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Energy · head to head

Cognite Data Fusion vs Semantic Kernel

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
Semantic Kernel logo

Semantic Kernel

Machine Learning

Model-agnostic SDK for AI orchestration

From
Free
Rated
-
Full reviewWebsite

The short version

  • Only Semantic Kernel has a free tier, so it costs nothing to try first.
  • 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.; Semantic Kernel steep learning curve for advanced features
  • They diverge on capability: Cognite Data Fusion covers Asset-centric data model, Semantic Kernel covers Multi-model support.
  • Prices and features above were last checked on 1 September 2026.

Where they differ

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

Attributes where Cognite Data Fusion and Semantic Kernel differ
AttributeCognite Data FusionSemantic Kernel
Starting priceOn requestFree
Pricing modelquoteOpen source, no pricing
Free tierNoYes
PlatformsWeb, CloudPython, .NET, Java
CategoryEnergyMachine Learning

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 Cognite Data Fusion

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

Only in Semantic Kernel

  • Multi-model support
  • Agent framework
  • Multi-agent systems
  • Plugin ecosystem
  • Vector database integration
  • Multimodal support
  • Local model support
  • Enterprise observability

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

Semantic Kernel

  • Building enterprise AI applications with LLM integrationnot Cognite Data Fusion
  • Creating multi-agent systems for complex workflowsnot Cognite Data Fusion
  • Developing AI-powered chatbots and assistantsnot Cognite Data Fusion
  • Implementing RAG systems with vector databasesnot 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.

Semantic Kernel

  • Steep learning curve for advanced features
  • Documentation focuses on Azure cloud services
  • Configuration complexity for multi-model scenarios
  • Requires understanding of AI/LLM concepts

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

Semantic Kernel

Free
  • Open SourceFree
    • MIT license
    • Full framework access
    • All language SDKs

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 Semantic Kernel if

  • You need multi-model support.
  • You want to start without paying.
  • You work on Python, .NET, Java.
  • You also want agent framework.
Semantic Kernel review

Questions people ask

Is Cognite Data Fusion or Semantic Kernel better?
Neither clearly leads. Cognite Data Fusion starts at On request and Semantic Kernel at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Cognite Data Fusion or Semantic Kernel?
Semantic Kernel has a free tier; the other does not. Paid plans start at On request for Cognite Data Fusion and Free for Semantic Kernel.
Does Cognite Data Fusion or Semantic Kernel run on more platforms?
Cognite Data Fusion runs on Web, Cloud. Semantic Kernel runs on Python, .NET, Java.
Can I use Semantic Kernel for free?
Yes. Semantic Kernel has a free tier, so you can try it without paying. Cognite Data Fusion starts at On request.
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 Semantic Kernel is typically brought in for.
What can Cognite Data Fusion do that Semantic Kernel cannot?
Cognite Data Fusion covers Asset-centric data model, Entity matching, P&ID parsing, 3D contextualisation. Semantic Kernel covers Multi-model support, Agent framework, Multi-agent systems, Plugin ecosystem.

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.

Semantic Kernel: What LLM providers does Semantic Kernel support?

Semantic Kernel supports OpenAI, Azure OpenAI, Hugging Face, Nvidia, and other providers through extensible model implementations.

Source
Cognite Data Fusion: How is it priced?

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

Semantic Kernel: Can I run Semantic Kernel locally?

Yes. Semantic Kernel supports local models through Ollama, LMStudio, and ONNX for complete data control and offline operation.

Source
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.

Semantic Kernel: Is Semantic Kernel free?

Yes. Semantic Kernel is MIT-licensed open source and completely free. You only pay for external LLM APIs you use.

Source
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.

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