MADIS · FinOps AI Assistant
Ask your cloud spend a question. Get a grounded answer.
Cloud, infrastructure, and human-resource costs rise as organizations scale — usually with little transparency. The FinOps AI Assistant bridges finance and operations: a pre-trained AI model lets anyone query cloud spend and usage in natural language, and surfaces optimization recommendations grounded in your actual data.
- 52%
- cost reduction from right-sizing a flagged VM, D4s_v3 → D2s_v3
- ~$89
- average monthly savings per underutilized VM detected
- <9%
- CPU utilization typically found on flagged development VMs
Intelligence in action
Performance metrics, cost data, resource configuration, and tags — normally scattered across cloud consoles — are combined and reasoned about directly, in plain language. See how it looks on your platform.
On Azure, the assistant reasons over Azure Monitor, Cost Management, and Resource Manager data together — so a question about one VM pulls in its performance, its cost, and its tags in a single answer.
Sample conversation — FinOps AI Assistant (Azure)
Are any of these VMs oversized?
Yes — vm-demo-dev-001 (Standard_D4s_v3) has averaged 9% CPU and 18% memory over 30 days. Downsizing to D2s_v3 saves ~$89/month (52%).
Which resources have no cost center tag?
3 resources lack a cost_center tag: vm-qa-002, func-api-staging, and sql-analytics-dev. Tagging is required for chargeback compliance.
- Virtual Machines
- SQL Database
- Kubernetes Service
- Function App
- Databricks Service
Today's cost management challenge
Visibility isn't the same as intelligence. Existing tools display data beautifully. They don't reason about it.
Data lives in silos
Usage, billing, and operational logs are spread across multiple sources and rarely combined into one picture.
Role misalignment
CFOs prioritize budgets; CIOs focus on performance — with no shared view to reconcile the two.
Visualization ≠ intelligence
Cost Management, CloudHealth, and Power BI show data clearly but can't reason about it or produce recommendations.
Analysis doesn't scale
Manual right-sizing works for a handful of resources. Across hundreds or thousands, it becomes cost-prohibitive.
The FinOps approach
Three principles, one shared source of truth.
Transparency & accountability
Real-time visibility into cloud usage and costs, shared across teams instead of siloed by role.
Continuous optimization
Identify waste, forecast accurately, and improve governance on an ongoing basis, not once a quarter.
Cross-functional collaboration
Finance, engineering, and business teams work from the same numbers and the same recommendations.
The MADIS methodology
From fragmented logs to explainable recommendations.
A cloud-agnostic FinOps platform that consolidates cost, usage, and organizational ownership data across Azure, AWS, and Databricks into one cost-intelligence layer — with end-to-end traceability from technical resource to business owner.
Data centralization
Usage, billing, and operational data aggregated through platforms like Databricks or Synapse.
Role-based data marts
CFO dashboards for budgets and forecasting; CIO dashboards for performance and efficiency.
AI-powered insights
Fine-tuned models with RAG integration turn raw data into actionable recommendations.
Intervention & automation
Alerts and semi-automated scripts today, on a roadmap toward a full Cloud Optimization Agent.
Key capabilities
Conversational interface
Ask questions about cost and usage in plain language.
Right-sizing engine
Flags oversized and underutilized resources automatically.
Grounded AI analysis
Recommendations are tied to your actual metrics — not generic advice.
Selective data context
The assistant reasons only over the data it's scoped to see.
Enterprise export
Push findings into the BI and reporting tools you already use.
Multi-tenant security
Built for organizations that need strict data separation by team or client.
What it covers
Cloud-agnostic, down to the resource.
| Layer | Coverage |
|---|---|
| Built-in | AWS Management Console, Azure Portal, Databricks Views |
| Custom | Tailored BI dashboards and AI-powered analysis |
| Third-party | IBM Cloudability for enterprise-wide optimization |
Also covers AWS: EC2/Compute, EBS, S3, Lambda, Athena, networking/data transfer, load balancers, ECS.
Ready to stop wasting cloud budget?
Let's define your FinOps priorities and next steps with a focused consulting session.
Looking for something else?
finops@madis.hu · Solution: AI-Based Cloud FinOps · Provider: MADIS Consulting