Cognite Data Fusion Alternative: GroveStreams Compared

Short version

Cognite Data Fusion is an industrial DataOps platform built around contextualization — the work of pulling time-series, asset hierarchies, documents, 3D models, work orders, and engineering drawings into one knowledge graph and then surfacing that graph through a suite of products (Industrial Canvas, Charts, Functions, Atlas AI, InField). Its center of gravity is heavy-process industry — oil & gas, power generation, large-scale manufacturing. Onboarding typically involves a systems-integration partner.

GroveStreams is a temporal intelligence platform built around one primitive: the stream. Every property, every relationship, and every measurement is a stream with its own time axis, queried through real SQL (GS SQL), with AI forecasting and an AI assistant built in. Center of gravity: any data with a time axis and relationships that change — energy storage, utility metering, manufacturing equipment, fleet operations, financial services, building systems. Onboarding is self-serve.

If your problem is “contextualize twenty years of plant data across a dozen source systems with a partner-led program,” Cognite is built for that. If your problem is “model my domain, get history on every value and every relationship, and run real SQL plus forecasting against it without standing up a separate ML pipeline,” GroveStreams is built for that.

What each platform is

Cognite Data Fusion (CDF) is the core platform from Cognite, a Norwegian industrial software company headquartered in Oslo. CDF stores and contextualizes industrial data — time-series, asset hierarchies, events, files (including P&IDs and other engineering documents), sequences, and 3D models — and surfaces it through a suite of products:

• Industrial Canvas — a visual workspace for assembling diagrams, charts, and documents into shared boards.
• Charts — a time-series exploration and calculation tool.
• Functions — serverless Python for custom logic.
• Cognite Atlas AI — the agentic AI layer for industrial workflows.
• InField — a mobile companion for field operators and inspections.
• Data Modeling (Flexible Data Modeling / FDM) — a GraphQL-queryable graph model that lets teams define their own ontology on top of the underlying data.

Developers integrate through Cognite's Python (and JavaScript / Java) SDKs and GraphQL for data-modeling queries. The deep partner relationships with Accenture, Capgemini, and others reflect the kind of program CDF is usually deployed inside — a multi-quarter contextualization engagement with a systems integrator.

GroveStreams is a single product. The architecture is built around one primitive — the stream — and one query language — GS SQL. Templates declare entity schemas; streams hold timestamped values; link streams hold timestamped relationships. Roll-ups, derived streams, AI forecasting, an AI assistant, dashboards, alerts, OData/JDBC for BI tools, MQTT and HTTP ingestion, branded subdomains for white-label deployments — all in one product, queried through one language, billed under one plan.

Ten years in production, 6,600+ organizations, 1.7M streams, 307K derivations. SOC2-certified data center. Self-serve sign-up; no required partner engagement.

The data model: contextualization vs. temporal-by-default

The hardest difference to see from the outside, and the most important one once you're building, is where time lives.

Cognite's underlying model separates time-series data (numeric or string sequences attached to an asset) from the surrounding context (assets, events, files, sequences, 3D, work orders). The Data Modeling layer lets you define a graph on top — nodes, edges, properties — and query it via GraphQL. Relationships in that graph model the current state: which pump is attached to which tank today. If a pump gets reconnected to a different tank next quarter, the standard workflow is to update the edge (and rely on event records or change-tracking patterns to remember the prior state).

GroveStreams takes a different shape. A relationship between two entities is itself a stream — a link stream. When Pump-A is reconnected from Tank-7 to Tank-12, a new data point is appended; the old connection stays in the history with its own timestamp. A query that asks “what was Pump-A feeding on March 14” resolves through the link stream the same way it would resolve any other value at that time. Relationship history is not a feature you add; it's how the relationships are stored.

Cognite's contextualization story has strengths GroveStreams doesn't match. P&ID parsing, 3D model integration, document classification against an asset hierarchy — these are deep, partner-supported workflows aimed at brownfield industrial sites with decades of accumulated engineering material. If your project starts with a warehouse of PDFs, CAD drawings, and SCADA tags that need to be tied together, that is exactly the work CDF was built for.

Query language: SDK + GraphQL vs. real SQL

Cognite's primary developer surface is the Python SDK, with GraphQL for data-modeling queries. Both are powerful and well-documented. They are also, deliberately, code — analysts who don't write Python rely on Charts, Industrial Canvas, or BI exports.

GroveStreams ships GS SQL, a purpose-built SQL dialect. One virtual table — Stream — projects across every entity in the org. Anyone who knows SQL can write:

• Roll-up queries with built-in temporal accessors (sample(), last(), time filters).
• Cross-stream joins with && conditions that align mismatched time axes through roll-ups onto a common grid.
• Saved-query VIEWs that expose temporal results — including rollups, derived streams, and FK-resolved temporal joins — to BI tools as standard tables.

