FabOps is the AI‑powered control tower for Fabric that turns blind spots, weak governance, and unpredictable capacity bills into a clean, governed estate and “good consumption” your CFO and Microsoft both trust. Built for Microsoft Fabric admins and platform owners who need total visibility across governance, performance, and cost, without moving a single byte of data out of their tenant.
Book a 30‑Minute FabOps Demo
Watch how FabOps unifies governance, performance, and cost into a single control plane, surfaces “bad consumption”, and gives you prescriptive, AI‑driven fixes, often in under 60 minutes.
Our interactive demo showcases real-world scenarios from enterprise Fabric environments, demonstrating the power of AI-driven governance and optimization
See FabOps analyse your Fabric estate and produce instant governance and “good vs bad consumption” scores, with clear explanations.
Ask Copilot‑style questions (“Where are we wasting capacity?”, “Which workspaces are risky?”) and get specific actions you can apply immediately.
Understand how each recommended change affects your Fabric bill, performance, and governance risk, before you implement it.
30-minute personalised demo
Built for leading data teams on leading technology
Governance Efficiency Improvement
Fabric Resource Cost Reduction Potential
Continuous Monitoring & Score Tracking
From governance scoring to Al-powered optimization, FabOps provides comprehensive visibility and control over your entire Fabric environment.
AI‑powered scoring of your Fabric governance posture with clear, ranked actions to eliminate risky “dark corners” in your estate.
Natural language queries to understand performance, costs, and optimisation opportunities.
Automatically identify underused models, inefficient refresh schedules, and redundant workloads that quietly drive up your Fabric spend.
Deep diagnostics on data refresh patterns, failures, and bottlenecks, so you maintain 99.9%+ reliability without manual log‑chasing.
End‑to‑end view of who has access to what, where permission creep is happening, and which changes reduce your security and compliance risk.
Identify cost reduction opportunities and safely reallocate capacity from low‑value workloads to high‑value AI and Copilot projects.
See how leading organizations transform their Fabric governance with FabOps
We were looking for a single, unified tool to manage governance, cost, and performance in Microsoft Fabric. FabOps™ stood out as a one-stop-shop solution. The UI is intuitive, performance is snappy, and admin controls are well thought-out. Alerts on outages and scoring functionality helped us make faster decisions and hit our KPIs. I’d recommend FabOps™ to any Fabric user—it’s lightweight, centralized, and packed with tools that simplify oversight.
Data Team Manager, Montego Pet Nutrition
"FabOps transformed how we manage our Fabric environment. We went from reactive firefighting to proactive optimization. Our governance score improved by 40% in just 3 months, and we've saved over $200K in compute costs."
Chief Data Officer, Global Financial Services
Case Study
They turned unmanaged Fabric spend into Safe Consumption—cutting waste by 35% while increasing AI and reporting workloads.
Cost Reduction
Refresh reliability
Governance Efficiency Improvement
Fabric Resource Cost Reduction Potential
To First Actionable Insight with FabOps AI
Continuous Monitoring
Scale from startup to enterprise with pricing that grows with your Fabric environment
Perfect for small to medium data teams getting started with Fabric governance
Most Popular
Advanced governance and optimization for growing data organizations
Comprehensive platform for large-scale Fabric environments
Perfect for small to medium data teams getting started with Fabric governance
Most Popular
Advanced governance and optimization for growing data organizations
Comprehensive platform for large-scale Fabric environments
Join data teams worldwide who use FabOps as their control plane for Microsoft Fabric, eliminating blind spots, stopping bad consumption, and funding new AI and Copilot workloads.
