SIGNLD vs Microsoft Fabric: where context lives

Microsoft Fabric unifies storage, pipelines, and Power BI semantic models under one workspace, but someone still has to author those models by hand. SIGNLD instead builds a Knowledge Graph from your live systems automatically, so a cross-system question gets answered in minutes, not after a modelling project.

TL;DR

Microsoft Fabric consolidates OneLake storage, Lakehouse and Warehouse compute, Data Factory pipelines, and Power BI semantic models into one capacity-based platform. It is a strong choice for teams already standardised on Microsoft with engineers who can build and maintain a semantic model. SIGNLD connects to 800+ systems read-only and builds a Knowledge Graph that resolves entities across those systems without a lakehouse project, then returns a ranked, traceable Decision Brief for a plain-language question. Fabric organises data for engineers to build on. SIGNLD organises entities and relationships so a CFO or COO can ask a question directly.

For the wider context, see our roundup of the best business knowledge graph platforms in 2026.

What is Microsoft Fabric actually built for?

Microsoft Fabric is a unified analytics platform combining several Microsoft products into one offering. OneLake is the storage layer every workload reads from and writes to. On top sit a Lakehouse for open-format files, a Warehouse for SQL workloads, Data Factory for pipelines, and Power BI for semantic modeling.

The business context in Fabric lives in the Power BI semantic model, formerly the dataset. An analyst defines tables, relationships, measures, and hierarchies inside it, and those definitions become the vocabulary that reports and Copilot-in-Fabric queries draw from. Copilot lets users ask questions in plain language, but answer quality depends on how completely that model was built.

Fabric is licensed through capacity-based F-SKUs, a compute pool shared across every workload, rather than per-user seats. That mechanism is public and lets an organisation predict cost against fixed capacity rather than a growing headcount.

Fabric assumes a data team exists to build the lakehouse, define the semantic model, and maintain pipelines. It is an engineering platform first, with a natural-language layer added on top.

What is SIGNLD actually built for?

SIGNLD is a decision intelligence platform from Inzata Analytics. It connects read-only to the systems a company already runs, from ERP and CRM to the spreadsheets an operations team maintains, and resolves those into a Knowledge Graph. Spreadsheets are first-class sources, because a meaningful share of operational truth in mid-market companies lives in them.

A user asks a question in plain language and receives a Decision Brief: a ranked answer, the entities and connections behind it, source citations back to individual records, and a confidence signal. There is no lakehouse to provision and no semantic model to author before the first question gets answered. You can connect your first system in about 15 minutes and get your first answer in minutes after that.

SIGNLD targets companies of 10 to 500 employees running 10 to 40 or more siloed systems, usually without a dedicated data team, or with one that cannot keep pace with the questions leadership asks. The buyer is typically a CEO, CFO, or COO, not an IT department building infrastructure for others to query.

Where the two genuinely differ

The core difference is what gets modelled and by whom. Fabric's semantic model captures tables, measures, and relationships that a person defines explicitly inside Power BI. SIGNLD's Knowledge Graph captures entities such as customers, orders, suppliers, and invoices, and resolves the same entity across different systems automatically as connections form.

	Microsoft Fabric	SIGNLD
what it models	tables, measures, and relationships in a Power BI semantic model	entities and relationships resolved automatically in a Knowledge Graph
who builds it	a BI developer or analyst, using DAX and Power Query	forms automatically as systems connect, no one authors it manually
time to first cross-system answer	weeks to months, after pipelines and a semantic model exist	minutes after the relevant systems connect
source traceability	traces to a report visual and its underlying query	traces to the source record in the originating system, with a confidence signal
where inference runs	Microsoft's shared Azure OpenAI Service infrastructure	a single-tenant AWS Bedrock instance, private LLM, no training on customer data
who it is for	organisations standardised on Microsoft with a data engineering team	companies of 10 to 500 employees without a dedicated data team

A few concrete distinctions:

Fabric requires a lakehouse or warehouse before Power BI can model anything meaningfully. SIGNLD reads operational systems directly.
Copilot in Fabric answers against whatever the semantic model already defines. SIGNLD's graph builds connections from source systems directly, including ones nobody modelled in advance.
Fabric traces a number to a report visual and its query. SIGNLD traces a number to the source record itself, plus a confidence signal.
Fabric's Copilot inference runs on Microsoft's shared Azure OpenAI Service. SIGNLD's private LLM powered by AWS Bedrock runs on a single-tenant instance, and we never train on your data.
Fabric's cost scales with capacity units purchased in advance. SIGNLD's plans scale with connected systems and users, detailed on pricing.

Who has to build the model?

In Fabric, a BI developer or analyst builds the semantic model: tables get related, measures get written in DAX, hierarchies get defined for drill-down. This is real engineering work, and it happens before most questions can be answered well. A large organisation with a dedicated Fabric team can build this once and reuse it across many reports, which is a genuine efficiency at scale.

