Industrial DataOps and contextualisation platform; being acquired by Schneider Electric.

Cognite Data Fusion is industrial DataOps and contextualisation platform; being acquired by Schneider Electric., a digital twin platform for industrial teams. Pricing starts from Enterprise quote (no free tier). Best for digital twin foundations, industrial data integration, AI enablement.

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Cognite Data Fusion contextualises industrial data — from historians, ERP, maintenance systems, 3D models and documents — into a connected model that powers digital twins, analytics and AI applications. It is used heavily in oil & gas, power and manufacturing to break down data silos so engineers and AI tools can reason about assets as connected systems rather than disconnected tags. Ownership: on 30 June 2026 Schneider Electric announced a definitive agreement to acquire 100% of Cognite in an all-cash transaction valued at $3.1 billion, with Cognite to be integrated alongside AVEVA and reported within Schneider Electric's Industrial Automation business. Completion remains subject to regulatory approvals. Confirm the contracting entity and roadmap commitments before a long-term agreement.

Cognite Data Fusion at a glance

Category	Digital Twin
Starting price	Enterprise quote
Free tier	No
Best for	digital twin foundations, industrial data integration, AI enablement
Sectors	power-generation, chemical, steel
Website	www.cognite.com

Key features

• Contextualised industrial data model (DataOps)
• Foundation for digital twins and 3D asset models
• Connects historians, ERP, maintenance and documents
• APIs and SDKs for custom industrial apps
• Supports AI / analytics on unified data

How to evaluate Cognite Data Fusion

Sector fit: Cognite Data Fusion is tagged as strong in power-generation, chemical. This typically means the platform has pre-built workflows, data models, and integrations tailored to those industries. If you operate in one of these sectors, that domain knowledge can dramatically reduce implementation time and ramp-up. If you're in a different sector, verify that the core capabilities still apply and ask the vendor about customization.

Pricing and TCO: Cognite Data Fusion starts with pricing from Enterprise quote. Industrial software pricing is rarely simple — most vendors quote per asset, per site, or per concurrent user after a scoping call. Budget for implementation and training, not just licensing. Most industrial deployments see ROI within 6–18 months if selected for the right use case.

Implementation and adoption: Rolling out Digital Twin like Cognite Data Fusion typically involves data import (historian integration), role and permission setup, and user training. Start with a pilot on a few critical assets. Many plants find that a 4–8 week pilot with 2–3 assets clarifies the ROI and what customization is truly needed vs nice-to-have.

Next steps: Evaluate Cognite Data Fusion against your specific pain point (downtime? energy? compliance? visibility?). Compare it head-to-head with 1–2 alternatives that target the same use case. Request a trial or pilot. Verify that support, integrations and data export are acceptable. The 'best' platform depends on your plant's technical maturity, integration needs and budget.

What Cognite Data Fusion actually is

Cognite describes Data Fusion as an industrial DataOps platform that streams industrial data into a structured, flexible and contextualised knowledge graph, running in the cloud with a modular design that exposes data through workspaces, APIs and SDKs.

The product framing on Cognite's own site is three moves. Unify — break down IT, OT and ET silos into a single source of truth. Contextualise — bring time series, P&IDs, 3D models and other industrial data into one model with AI-assisted search. Activate — use that foundation for industrial tools and AI solutions across assets, sites and the enterprise.

The important word is contextualise. CDF is not primarily a place to store data — most sites already have historians and data lakes for that. It is a layer that establishes relationships between data that arrived from different systems and never knew about each other.

Where it sits relative to what you already own

Layer	Typical incumbent	What CDF adds
Process data capture	Historian (PI, Proficy, Aspen)	Nothing — CDF consumes it, it does not replace the historian
Maintenance records	CMMS / EAM	Joins work orders to the assets and signals they relate to
Engineering documents	Document management, CAD	Extracts and links P&ID and 3D model content to tags
Analytics and AI	Point tools, notebooks	A shared contextualised foundation instead of per-project data wrangling

This is why comparisons that place CDF against a historian mis-frame the decision. The realistic question is not "CDF or the historian" but "is our data-joining problem large enough to justify a dedicated layer".

Cognite's own implementation sequence tells you where the work is

Worth noting what the vendor's documentation puts first. Cognite's implementation overview does not begin with connecting data sources. It begins with data management: define governance policies, appoint a CDF administrator to work with IT so the platform follows the organisation's security practices, and connect CDF to the identity provider so access uses existing user identities.

That ordering is a realistic signal about where projects stall. The technical ingestion is rarely the hard part. Agreeing who owns which data, who may see it, and what the canonical asset naming is — that is the part that consumes the timeline, and it is organisational rather than technical.

Where it disappoints

• It inherits your naming inconsistencies. Contextualisation joins systems that disagree. Where the historian, CMMS and control system use different tag conventions, reconciliation becomes the project, and it is rarely fully scoped up front.
• It does not improve the underlying data. A mis-scaled transmitter produces a well-contextualised wrong number. Instrumentation quality still sets the ceiling.
• It needs a consumer. A contextualised foundation with no analytics team, no AI use case and no operational workflow attached is infrastructure without a return.
• Governance first is not optional. Teams that skip to ingestion generally rebuild access control later, at greater cost.

Signals worth noting for AI-era evaluation

Two things on Cognite's documentation are unusual enough to mention, because they indicate how the vendor expects the platform to be consumed. Cognite publishes an llms.txt documentation index explicitly for machine discovery, and offers an MCP server for its documentation. Whatever one thinks of the product, that is a vendor building for agent and assistant access rather than only for human readers.

For a buyer, the practical read is that programmatic and assistant-driven access to the platform's own documentation is a first-class concern for this vendor — a reasonable proxy for how seriously its APIs are treated.

What this page does not claim

No pricing — Cognite does not publish it. No ratings, no rankings, no performance benchmarks, and no customer outcome figures. Statements above are taken from Cognite's own product page and documentation; where the vendor does not state something, it is not asserted here.

Sources

• Cognite — About Cognite Data Fusion (CDF), product documentation
• Cognite — Cognite Data Fusion - simple access to complex industrial data, product page

Vendor pages retrieved 2 September 2026. Product scope and packaging change; check current documentation before specifying.

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