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Energy · head to head

Cognite Data Fusion vs Sight Machine

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
Sight Machine logo

Sight Machine

Manufacturing

Enterprise manufacturing data platform that builds a plant data model rather than a dashboard

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.; Sight Machine onboarding is a data engineering project measured in months per plant, so value arrives long after the contract starts and the internal sponsor needs the patience and budget to survive that gap.
  • They diverge on capability: Cognite Data Fusion covers Asset-centric data model, Sight Machine covers Unified plant data model.
  • Prices and features above were last checked on 1 September 2026.

Where they differ

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

Attributes where Cognite Data Fusion and Sight Machine differ
AttributeCognite Data FusionSight Machine
PlatformsWeb, CloudWeb, API
CategoryEnergyManufacturing

Identical on both: starting price (On request), pricing model (quote), free tier (No), 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 Cognite Data Fusion

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

Only in Sight Machine

  • Unified plant data model
  • Broad ingestion
  • Cross plant benchmarking
  • Root cause analysis
  • Natural language querying
  • Cloud data platform delivery

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 Sight Machine
  • A company standardising asset data across sites so an analytics team can build once and deploy to many plantsnot Sight Machine
  • An upstream operator building a production-optimisation model that needs sensor data joined to equipment metadatanot Sight Machine
  • A team replacing a stalled internal data-lake project where nobody could reconstruct what the tag names meantnot Sight Machine

Sight Machine

  • A corporate operations team that cannot compare plant performance because every site defines a stoppage differentlynot Cognite Data Fusion
  • A manufacturer whose historian data is plentiful but has never been modelled into anything a business can querynot Cognite Data Fusion
  • A group standardising manufacturing reporting into a cloud data warehouse alongside finance and supply chain datanot Cognite Data Fusion
  • A quality organisation investigating a defect that appears at several plants with different equipmentnot 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.

Sight Machine

  • Onboarding is a data engineering project measured in months per plant, so value arrives long after the contract starts and the internal sponsor needs the patience and budget to survive that gap.
  • It assumes the instrumentation already exists, so a plant whose machines produce no usable data gets nothing from a data platform and needs to solve connectivity first with a different class of product.
  • Pricing scales with plants and data volume and is never published, which puts it out of reach for single site manufacturers regardless of how relevant the capability sounds.
  • It is not an MES and does not control or execute anything, so it sits on top of the operational systems and adds a layer to maintain rather than replacing one.
  • Success depends on a central team that owns definitions and enforces them, and in groups where plants retain autonomy the standardisation the platform provides gets quietly ignored at site level.

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

Sight Machine

On request
  • Sight Machine$undefined/year
    • Enterprise manufacturing data platform
    • Per plant onboarding and modelling services
    • Cross site analytics and benchmarking

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 Sight Machine if

  • You need unified plant data model.
  • You work on Web, API.
  • You also want broad ingestion.
Sight Machine review

Questions people ask

Is Cognite Data Fusion or Sight Machine better?
Neither clearly leads. Cognite Data Fusion starts at On request and Sight Machine 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 Sight Machine?
Cognite Data Fusion starts at On request and Sight Machine at On request.
Does Cognite Data Fusion or Sight Machine run on more platforms?
Cognite Data Fusion runs on Web, Cloud. Sight Machine runs on Web, API.
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 Sight Machine is typically brought in for.
What can Cognite Data Fusion do that Sight Machine cannot?
Cognite Data Fusion covers Asset-centric data model, Entity matching, P&ID parsing, 3D contextualisation. Sight Machine covers Unified plant data model, Broad ingestion, Cross plant benchmarking, Root cause analysis.

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.

Sight Machine: Is this an OEE product?

It can produce OEE, but buying it for OEE alone is expensive. The reason to buy it is cross plant data standardisation.

Cognite Data Fusion: How is it priced?

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

Sight Machine: Does it need a historian?

It reads from historians, control systems and MES. Some source of machine data is a prerequisite.

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.

Sight Machine: How long does a plant take to onboard?

Plan in months per plant, depending on how many sources and how inconsistent the existing definitions are.

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.

Sight Machine: Who is the buyer?

A corporate manufacturing, quality or digital function. It is rarely a plant level purchase.

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