Softwr

Energy · head to head

Cognite Data Fusion vs LangGraph

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
LangGraph logo

LangGraph

AI

Agent runtime and orchestration framework

From
Free
Rated
-
Full reviewWebsite

The short version

  • Only LangGraph 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.; LangGraph steeper learning curve compared to high-level abstractions
  • They diverge on capability: Cognite Data Fusion covers Asset-centric data model, LangGraph covers Human-in-the-loop controls.
  • Prices and features above were last checked on 1 September 2026.

Where they differ

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

Attributes where Cognite Data Fusion and LangGraph differ
AttributeCognite Data FusionLangGraph
Starting priceOn requestFree
Pricing modelquoteOpen source and free, with optional managed platform
Free tierNoYes
PlatformsWeb, CloudPython, JavaScript, Web
CategoryEnergyAI

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 LangGraph

  • Human-in-the-loop controls
  • Customizable workflows
  • Memory management
  • Token-by-token streaming
  • Low-level control
  • Multi-agent support

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

LangGraph

  • Building production AI agents with auditable workflowsnot Cognite Data Fusion
  • Designing multi-agent systems for complex tasksnot Cognite Data Fusion
  • Implementing human oversight in autonomous systemsnot Cognite Data Fusion
  • Creating reliable agentic applications at scalenot 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.

LangGraph

  • Steeper learning curve compared to high-level abstractions
  • Requires understanding of graph-based architecture
  • Debugging complex workflows can be challenging
  • Not optimized for simple, one-off use cases

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

LangGraph

Free
  • Open SourceFree
    • MIT-licensed framework
    • Self-hosted deployment
    • Full API access
  • LangGraph Platform$35/month
    • Managed hosting
    • Enterprise deployment
    • Integrated tooling

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 LangGraph if

  • You need human-in-the-loop controls.
  • You want to start without paying.
  • You work on Python, JavaScript, Web.
  • You also want customizable workflows.
LangGraph review

Questions people ask

Is Cognite Data Fusion or LangGraph better?
Neither clearly leads. Cognite Data Fusion starts at On request and LangGraph at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Cognite Data Fusion or LangGraph?
LangGraph has a free tier; the other does not. Paid plans start at On request for Cognite Data Fusion and Free for LangGraph.
Does Cognite Data Fusion or LangGraph run on more platforms?
Cognite Data Fusion runs on Web, Cloud. LangGraph runs on Python, JavaScript, Web.
Can I use LangGraph for free?
Yes. LangGraph 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 LangGraph is typically brought in for.
What can Cognite Data Fusion do that LangGraph cannot?
Cognite Data Fusion covers Asset-centric data model, Entity matching, P&ID parsing, 3D contextualisation. LangGraph covers Human-in-the-loop controls, Customizable workflows, Memory management, Token-by-token streaming.

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.

LangGraph: Is LangGraph free to use?

Yes. The core LangGraph framework is MIT-licensed and completely free. You only pay if you use the optional managed LangGraph Platform for hosting.

Source
Cognite Data Fusion: How is it priced?

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

LangGraph: What programming languages does LangGraph support?

LangGraph provides first-class support for Python and JavaScript, enabling cross-platform agent development.

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.

LangGraph: Can I deploy LangGraph in production?

Yes. LangGraph can be self-hosted on your own infrastructure or deployed through LangGraph Platform with enterprise support and SLA guarantees.

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.

Share

Related pages

More on Cognite Data Fusion

More on LangGraph

Keep looking

Other head to heads