Stop Bleeding Runway on Cloud Bills. ☁️
I eliminate CI/CD compilation bottlenecks and implement minimalist, high-velocity deployment pipelines that protect your engineering budget and maximize infrastructure efficiency on AWS, GCP, or Azure.
Optimizing infrastructure across
AWS • GCP • Azure • Vercel • GitHub Actions • Terraform
Over-engineered DevOps is a silent killer.
You don't need a massive Kubernetes cluster to serve your first 10,000 users.
Resume-Driven Infrastructure
Enterprise-grade infrastructure you don't need yet is quietly inflating your server costs every single month.
The 45-Minute Pipeline
Your developers spend 20% of their week just waiting for code to compile and deploy.
Brittle by Design
Deployment pipelines built without discipline break under pressure, right when you can least afford it.
Who this is for
One of these is a finance problem and one is a morale problem, and they usually turn out to have the same cause.
Founders watching the bill outgrow the revenue
Your cloud spend is rising faster than usage in a way nobody can fully explain, and the answers you get are about architecture rather than about money.
What changes: A line-by-line account of what you are paying for, what it is for, and what breaks if it goes away, followed by a structurally lower bill.
Teams waiting on their own pipeline
Engineers are waiting tens of minutes for a build, deploys are manual enough to be frightening, and nobody ships on a Friday.
What changes: A pipeline fast enough that nobody context-switches while it runs, and a deploy uneventful enough to do on a Friday.
What you get
All of it lands in your repositories and your accounts, in a form your team can keep running.
A costed account review
Line by line: what each item is for, what removing it would save, and what stops working if you do. The third column is the one that makes the first two safe to act on.
Infrastructure as code
Terraform, Pulumi or CDK, committed to your repository, so the environment is reproducible rather than an arrangement somebody remembers making.
A rebuilt pipeline
Caching what is deterministic, running in parallel what is independent, and removing the steps that exist because they always have.
Billing alerts and dashboards
So the next unwelcome change in spend arrives as a notification partway through the month rather than as an invoice at the end of it.
How the engagement runs
Nothing is changed in the first phase, which is what makes the read-only access an easy decision.
1. Read-only audit
Read-only access to the cloud accounts and the pipeline is enough to find where the money and the time are going. Nothing is modified at this stage.
2. The obvious savings
Unattached storage, oversized instances, data nobody reads, caches that never hit. This phase is unglamorous and it is normally what pays for the engagement.
3. Pipeline rebuild
Cache the deterministic work, parallelise the independent work, and make a deploy boring enough that nobody schedules it for a quiet week.
4. Handoff
The infrastructure code lands in your repository with the alerting configured, so the improvement survives my leaving.
From the workbench
Where the money actually goes
Cloud bills rarely grow because of one bad decision. They grow in four predictable places, and you can look in all of them today without changing anything:
| What you see | What it usually is | How to check it yourself |
|---|---|---|
| Storage nobody reads | Volumes detached from deleted instances, snapshots on no schedule, and logs retained forever by default. | Sort storage by age and look at anything untouched for six months. |
| Instances sized for a launch that already happened | Capacity provisioned for a worst case that was estimated rather than measured, and never revisited. | Compare peak utilisation over the last month against what you are paying for. |
| Traffic paying a toll between services | Data crossing a zone, region or gateway boundary that a different layout would have kept local. | Look at the data-transfer line. If it is a meaningful share of the bill, the architecture is being billed, not the traffic. |
| A pipeline everyone waits for | Every run rebuilds and retests everything, because nothing declares what it depends on. | Multiply the build wait by the number of pushes a day by the size of the team. |
The first two are usually most of the saving and take a day. The third is the one that changes an architecture, so it is the one worth measuring before anyone argues about it.
FAQs
Read-only access to your cloud and GitHub accounts is enough for the initial audit. Nothing gets changed until you and I agree on the specific cost and pipeline fixes to execute.
That's the point of the audit phase. I run a line-by-line review of your AWS, GCP, or Azure spend, kill zombie resources, right-size databases, and hand you the exact changes before anything gets touched.
I work with what you have wherever possible. The goal is minimalist infrastructure-as-code that matches your actual scale, not a rewrite for its own sake.
The initial cost and pipeline audit is scoped as a fixed engagement; ongoing optimization runs on the monthly tiers shown above.
Ready to find out what you are actually paying for?
Cloud bills rarely grow because of one bad decision. They grow because a hundred reasonable ones were never revisited, and nobody owns the revisiting.
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