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Energy · head to head

Cognite Data Fusion vs Novity

Cognite Data Fusion logo

Cognite Data Fusion

Energy

Industrial data platform that contextualises OT, IT and engineering data into an asset-centric knowledge graph

From
On request
Rated
-
Full reviewWebsite
Novity logo

Novity

Energy

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

From
On request
Rated
-
Full reviewWebsite

The short version

  • Each has a real cost: Cognite Data Fusion the platform is only as good as the contextualisation work, and that mapping effort is a consulting project that regularly costs more than the first-year subscription.; 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.
  • They diverge on capability: Cognite Data Fusion covers Asset-centric data model, Novity covers TruPrognostics engine.
  • Prices and features above were last checked on 1 September 2026.

Where they differ

Only the attributes on which Cognite Data Fusion and Novity actually diverge.

Attributes where Cognite Data Fusion and Novity differ
AttributeCognite Data FusionNovity

Identical on both: starting price (On request), pricing model (quote), free tier (No), platforms (Web, Cloud), user rating (Not yet rated), category (Energy).

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 Cognite Data Fusion

  • Asset-centric data model
  • Entity matching
  • P&ID parsing
  • 3D contextualisation
  • Cognite Atlas AI
  • Data workflows
  • Open SDKs
  • Extractors

Only in Novity

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

What people use each for

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

Cognite Data Fusion

  • An operator that wants engineers to find the drawing, the sensor trend and the last work order for a valve from one searchnot Novity
  • A company standardising asset data across sites so an analytics team can build once and deploy to many plantsnot Novity
  • An upstream operator building a production-optimisation model that needs sensor data joined to equipment metadatanot Novity
  • A team replacing a stalled internal data-lake project where nobody could reconstruct what the tag names meantnot Novity

Novity

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

Where each one falls short

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

Cognite Data Fusion

  • The platform is only as good as the contextualisation work, and that mapping effort is a consulting project that regularly costs more than the first-year subscription.
  • Pricing is consumption-based and unpublished, so costs move with data volume and usage patterns you cannot forecast well until a year in.
  • It does not replace your historian, your ERP or your maintenance system, so Cognite is an additional recurring cost layered on systems you still pay for.
  • The reference base and data model lean heavily towards Norwegian and wider oil, gas and process industries; discrete manufacturing fit is weaker and the local partner network thinner outside energy.
  • Getting value out requires in-house Python and data engineering skill; organisations without a data team end up dependent on Cognite professional services for every new use case.

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.

Pricing, plan by plan

Cognite Data Fusion

On request
  • Cognite Data Fusion$undefined/year
    • Consumption-based pricing on data volume, compute and users
    • Available through cloud marketplaces with private offers
    • Contextualisation and onboarding quoted as a separate engagement

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

Which should you pick?

Choose Cognite Data Fusion if

  • You need asset-centric data model.
  • You work on Web, Cloud.
  • You also want entity matching.
Cognite Data Fusion review

Choose Novity if

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

Questions people ask

Is Cognite Data Fusion or Novity better?
Neither clearly leads. Cognite Data Fusion starts at On request and Novity at On request, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Cognite Data Fusion or Novity?
Cognite Data Fusion starts at On request and Novity at On request.
Does Cognite Data Fusion or Novity run on more platforms?
Both run on Web, Cloud, so platform support will not decide this one for you.
What is Cognite Data Fusion best used for?
Cognite Data Fusion is most often used for an operator that wants engineers to find the drawing, the sensor trend and the last work order for a valve from one search, a company standardising asset data across sites so an analytics team can build once and deploy to many plants, an upstream operator building a production-optimisation model that needs sensor data joined to equipment metadata, a team replacing a stalled internal data-lake project where nobody could reconstruct what the tag names meant. Of those, an operator that wants engineers to find the drawing, the sensor trend and the last work order for a valve from one search and a company standardising asset data across sites so an analytics team can build once and deploy to many plants are not what Novity is typically brought in for.
What can Cognite Data Fusion do that Novity cannot?
Cognite Data Fusion covers Asset-centric data model, Entity matching, P&ID parsing, 3D contextualisation. Novity covers TruPrognostics engine, Cold-start modelling, Fault mode diagnosis, Remaining useful life.

Answered from the vendors’ own pages

Cognite Data Fusion: Is Cognite a historian?

No. It reads from historians such as PI System and adds context. You still need the historian underneath.

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.

Cognite Data Fusion: How is it priced?

Consumption-based on data, compute and users, quoted per customer. Nothing is published.

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.

Cognite Data Fusion: How long does a deployment take?

First useful graph in a few months is realistic; full plant contextualisation across a site is typically a year or more.

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.

Cognite Data Fusion: Can we do the contextualisation ourselves?

Technically yes, the SDKs and matching tools are open, but most customers use Cognite or a partner for the first site.

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.

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