GroveStreams also speaks the PostgreSQL wire protocol (Beta). Tableau, Power BI Desktop, Grafana, DBeaver, Excel, DataGrip, and psql connect directly with a user's email and password — no special driver, no API keys, no OData URL. For a team where the analysts already know SQL and the BI tools already know PostgreSQL, this collapses a lot of integration work.

AI: built-in forecasting and an in-product assistant

Both platforms have AI stories. They're structured differently.

Cognite Atlas AI is a newer agentic layer aimed at industrial workflows — orchestrating LLMs over the contextualized graph so operators and engineers can ask questions in natural language. Custom model code typically runs through Cognite Functions in Python (with the developer responsible for bringing models, training data, and orchestration). It is a flexible, code-first foundation.

GroveStreams takes a more opinionated, in-product approach:

• Eight built-in forecasting model types — TFT, N-BEATS, ARIMA, Prophet, TCN, Transformer, Exponential Smoothing, RNN — with a Model Builder and Wizard. Each model trains on one or many input streams and emits a forecast stream. Pick the streams, pick a horizon, run a forecast. No Python required. (Details: Forecasting docs.)
• An AI Assistant in every org that can read templates, streams, samples, and metadata; write and run GS SQL; build dashboards; and act as the natural-language interface to the platform. Multi-provider (OpenAI, Anthropic, Gemini, xAI), org-controlled keys.
• Built-in correlation detection across streams.

Cognite's approach gives you more room to bring your own models; GroveStreams gives you more out-of-the-box and a faster path from “I have a stream” to “here's the forecast.”

Workflow surface: one product vs. a suite

Cognite is a suite. Industrial Canvas, Charts, Functions, Atlas AI, InField, Data Modeling, plus the SDK — each is its own product with its own UI, its own learning curve, and its own integration points. That breadth is a strength for large industrial programs that need each of those surfaces, and a tax on small teams that just want to model their data and ask questions about it.

GroveStreams is one product. Templates, streams, derivations, dashboards, AI Assistant, alerts, OData/JDBC, MQTT, REST, branded subdomains — all under one nav, one billing line, one RBAC system. There is no Industrial Canvas equivalent and no 3D model viewer. For non-industrial domains, that's not a gap; for heavy oil & gas with 3D plant models and P&ID parsing as core requirements, it is.

Pricing and onboarding

Cognite's pricing is not published. Customer engagements are typically enterprise, often consumption-based, and almost always involve a systems-integration partner during initial deployment. The path from “saw a demo” to “running in production” is measured in quarters and includes contracted contextualization work. For a large industrial program, that is appropriate and often necessary.

GroveStreams pricing is public, predictable, and self-serve. See pricing for current tiers. There is a free tier; you can sign up and have a model running in an afternoon. The platform is designed to be operated by the customer, not by a partner on the customer's behalf. A skilled DBA, data architect, or domain expert can build a working temporal model without an SI engagement — and an AI Assistant can help vibe-design the schema from natural language if that fits the team better.

At-a-glance comparison

When to pick Cognite

• You are a large industrial operator (oil & gas, power generation, heavy manufacturing) with decades of engineering material — P&IDs, CAD drawings, work orders, SCADA tags — that needs to be contextualized into one graph.
• 3D plant models and document parsing are core to the workflow.
• You have, or plan to engage, a systems-integration partner to lead the initial deployment.
• You want a suite of workflow products (Canvas, Charts, InField, Atlas AI) and your teams will adopt each one.
• Procurement prefers enterprise consumption pricing and a multi-quarter program.

When to pick GroveStreams

• You want every value and every relationship to have temporal history natively — not reconstructed from event records.
• You want real SQL (and PostgreSQL-compatible BI tooling) as the primary query interface.
• You want AI forecasting built into the platform — eight model types, in-product wizard — without standing up a separate ML pipeline.
• You want a customer-operated platform — self-serve sign-up, predictable pricing, no required partner engagement.
• Your domain is industrial-adjacent but not heavy-process (energy storage, utility metering, fleet operations, building systems, financial services, agriculture, logistics, healthcare) — the cross-industry temporal model lands cleanly.
• You want the AI Assistant to vibe-design templates and dashboards from natural-language descriptions, so non-coders on the team can contribute to the schema and the surface.

Honest note

Cognite is the closest peer in the industrial-data-platform category — we share more architectural goals with them than with the historians (AVEVA PI, GE Proficy) or the time-series databases (InfluxDB, TimescaleDB). The differences above are real and they cut in both directions. We don't try to replace Cognite at the things it's great at (P&ID parsing, 3D models, oil & gas at scale with a partner program). They don't try to be a self-serve temporal SQL platform with built-in forecasting and an in-product AI assistant for cross-industry teams. Pick the one that fits the problem you actually have.

Still weighing options? Our roundup of the 10 best industrial intelligence platforms for 2026 places every major tool — Cognite included — at the buyer and use case where it genuinely wins. For the historian-replacement angle specifically, see our AVEVA PI comparison. For the ontology + temporal angle, see our Palantir Foundry comparison.