In SIGNLD, the Knowledge Graph forms as systems connect. Entity resolution, matching a "customer" in the CRM to the same customer in the billing system, happens automatically rather than through a manually authored join. Nobody writes DAX. The person asking the question does not need to know how the systems relate to each other beforehand, because the graph already worked that out.

This distinction matters most for companies without a data team. A Fabric deployment without a skilled semantic modeler produces reports that are technically live but analytically shallow. SIGNLD is built for exactly the gap that leaves behind: a CFO or COO who has a question today and no engineer to build a model for it.

How long until you get a cross-system answer?

A typical Fabric rollout starts with provisioning capacity, landing data in OneLake through Data Factory pipelines, building Lakehouse or Warehouse tables, then authoring the Power BI semantic model. For a single cross-system question, that sequence commonly runs several weeks to a few months, depending on how many source systems need pipelines.

SIGNLD's timeline is measured in minutes for the first connection and the first answer. Because entity resolution happens as part of connecting a system rather than as a separate modelling phase, a cross-system question, for example one that spans a CRM and an accounting system, can be answered the same day the second system connects. There is no pipeline to schedule and no DAX measure to write first.

This is not a claim that Fabric is slow at what it does. A mature Fabric deployment with an established semantic model answers new questions quickly too, once that model exists. The difference is the fixed cost paid before the first answer, not the marginal cost of each additional question afterward.

Where Microsoft Fabric is the better choice

Fabric is the stronger choice for an organisation already standardised on Microsoft 365, Azure, and Power BI, where switching costs for a new tool are real and the existing skill base is deep. It is also better suited to large-scale data engineering: petabyte-scale Lakehouse workloads, complex Data Factory orchestration across dozens of pipelines, and Spark-based transformation jobs that a decision intelligence platform is not built to run.

Fabric also wins on distribution into everyday Microsoft tools. Reports embed cleanly into Teams, and semantic models expose directly into Excel through PivotTables, which matters where Excel is the primary interface for finance staff. Capacity-based F-SKU licensing also gives predictable cost control at scale that a per-connector model does not offer once usage is high and consistent. For a company with an established data engineering team, Fabric is a serious platform and SIGNLD does not compete with it there.

What running both looks like

Companies already on Fabric do not need to abandon it to use SIGNLD. Fabric remains the system of record for engineered pipelines and recurring reports that Excel and Teams users depend on. SIGNLD sits alongside it, reading the operational systems and spreadsheets that never made it into OneLake, and answers the one-off cross-system questions that would otherwise wait for the Fabric backlog.

Key takeaways

Microsoft Fabric consolidates OneLake storage, Lakehouse and Warehouse compute, Data Factory pipelines, and Power BI semantic models into one capacity-based platform.
The core difference is what gets modelled and by whom.
A typical Fabric rollout starts with provisioning capacity, landing data in OneLake through Data Factory pipelines, building Lakehouse or Warehouse tables, then authoring the Power BI semantic model.
Fabric is licensed through capacity-based F-SKUs, a shared compute pool priced by capacity tier rather than per user.
If your Fabric rollout has a semantic model half-built and a cross-system question waiting on it, SIGNLD can answer that question today, without the modelling step.

FAQ

Is Microsoft Fabric a knowledge graph?

No. Fabric is a unified analytics platform built around OneLake storage, Lakehouse and Warehouse compute, and Power BI semantic models. Those models capture tables, measures, and relationships a person defines, not automatically resolved entities and connections across systems, which is what a Knowledge Graph does.

Do I need a Power BI semantic model before Copilot in Fabric works well?

Yes, in practice. Copilot in Fabric answers questions against the definitions already present in the semantic model. If a relationship or measure was never modelled, Copilot cannot infer it reliably, so answer quality tracks how completely the model was built.

Can SIGNLD replace Microsoft Fabric?

Not for large-scale data engineering. SIGNLD does not run Spark jobs, manage petabyte-scale lakehouses, or orchestrate complex pipelines. It replaces the modelling step needed just to answer a cross-system business question, which is a narrower and faster job than what Fabric is built to do.

How much does Microsoft Fabric cost compared to SIGNLD?

Fabric is licensed through capacity-based F-SKUs, a shared compute pool priced by capacity tier rather than per user. SIGNLD does not publish pricing figures publicly. Current SIGNLD plans, including a free tier, are listed on pricing.

Where does SIGNLD's AI processing run?

SIGNLD's inference runs on a private LLM powered by AWS Bedrock, on a single-tenant instance dedicated to your account. Your data stays yours through read-only connections to source systems, and we never train models on your data.

Try SIGNLD free

If your Fabric rollout has a semantic model half-built and a cross-system question waiting on it, SIGNLD can answer that question today, without the modelling step. Try SIGNLD free and connect your first system in minutes, or Browse all articles for more on how the Knowledge Graph compares to catalogs and semantic layers.

SIGNLD vs Microsoft Fabric: where the business context actually lives

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