Compare Common Fabric Operating Models

Understanding your operating model is critical when scaling Microsoft Fabric across your organization. This article compares common operating models, adapted from Azure practices, and extends them with specific patterns and capabilities from Microsoft Fabric deployments.

For foundational terminology, refer to our Operating Model Glossary.

Operating Model Comparison

Operating Model	Strategic Priority	Scope	Typical Fabric Scale	Foundation Utilities	Landing Zone Design
Decentralized Ops	Innovation	Workload	Few isolated Workspaces	None	Not defined
Centralized Ops	Control	Landing Zone	Multiple Workspaces, 1 Capacity	Limited	Classic hierarchy
Enterprise Ops	Democratization	Cloud Platform	Dozens of Workspaces, Multi-Capacity	Centralized	Segmented & policy-governed
Distributed Ops	Integration	Full Portfolio	Multi-tenant or business unit model	Coordinated	Cross-region & federated

Each of these maps to a different phase of organizational maturity and provides different benefits and challenges for Microsoft Fabric.

Model 1: Decentralized Operations

• Individual Fabric Workspaces owned by teams.
• No shared governance or capacity strategy.
• Pros: High innovation speed, no dependency on central IT.
• Cons: Poor cost visibility, fragmented access, weak governance.
• Best suited for: Small teams, proofs-of-concept, startups.

Model 2: Centralized Operations

• One or few capacities centrally owned.
• Workspaces are tightly governed and centrally provisioned.
• Pros: Central billing, easier support, standardized operations.
• Cons: Bottlenecks, slow provisioning, low flexibility.
• Best suited for: Organizations with strong central IT and compliance needs.

Model 3: Enterprise Operations

• Workspaces aligned to business domains, federated operations via a CCoE.
• Guardrails are automated via governance policies and deployment pipelines.
• Workspaces tied to Data Products following Data Mesh principles.
• Capacities managed per workload class (dev/test/prod).
• Pros: Balanced autonomy and control, good cost attribution.
• Cons: Requires platform engineering and automation maturity.
• Best suited for: Scaling organizations, complex data ecosystems.

Model 4: Distributed Operations

• Combines multiple models (e.g., Enterprise in EU, Decentralized in subsidiaries).
• Often required due to legal, organizational, or functional complexity.
• Pros: Local autonomy with centralized observability.
• Cons: Very complex to govern and support.
• Best suited for: Multinational corporations, federated orgs, M&A scenarios.

Accelerating Fabric Operating Model Implementation

Operating Model	Starting Point	Fabric-Specific Guidance
Decentralized Ops	Build individual Workspaces and assign Capacity	Use manual provisioning and workspace-level governance
Centralized Ops	Set up one central Capacity and Workspace factory	Implement standard naming, access templates, tagging
Enterprise Ops	Define Platform Workspaces and Dev/Test/Prod pattern	Implement CI/CD pipelines, capacity management, PIM
Distributed Ops	Group Workspaces by Region, Org Unit or Product	Coordinate Fabric Policies across Tenants/Subs

Each approach should evolve alongside your maturity. For a structured model definition, refer to Define your Operating Model and the Operating Model Glossary.