case study

PE-Owned Global SaaS Company

PE-Owned Global SaaS Company

A PE-owned global SaaS company with £2.2M in annual cloud spend identified €772,556 in annual savings across VMs and Disks, with €377,325 actionable as quick wins within 0–3 months.

A PE-owned global SaaS company with £2.2M in annual cloud spend identified €772,556 in annual savings across VMs and Disks, with €377,325 actionable as quick wins within 0–3 months.

A PE-owned global SaaS company with £2.2M in annual cloud spend identified €772,556 in annual savings across VMs and Disks, with €377,325 actionable as quick wins within 0–3 months.

Industry

Software (PE-Owned)

Cloud Suite

Azure & AWS (Multi-Cloud)

Results

Identified €772,556 in annual savings across VMs and Disks

context

Client Context & Background

The client is a global software company with 600+ employees, running multi-cloud infrastructure across Azure and AWS. With £2.2M in annual cloud spend growing year-on-year, the company had no structured FinOps practice and limited visibility into where cloud waste existed across its VM and Disk infrastructure. As a PE-owned business, the parent firm also needed a repeatable way to understand and benchmark cloud efficiency across its wider portfolio.

Results

Results & Outcomes Achieved

The engagement identified €772,556 in annual savings across VMs and Disks, with €377,325 of that actionable as quick wins within 0–3 months. This represented a 48% VM spend reduction (€661,986) and a 19% disk spend reduction (€110,570). Beyond the savings, the engagement delivered a repeatable, automated reporting model — the FinEye platform — that can be scaled to 40+ portfolio companies with a single aggregated portfolio report, giving the PE firm portfolio-wide visibility into cloud spend and savings opportunities.

Process

Our Process & Key Observations

Finabeo gained read-only access to the client's Azure and AWS environments with no disruption to technical teams. Our proprietary tool Optimus analysed 37,114 cloud resources across 1,349 VMs and 35,765 Disks. FinEye, our AI-powered reporting engine, then autonomously generated detailed optimisation reports in under six seconds each for Virtual Machines and Disks — powered by AI agents using reasoning models with built-in guardrails and data security. FinEye's optimisation reporting capability covers VMs, Disks, Databases, Storage, Networking and 20+ resource types, and scales natively to portfolio-level reporting. The PE firm now receives individual company-level reports monthly, alongside an aggregated portfolio view.

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view case study

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