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.
Live enterprise engagement. Every verified dollar reconciled to the client's own AWS bill.
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 runs QCW across Master, Development, and Production AWS accounts. An initial limited-coverage pass surfaced ~$600K of opportunity. As coverage matured, validated savings grew past $2.4M annually. By August 2026, $515K a year is delivered, invoiced and verified line by line against the client's own AWS bill. No fee is charged before verification.
Measured annual spend across the AWS estate under management.
Savings identified, validated and actively managed through the platform.
Delivered, invoiced and reconciled to the client's own Cost & Usage Report.
First pass with limited data coverage — early-stage estimate drawn from surface-level signals only.
Full billing, workload and container-grain attribution unlocked the true opportunity across the estate.
Delivered and invoiced: verified line by line against the client's own AWS Cost & Usage Report.
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 $515K a year verified on the bill — as data matured, the number became provable.
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 with row-level isolation per organisation — encrypted at rest and in transit, role-scoped access, immutable audit trail. An accreditation-readiness programme is underway. 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.
Every discipline the platform depends on is held by someone who has practised it inside large, regulated organisations: finance and governance, cloud platform engineering, data, machine learning and product design. The combination is deliberate, because a savings claim has to satisfy a finance evidence standard and a platform engineering safety standard at the same time.
A career in finance transformation, treasury, data platforms and enterprise change inside regulated institutions, including HSBC, Barclays and Nasdaq. Sat on the side of the business that has to answer for the number, which is where the idea for QCW came from: the constraint was never finding the waste, it was governing the change and evidencing the result.
Seventeen years building and running production cloud platforms, as both a technical architect and a programme manager, across products serving UK, US and European markets. Deep AWS and Kubernetes practice with Terraform and CI/CD as the delivery path. This is the discipline behind our rule that QCW never touches infrastructure directly.
Six years designing and optimising large-scale data systems, with distributed processing and open table formats at the core, and AWS certification in both data analytics and solution architecture. Owns the layer that makes the numbers reproducible: billing at line-item grain, container-grain telemetry, and pipelines built so a client can re-derive our figures from their own data.
A research engineer working at the intersection of machine learning and quantum computing in enterprise finance, certified by Google Cloud as both a Professional Machine Learning Engineer and a Professional Cloud Architect. Builds the models behind our rightsizing and forecasting, trained on measured utilisation rather than vendor defaults and held to the same evidence bar as everything else we publish.
A full-stack designer who runs every part of the product design lifecycle, from the vague and fragile idea through user testing to pixel-perfect delivery. Enterprise tooling is usually excused from good design. Ours is not: if a CFO and an engineer cannot read the same screen and reach the same conclusion, the governance model does not actually work.
No handoffs.
You work directly with the people who built the platform. The evidence behind every savings claim comes from the same hands that wrote the pipeline, with no account layer between you and the work.
Every estate we take on brings another client team to work alongside and another set of decisions to win. The platform does the finding and the proving. These roles exist for the parts that need judgement and a person in the room.
You would own an enterprise client estate end to end: the decision queue, the technical case for each change, and the work with their platform team that carries an approved recommendation through to a merged pull request and a verified saving on the bill. It is the role closest to the outcome we are paid for.
You have run production Kubernetes and AWS at scale, and you can hold a technical conversation with a client principal engineer and a commercial one with their CFO in the same afternoon. The role sits between engineering and the client relationship and needs someone credible in both rooms.
The platform is live and carrying enterprise estates today. This role deepens the engineering underneath it: client onboarding, ingestion across cloud billing, telemetry and Kubernetes, and the measurement pipeline that produces the evidence behind every savings claim we publish.
You are comfortable across TypeScript, Postgres and Python, and you have opinions about data correctness. The bar here is unusual: a wrong number is worse than a slow one, because our numbers end up on a client invoice.
Send a note and anything that shows your work to info@qcw.io. No cover letter required. If you are a specialist in cloud economics, measurement or infrastructure governance and neither role fits, write anyway and tell us what you would own.
Read-only connection to your cloud accounts. First-pass recommendations in two days; validated picture grows as data matures.
