A Sovereign Microsoft Fabric Alternative
Your semantics are strategy — own them. An organisation-wide business ontology on open formats, run where you choose, answerable to no single hyperscaler.
Your Own Organisation-Wide Ontology
An organisation-wide business ontology you own and run yourself — not a semantic layer hosted inside the Microsoft cloud.
Not Azure-SaaS-Locked
Runs inside your perimeter on open formats, European-native — not Azure-only SaaS on OneLake and the Microsoft ecosystem.
Grounded AI Built In
The same ontology grounds AI agents for accurate, explainable answers — beyond dashboards and Copilot over BI models.
Fabric now offers a business model of your data too — but delivered as a service inside the Microsoft cloud. The question this page answers is what that means when your obligations, or your board, require the semantics and the data to sit somewhere you operate yourself.
Read this if you're weighing Fabric against a requirement to keep data and its meaning outside a single hyperscaler.
Scrydon is a sovereign alternative to Microsoft Fabric: a first-class, organisation-wide business ontology you run inside your own perimeter. Fabric now offers its own ontology (Fabric IQ), but it is delivered as Azure-only SaaS on OneLake; Scrydon runs on open table formats inside your own perimeter, European-native, from air-gapped on-premises to cloud — so your semantics, data, and deployment are not tied to the Microsoft cloud.
Microsoft Fabric unifies analytics well for Microsoft-centric teams, and whether you use Power BI semantic models or its newer Fabric IQ ontology, the whole suite runs as Azure-only SaaS on OneLake. That ties your data, your modelling, and your residency to a single hyperscaler. Scrydon takes a different path: one first-class business ontology you run inside your own perimeter, on open formats, European-native — so your semantics and sovereignty do not depend on Azure.
Microsoft Fabric Alternative in the Scrydon platform
One integrated, sovereign architecture. Here is where Microsoft Fabric Alternative sits — highlighted against the full stack it works with.
The AI OS for Humans & AI Agents
Ontology & Semantic Layer, one connected model for your data, knowledge & processes
Combining the best of data lakes, data warehouses and search
AI agents, workflows & automations that execute across your systems
Integrate across A2A, MCP, legacy systems and data sources
Secure domain federation, trusted data sharing, and cross-boundary intelligence
Sovereign Foundations
Microsoft Fabric Alternative in depth
Analytics
Data sitting in warehouses and dashboards that nobody reads is data they can't use. The Analytics layer changes that — giving the right people the right information without them having to ask for it. Every metric is anchored to the Cognitive Enterprise ontology, so a revenue figure doesn't arrive in isolation. Data in context — not just in dashboards.
Decision-makers get a live view of the enterprise — financial performance, operational health, procurement status — without waiting for a data team to prepare a report.
- Interactive notebooks: Python and SQL environments with full access to your lakehouse data — no data movement required.
- Visual dashboards: Pre-built, always-current reporting updated automatically as the business moves — no manual refresh, no stale numbers.
- Agent-native analytics: AI agents can query, summarise, and act on insights autonomously — closing the loop between analysis and action.
Cognitive Enterprise
Ontology & Semantic Layer, one connected model for your data, knowledge & processes
Most organisations have data they can't use — not because it doesn't exist, but because nothing connects it. The Cognitive Enterprise layer is the defining intelligence of the AI OS: a living, queryable semantic model of your organisation's entities, processes, and rules. It is the single source of truth that allows every agent, analyst, and workflow to reason about your business with a consistent understanding.
Without it, AI agents reason on noise. With it, they reason on the business.
- Entity graph: Model customers, accounts, orders, products, and any domain concept — then connect them with typed, traversable relationships.
- Process integration: Link real-world workflows to ontology entities so agents understand how data flows through your business.
- Continuous enrichment: Agents automatically enrich ontology nodes with fresh data from the lakehouse, keeping the model current without manual effort.
The Lakehouse is the high-performance data foundation underpinning the Cognitive Enterprise. It is built on StarRocks — a blazing-fast, vectorised MPP query engine delivering sub-second analytics, real-time updates, and high concurrency — and queries open Apache Iceberg tables directly, merging the flexibility of a data lake with the speed of a warehouse under a single, sovereign roof.
- Open Iceberg tables: Query Apache Iceberg and other open table formats directly — your data stays yours, with no proprietary lock-in and no data movement.
- Lightning OLAP: StarRocks' vectorised engine, cost-based optimiser, and materialised views power real-time SQL — from dashboards to agent reasoning — without data duplication.
- Integrated Vector Search: Store and query embeddings alongside traditional data, making the Lakehouse instantly ready for AI workloads.
Shared business meaning, not scattered semantic models
Fabric's semantics historically lived in per-dataset Power BI models, and its newer Fabric IQ ontology runs inside the Microsoft cloud on OneLake. Scrydon models the organisation once in a native ontology — entities, relationships, and definitions shared by every dashboard, report, and AI agent — and keeps it on open formats inside your own perimeter rather than in an Azure-only SaaS estate.
Organisation-wide ontology — Model meaning once across the whole organisation, and run that ontology inside your own perimeter.
Open, sovereign storage — Open table formats inside your own perimeter, with your keys — so you are not tied to an Azure capacity, tenant, and Entra estate.
Consistent metrics — Each metric defined once so reports and agents reconcile, with no conflicting Power BI models.
Grounded AI — Agents reason over the ontology for explainable answers, inside your own perimeter rather than a vendor-hosted cloud.
Sovereignty and shared meaning, not Azure lock-in
Fabric is a strong choice if you are all-in on Azure and Power BI — but that is exactly the constraint: it is Azure-only SaaS, hosted on OneLake, with your semantics and ontology living inside the Microsoft cloud. A sovereign alternative gives you one organisation-wide ontology on open formats inside your own perimeter, European-native, with your keys — so your data, your modelling, and your residency are no longer tied to a single hyperscaler, and the same foundation grounds your AI.
Scrydon vs Microsoft Fabric
Both unify analytics and both offer an ontology. The difference is where it runs: Scrydon's ontology and data sit inside your own perimeter on open formats, versus Fabric hosting yours in Azure-only SaaS on OneLake.
| Capability | Scrydon | Microsoft Fabric |
|---|---|---|
| Primary focus | Ontology-based data unification and trusted insight | Unified SaaS analytics and BI |
| Semantic / ontology layer | Native, first-class organisation-wide business ontology | Power BI semantic models, plus the Fabric IQ ontology (preview) — hosted in the Microsoft cloud |
| Analytics & insights | Analytics anchored to the ontology; consistent metrics everywhere | Deep BI through Power BI |
| Deployment & sovereignty | Sovereign — air-gapped to cloud, European-native, your keys | Azure cloud SaaS only |
| Openness & lock-in | Open formats, your perimeter, your keys | Open formats in OneLake, but locked to an Azure capacity SKU, tenant, and Entra |
| Best fit | Organisations needing a sovereign semantic layer for insight and AI | Microsoft-centric BI teams |
Comparison is Scrydon's summary for orientation. Microsoft, Microsoft Fabric, Power BI, and Azure are trademarks of Microsoft Corporation; capabilities evolve — verify current details with the vendor.
Frequently asked questions
What is the best alternative to Microsoft Fabric?+
How is Scrydon different from Microsoft Fabric?+
Isn't a Power BI semantic model the same as an ontology?+
Is Scrydon more sovereign than Microsoft Fabric?+
Does Scrydon work with AI and Copilot-style assistants?+
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