feature extraction and vectorization. For comprehensive text processing, use spaCy or NLTK instead.

Source
scikit-learn: When was scikit-learn first released?

Scikit-learn's first public release was February 1, 2010, following its start as a Google Summer of Code project in 2007.

Source
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