€772,556 in Savings and Portfolio-Wide Visibility for a PE-Owned 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 nought to three months.
Company Overview
The client is a global software company with more than 600 employees, running multi-cloud infrastructure across both Azure and AWS. With £2.2 million in annual cloud spend growing year on year, it had no structured FinOps practice and limited visibility into where waste sat across its VM and Disk infrastructure.
As a private-equity-owned business, the parent firm also needed a repeatable way to understand and benchmark cloud efficiency across its wider portfolio, not just this one company.
Problem Statement
Two problems sat on top of each other here. The company itself needed to find and remove waste, and its PE owner needed a way to do that consistently across many companies, so a one-off audit was never going to be enough; the answer had to be repeatable by design.
Outcome Delivered
Analysing 37,114 resources and generating reports in under six seconds each, the engagement identified €772,556 in annual savings, €377,325 of it as quick wins, through a 48% cut in VM spend (€661,986) and a 19% cut in disk spend (€110,570), and delivered a repeatable reporting model that scales to more than 40 portfolio companies with portfolio-wide visibility.
Executive Overview
The engagement identified €772,556 in annual savings across VMs and Disks, with €377,325 of that actionable as quick wins within nought to three months, representing a 48% reduction in VM spend (€661,986) and a 19% reduction in disk spend (€110,570).
Beyond the savings, the work delivered a repeatable, automated reporting model that can be scaled to more than 40 portfolio companies with a single aggregated portfolio report, giving the PE firm portfolio-wide visibility into cloud spend and savings opportunities.
Our Approach
We 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, and FinEye, our AI-powered reporting engine, then generated detailed optimisation reports in under six seconds each for Virtual Machines and Disks, drawing on AI agents that use reasoning models with built-in guardrails and data security.
FinEye's optimisation reporting covers VMs, Disks, Databases, Storage, Networking and more than 20 resource types, and scales natively to portfolio-level reporting, so the PE firm now receives individual company-level reports each month alongside an aggregated portfolio view.
Challenges
Challenges Faced
Every engagement starts with a clear-eyed view of what was actually getting in the way, because you cannot fix what you cannot see. Here we set out the real constraints the client was working within, the costs and risks they were carrying, and the gaps that needed closing before any meaningful progress could be made.
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Send an EnquiryMulti-cloud complexity
Infrastructure ran across both Azure and AWS, adding complexity to any cost analysis.
£2.2M and growing
Annual cloud spend stood at £2.2 million and was growing year on year.
No structured FinOps
The company had no structured FinOps practice to keep that spend in check.
Limited view of VM and Disk waste
Visibility into where waste sat across VM and Disk infrastructure was limited.
Deployed solutions
What We Did
A look at how we approached the work, from the first discovery sessions through to the changes we put in place, and the thinking behind each decision along the way. The aim is always the same, to deliver something measurable that the client owns and can build on, rather than a one-off fix that fades once we leave.
Read-only access, zero disruption
Read-only access to both Azure and AWS meant the analysis ran with no disruption to technical teams.
Optimus estate analysis
Optimus analysed 37,114 cloud resources across 1,349 VMs and 35,765 disks to locate the waste precisely.
Automated FinEye reporting
FinEye generated detailed optimisation reports in under six seconds each, using reasoning-model AI agents with guardrails and data security built in.
VM savings — 48%
Virtual machine spend was cut by 48%, worth €661,986, the larger share of the total.
Disk savings — 19%
Disk spend was cut by 19%, worth €110,570, with €377,325 of the overall total actionable as quick wins.
A portfolio-scale model
The reporting model scales to more than 40 portfolio companies, with monthly company reports and a single aggregated portfolio view.