Turning cost insight into audited, measurable action. QCW picks up where your cost dashboards stop — scoring the risk, routing through your approvals, and proving the saving landed.
Recent enterprise engagement.
Results vary by estate.
The savings exist. Every credible analyst report puts the waste figure between a fifth and two-fifths of cloud spend. So why does the bill keep growing? Because finding waste and safely removing it are two completely different problems — and most tools only solve the first.
You can see total spend by account. You can't see which workload, which team, or which decision is driving it. Optimisation lives at the resource level — and that's the level most tools never reach.
What's driving the increase? Is this workload efficient? Where should we cut first without breaking anything? These are the questions your CFO is asking — and the questions your current tooling can't answer.
Engineers see the recommendation. They see the saving. Then they see the SLA, the dependency map, and the on-call rota — and the recommendation sits in the backlog. Forever.
FinOps owns the report. DevOps owns the change. Engineering owns the workload. Finance owns the budget. Without one accountable owner of the action loop, recommendations become tickets, then noise, then nothing.
QCW picks up where your cost dashboards stop. It takes an optimisation opportunity, scores the risk, routes it through your approvals, and tracks whether the saving actually landed — all in one place, with the audit trail regulated enterprises need.
Pulls billing and usage data from AWS, Azure, and GCP into one consistent view — down to individual workloads and resources.
Risk-aware prioritisation of recommendations with forecasted savings, confidence intervals, and dependency checks.
Controlled, auditable change workflows with approval gates and IaC integration — no unmanaged drift.
Continuously track forecasted versus realised savings, validating every action against the promised outcome.
Built for the full loop — from pulling in your cloud data, through prioritising what to change, to executing safely and proving the saving.
Direct connections to AWS, Azure, and GCP across all your accounts. Billing, usage, and workload data pulled into one consistent view — the foundation every recommendation is built on.
Continuous detection across seven categories — compute rightsizing, idle elimination, storage tiering, commitments, container efficiency, data-transfer, and re-evaluation as new data arrives.
Every recommendation scored on financial impact and operational risk. Dependencies, criticality, and payback periods translate raw findings into an ordered, executable queue.
Human approval workflows, Terraform/IaC integration, and a complete audit trail of every decision, approval, change, and outcome. Governance-aligned by default.
A commodities trading firm deployed QCW across Master, Development, and Production AWS accounts. Initial limited-coverage analysis surfaced ~$600K of opportunity. As data coverage matured and ML models refined, validated savings potential grew to $2.2M annually — representing approximately 37% of annual cloud spend.
Total estimated annual cloud expenditure across the AWS estate.
Savings identified and validated through QCW's optimisation analysis.
Projected cost reduction as a share of total annual cloud spend.
First pass with limited data coverage — early-stage estimate drawn from surface-level signals only.
Full billing and usage data unlocked workload-level and resource-level analysis across the estate.
ML models refined against richer data — ongoing discovery continues to surface new opportunities over time.
The waste figure has been well-established for a decade. Every credible analyst report — Gartner, Flexera, the hyperscalers' own published data — puts enterprise cloud waste between 20% and 40% of annual spend. The dashboards got better. The bills kept growing.
The same pattern showed up inside regulated enterprises: a FinOps team with a good recommendation engine, a DevOps team too stretched to action the backlog, a CFO asking why the forecast kept slipping, and a Head of Cloud who'd rather absorb the on-call risk than the change-management risk. The tools on the market were built for the first mile — reporting. The last mile — safely making the change — was left to whoever had capacity, which was usually nobody.
QCW was built for that last mile. Not another cost dashboard. An execution layer that scores the risk, respects the approval gates, integrates with the IaC of record, and proves the saving landed. Built for regulated estates, governed by default, and engineered for the audit.
QCW's interface is engineered for the people who actually run the estate — cloud engineers, FinOps leads, and finance partners. Every recommendation is scored, ranked, and linked to the change that realises the saving.
Representative dashboard. Figures illustrative.
From a $600K first estimate to $2.2M validated — as data matured, so did the number.
What happens when QCW meets your estate, your security team, and your change-management process — answered straight.
You can — and most enterprises try. The savings sit there for years anyway, because identifying waste and safely removing it across hundreds of workloads is a full-time engineering programme nobody has the bandwidth to run. QCW is that programme, built and ready to deploy — with the data layer, prioritisation engine, and audit trail already in place.
Not without your sign-off. Initial assessment is read-only — billing and utilisation data, no agents, no production impact. When execution begins, every change runs through your dev environment first, then promotes to production through your existing approval gates. Same path your engineers use today.
You don't need it. QCW offers a managed execution option — our team takes the recommendations through your approval workflows on your behalf, with permissions and risk levels you define upfront. Same governance, none of the engineering load on your side.
If you're already using a cost-reporting tool — Cloudability, CloudHealth, native AWS Cost Explorer — keep it. QCW sits on top, adding workload-level insight, a prioritised queue of actions, and safe execution. Most customers run both: the report below, the action layer above.
Cloud billing and utilisation data is ingested into QCW's tenant — SOC 2 Type II aligned, encrypted at rest and in transit, role-scoped access. Single-tenant deployments available for regulated environments. Full data-processing documentation provided for your security review.
The audit trail makes rollback trivial. Every executed change is version-controlled through your IaC. Every approval, parameter, and outcome is logged. Reversing a change takes the same path as applying it — approved, controlled, recorded.
Cloud cost optimisation touches four functions. Each one inherits something specific when QCW sits between insight and execution.
Cloud is usually the line item nobody can defend in detail. QCW replaces the quarterly explanation with an auditable, forecast-aligned number — and the evidence to back it up.
Most cost-optimisation programmes slow engineering down. QCW runs parallel to delivery, not across it — and uses one governance model across AWS, Azure, and GCP so you aren't managing three.
You own the platform, the on-call rota, and the change-management process. QCW is the optimisation layer that respects all three.
You've built the dashboards. You've run the showback. The bill still grows. QCW is the execution layer that closes your loop — so the number you forecast becomes the number the CFO reports.
Analysis uses read-only cloud integration — no write access to production during assessment phase.
Recommendations are generated without impact to running workloads or application performance.
Scalable multi-tenant or single-tenant options to meet enterprise security and compliance needs.
One optimisation platform across AWS, Azure, and GCP — consistent governance, consistent execution.
Recommendations scored, ranked, and approved through your existing change-management workflows.
Automated design decisions for new and modified workloads — cost-efficient architecture proposed by default.
End-to-end automation from telemetry to test-environment deployment, with full rollback and audit.
Full lifecycle automation — human oversight on policy, machine execution on detail.
What ships today is the assisted loop — scored, approved, audited. The agents are on the shipping roadmap; autonomous is where we're heading.
Read-only connection to your cloud accounts. First-pass recommendations in two days; validated picture grows as data matures.
