Compare

Cognite vs Seeq

Both read data out of historians like AVEVA PI. Cognite contextualizes it into a knowledge graph for many applications; Seeq is a focused analytics workbench.

Side by side

	Cognite	Seeq
Vendor	Cognite AS	Seeq Corporation
Pricing model	Quote only	Quote only
Free tier	No	No
Deployment	Cloud	Cloud, Self-hosted
Open source	No	No
Best for	Asset-heavy industrial enterprises needing to contextualize scattered historian and engineering data for analytics and AI.	Process engineers and manufacturers who need to analyze historian time-series data without heavy custom scripting.
Pricing	Sold as a cloud subscription to industrial enterprises; a dedicated pricing page was not available at the time of writing. Pricing has not been verified yet — see the vendor's site.	Sold as a software subscription to industrial manufacturers; a dedicated pricing page was not available at the time of writing. Pricing has not been verified yet — see the vendor's site.
Features	Contextualized industrial knowledge graph Ingestion from historians, SCADA and engineering systems 3D model and document linking to equipment data APIs and SDKs for building custom applications Machine-learning model support for industrial use cases Data governance across industrial data sources	Time-series search and cleansing across plant historians Reusable calculations and equipment monitoring rules Anomaly and capacity-constraint detection Dashboards and reports for operations staff Connectors to AVEVA PI System and other historians Cloud and on-premises deployment options

Verdict

Cognite and Seeq both sit on top of a plant's existing data infrastructure, commonly AVEVA PI System, rather than replacing it, and both are frequently evaluated together because they overlap at the edges. But they are built to solve different problems. Cognite Data Fusion's core product is contextualization: it ingests data from historians, SCADA, engineering documents and 3D models and links raw sensor tags to the equipment, documents and processes they actually belong to, producing what it calls an industrial knowledge graph that any downstream application or model can query. Seeq is a focused analytics workbench: it lets process engineers and data scientists search, clean and analyze time-series data directly, build reusable calculations and monitoring rules, and package findings into dashboards — without necessarily building a broader contextualized data model first.

Choose Cognite if

Your goal is making scattered industrial data usable across many applications and teams, not just one analytics use case.
You are building custom applications or machine-learning models that need equipment context, not just clean time-series charts.
You operate across asset-heavy, multi-source environments — historians, SCADA, engineering documents, 3D models — that need a shared data model.

Choose Seeq if

Your immediate need is process engineers investigating quality deviations or capacity constraints directly against historian data, without a data-modeling project first.
You want a tool aimed at analysis and reporting output — dashboards, monitoring rules, notebooks — rather than infrastructure for other applications to build on.
You want the option of on-premises deployment alongside cloud, matching how your historian itself is likely deployed.

The honest caveat

These tools are not always rivals — some organizations run both, using Cognite's contextualized data model as the foundation and Seeq for day-to-day process analysis on top of it. If you are choosing only one, the real question is whether you need infrastructure that many future applications can build on (Cognite) or a working analytics tool process engineers can use this quarter (Seeq). Ask each vendor directly how they'd expect to coexist with the other, since in practice they often do.

Last reviewed September 22, 2026

Install