Product
What CloudMonitor is, what it does, and how it works.
Do we host any infrastructure in our Azure tenancy?
Next to nothing. CloudMonitor's platform runs entirely in our Microsoft Fabric tenancy — no Fabric capacity for you to license, no Hubs deployment, no compute or managed app in your tenant. The only Azure resource you create is one storage account that receives your scheduled cost exports; CloudMonitor reads it in place and needs nothing else. The annual license covers the Fabric capacity we run for you, and your team just gets a hosted SaaS URL and a Fabric app.
What does CloudMonitor do?
CloudMonitor is an Azure-native FinOps platform. It reads your Azure billing via FOCUS, pre-builds the reports your finance and engineering teams need in a Microsoft Fabric app, and runs an optimization engine that surfaces savings recommendations.
Which Clouds and SaaS products does CloudMonitor support?
For hyperscaler billing, CloudMonitor supports Microsoft Azure today — the platform is purpose-built for Azure FinOps — with AWS and GCP read-only support on the 2026 roadmap. It also reaches beyond the hyperscaler bill to the AI and SaaS tools your teams already run: OpenAI, Anthropic Claude, Cursor, and GitHub Copilot. That puts your AI token and per-seat spend in the same reports as your Azure cost, so you see total technology spend side by side.
What does agentic FinOps mean in practice?
Each agent is scoped to a cost group and a governance policy. The output is always a recommendation, ticket, or runbook — your team or your existing automation makes the change. CloudMonitor itself is read-only.
Can we white-label for our customers?
Yes — white-label is built into the partner model. CSP and MSP partners rebrand the Fabric app with their own logo, brand colors, and report copy; set per-customer cost-group templates so each client sees only its own allocation; and get a single portfolio view across their whole book of business. The customer signs your contract and pays your invoice — CloudMonitor is the platform inside, and you set your own retail margin on top. See the partner channel for the full reseller model, and how partners like Arraya Solutions and XContent resell it.
How does the savings calculator work out its estimate?
The estimate combines three pillars. Your core Azure spend is anchored to the 22% average year-one saving CloudMonitor customers see, scaled down by how optimized you already are. Your Azure OpenAI token spend applies Microsoft-documented levers: Batch API routing, prompt caching, model right-sizing, and provisioned-throughput right-sizing. Your Microsoft Fabric capacity uses current Azure prices for pay-as-you-go compute, one-year reservations, SKU right-sizing, and pause schedules. Every rate appears in the methodology section with its source and the date we last verified it, and the result is always a range.
Do I have to enter my details to see a savings number?
No. The headline savings range, the pillar breakdown, and the license comparison are free to use with no form. Your work email and company name unlock the full report: the lever-by-lever breakdown with the formula behind each number, an audit of every assumption you kept or changed, and a PDF you can share internally.
How accurate is the savings estimate?
It's an estimate built from your inputs and published rates, not from your billing data, so treat it as a range, not a promise. Mature FinOps teams see smaller numbers by design, because less of the average saving is still on the table. For a real figure, connect CloudMonitor: it reads your billing export in place, no second copy, and replaces every assumption with your actual usage.
Does the estimate cover AI and Microsoft Fabric costs?
Yes — those are the two costs most savings calculators skip. The AI section models Azure OpenAI token spend (batch routing, caching, model mix, provisioned throughput) and the Fabric section models capacity cost (SKU right-sizing, reservations, pause schedules). It deliberately excludes AI harness costs such as vector databases, embeddings, and egress (typically another 40–60% of an AI feature's cost) and OneLake storage; the exclusions are listed alongside the methodology.