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Energy · head to head

Cognite Data Fusion vs Replicate

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
Replicate logo

Replicate

AI

Run AI models in the cloud

From
Free
Rated
-
Full reviewWebsite

The short version

  • Only Replicate 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.; Replicate private model deployments are billed for all the time instances are online, including setup and idle time, not only for processing
  • They diverge on capability: Cognite Data Fusion covers Asset-centric data model, Replicate covers Model hosting.
  • Prices and features above were last checked on 1 September 2026.

Where they differ

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

Attributes where Cognite Data Fusion and Replicate differ
AttributeCognite Data FusionReplicate
Starting priceOn requestFree
Pricing modelquoteusage-based
Free tierNoYes
PlatformsWeb, CloudApi, Cloud
CategoryEnergyAI
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 Replicate

  • Model hosting
  • Simple API
  • Auto-scaling
  • Custom models
  • REST API
  • Python client
  • JavaScript client
  • Api 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 Replicate
  • A company standardising asset data across sites so an analytics team can build once and deploy to many plantsnot Replicate
  • An upstream operator building a production-optimisation model that needs sensor data joined to equipment metadatanot Replicate
  • A team replacing a stalled internal data-lake project where nobody could reconstruct what the tag names meantnot Replicate

Replicate

  • Running open source machine learning models through a hosted API without managing GPUsnot Cognite Data Fusion
  • Deploying and serving a custom or fine tuned model on rented GPU hardwarenot Cognite Data Fusion
  • Per second billed batch image, video and language model inferencenot 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.

Replicate

  • Private model deployments are billed for all the time instances are online, including setup and idle time, not only for processing
  • Multi-GPU A100, H100, H200 and L40S capacity beyond the listed configurations is only available with a committed spend contract
  • The pricing page publishes no free tier allowance

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

Replicate

Free
  • Pay-as-you-go$null/usage
    • Billed by execution time for public models
    • CPU Small: $0.000025/second ($0.09/hour)
    • 8x Nvidia A100 GPUs: $0.0112/second ($40.32/hour)
  • Enterprise$null/custom
    • Dedicated account manager
    • Priority support
    • Higher GPU limits

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 Replicate if

  • You need model hosting.
  • You want to start without paying.
  • You work on Api, Cloud.
  • You also want simple api.
Replicate review

Questions people ask

Is Cognite Data Fusion or Replicate better?
Neither clearly leads. Cognite Data Fusion starts at On request and Replicate at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Cognite Data Fusion or Replicate?
Replicate has a free tier; the other does not. Paid plans start at On request for Cognite Data Fusion and Free for Replicate.
Does Cognite Data Fusion or Replicate run on more platforms?
Cognite Data Fusion runs on Web, Cloud. Replicate runs on Api, Cloud.
Can I use Replicate for free?
Yes. Replicate 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 Replicate is typically brought in for.
What can Cognite Data Fusion do that Replicate cannot?
Cognite Data Fusion covers Asset-centric data model, Entity matching, P&ID parsing, 3D contextualisation. Replicate covers Model hosting, Simple API, Auto-scaling, Custom models.

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.

Replicate: How much does Replicate cost?

Replicate uses pay-as-you-go pricing based on model execution time and compute type. Costs range from $0.09/hour for CPU (Small) to $40.32/hour for 8x Nvidia A100 GPUs. Some models charge per input/output tokens instead of time.

Source
Cognite Data Fusion: How is it priced?

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

Replicate: Does Replicate offer a free tier?

Yes, Replicate is free to start with pay-as-you-go pricing. There are no subscription tiers or minimum commitments; you pay only for what you use.

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.

Replicate: What is the difference between public and private models?

Public models are billed by execution time. Private models are billed for all instance uptime including setup, idle, and active processing time, except for fast-booting fine-tunes which are billed only during active processing.

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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