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Energy · head to head

Cognite Data Fusion vs Materialize

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
Materialize logo

Materialize

Databases

Live context layer for AI agents using real-time SQL transformations

From
Free
Rated
-
Full reviewWebsite

The short version

  • Only Materialize 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.; Materialize community tier limited to 24GB memory, restricting production deployments
  • They diverge on capability: Cognite Data Fusion covers Asset-centric data model, Materialize covers Real-time Data Ingestion.
  • Prices and features above were last checked on 1 September 2026.

Where they differ

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

Attributes where Cognite Data Fusion and Materialize differ
AttributeCognite Data FusionMaterialize
Starting priceOn requestFree
Pricing modelquoteUsage-based compute credits with volume discounts for annual prepay
Free tierNoYes
PlatformsWeb, CloudCloud, Self-Managed, Local
CategoryEnergyDatabases
FoundedUnknown2019

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 Materialize

  • Real-time Data Ingestion
  • SQL Transformations
  • Incremental Computation
  • Context Graph
  • Multiple Deployment Options
  • Agent Integration

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

Materialize

  • Building AI agent context layers from operational databasesnot Cognite Data Fusion
  • Creating event-driven applications without message queue complexitynot Cognite Data Fusion
  • Powering real-time analytics dashboards for user-facing applicationsnot Cognite Data Fusion
  • Simplifying vector search indexing pipelinesnot 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.

Materialize

  • Community tier limited to 24GB memory, restricting production deployments
  • Compute credit pricing requires predicting usage patterns
  • Learning SQL transformation models adds complexity vs pre-built solutions
  • Self-managed deployments require operational expertise

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

Materialize

Free
  • CommunityFree
    • Free forever
    • Up to 24GB memory and 48GB disk
    • Community Slack support
  • Cloud On-Demand$1.5/compute-credit
    • Monthly billing
    • Pay-as-you-go
    • Chatbot and helpdesk support
  • Cloud Capacity$1.5/compute-credit
    • Annual prepaid pricing
    • Volume discounts available
    • Dedicated account team
  • Enterprise LicenseFree
    • Unlimited scale for production
    • Dedicated account team
    • Priority engineer support

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 Materialize if

  • You need real-time data ingestion.
  • You want to start without paying.
  • You work on Cloud, Self-Managed, Local.
  • You also want sql transformations.
Materialize review

Questions people ask

Is Cognite Data Fusion or Materialize better?
Neither clearly leads. Cognite Data Fusion starts at On request and Materialize at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Cognite Data Fusion or Materialize?
Materialize has a free tier; the other does not. Paid plans start at On request for Cognite Data Fusion and Free for Materialize.
Does Cognite Data Fusion or Materialize run on more platforms?
Cognite Data Fusion runs on Web, Cloud. Materialize runs on Cloud, Self-Managed, Local.
Can I use Materialize for free?
Yes. Materialize 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 Materialize is typically brought in for.
What can Cognite Data Fusion do that Materialize cannot?
Cognite Data Fusion covers Asset-centric data model, Entity matching, P&ID parsing, 3D contextualisation. Materialize covers Real-time Data Ingestion, SQL Transformations, Incremental Computation, Context Graph.

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.

Materialize: What is included in the free Community tier?

The Community tier is free forever for deployments up to 24GB memory and 48GB disk with community Slack support and self-service setup.

Source
Cognite Data Fusion: How is it priced?

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

Materialize: What are the storage and networking costs?

Cloud plans charge for storage at $0.00004110-$0.00003151 per GB/hour and networking at $0.12-$0.09 per GB, with lower rates on the Capacity plan.

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.

Materialize: How do I get started with Materialize?

Start with the free Community tier for development and non-production use, then migrate to Cloud On-Demand or Cloud Capacity when you need production scale.

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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