01
The bill grows faster than the business
Cloud spend climbs 8% a quarter, finance sees the total but not the causes, and half your resources aren't even tagged. Every review meeting ends with 'we should look into that.'
Services
Cloud & DevOps engineering
Cloud bills that outgrow revenue and deploys that need a war room are the same disease: infrastructure nobody fully owns. We right-size the spend, automate the path to production, and leave guardrails your team operates without us on the call.
Who this is for
You’ll recognize the situation
02
Deploys are events, not routine
Releases happen after hours with a rollback plan and crossed fingers. The team ships less often than it could because the path to production punishes frequency.
03
One person holds the infrastructure
A single engineer knows how production actually works, the Terraform drifted from reality months ago, and their next resignation letter is a business continuity event.
What’s included
Cloud & DevOps, itemized
Cloud cost optimization
Cost attribution down to team and service, then a ranked savings backlog — right-sizing, spot workloads, storage tiering — each item priced by value, effort, and risk.
Kubernetes and container platforms
Clusters that scale with demand instead of fear: autoscaling from real usage data, paved-road defaults for new services, and no pet nodes.
Infrastructure as code
Versioned Terraform modules as the only way infrastructure changes — reviewed, tested, and drift-checked, so the code and reality stop diverging.
CI/CD pipelines
Trunk-based pipelines with staged rollouts and automatic rollback — designed so a Tuesday-afternoon deploy is unremarkable.
Observability and SRE
Dashboards and alerts built around user-facing symptoms, SLOs your team actually reviews, and cost shown next to latency so engineers see what they spend.
Security and compliance hardening
Least-privilege IAM, network segmentation, and audit-ready change control — done inside your existing compliance process, not around it.
How it runs
Four phases, no surprises
01
Diagnose
Two weeks of measurement before any migration: full cost attribution, utilization data, and a ranked backlog your leadership can approve line by line.
02
Plan
Sequenced changes with a risk note on each, agreed through your change-control process — compliance runs alongside, never as an afterthought.
03
Build
Mechanical wins first — right-sizing, autoscaling, ephemeral environments — with availability watched at every step and rollback ready.
04
Scale
New defaults packaged as Terraform modules, per-team cost dashboards with budget alerts, and one number for finance to watch.
−38%
monthly cloud spend on a recent engagement
99.99%
availability held throughout the changes
6 mo
later, spend still within 4% of baseline
Illustrative figures from anonymized engagement profiles.
Case study
What that looks like shipped
A Series B payments platform was watching AWS spend outgrow transaction revenue. We cut the bill 38% in 90 days — with zero customer-facing incidents and the PCI scope untouched.
Read the full case study
In depth
Where cloud waste actually hides
The biggest line items on a cloud bill are rarely mistakes anyone made — they are defensible decisions nobody revisited. When we audited a Nordic payments platform spending roughly $212k a month on AWS, the largest single source of waste was Kubernetes resource requests set generously during a 2023 incident and never touched again. Average cluster utilization sat at 22%. No engineer was wrong at any point; the organization simply had no mechanism for asking whether last year's caution was still this year's requirement.
The same categories are where every audit we run looks first. Compute provisioned for a peak that already passed. Development and staging environments running nights and weekends — often a mid-five-figure annual line on its own. Storage that only ever accumulates: orphaned volumes, snapshot chains nobody prunes, gp2 disks that should have become gp3 years ago. Data transfer nobody modeled, because two chatty services ended up in different availability zones and every request between them is metered. And duplicated databases, because each team provisioned its own replica to be safe.
This is why cloud cost optimization services are engineering work, not procurement. A cost dashboard reports the numbers; it cannot tell you which of them are safe to change. Separating waste from headroom means reading the workload, the incident history, and the traffic pattern behind each line item. For the payments client, that reading produced a ranked backlog of 31 opportunities — each with an estimated monthly value, an effort score, and a risk note — which is what let leadership approve a 38% reduction line by line instead of on faith.
FinOps as an engineering practice
Most FinOps programs fail the same way: a reporting layer gets bolted on, a monthly cost meeting appears on calendars, and six months later the bill looks the same — because nobody who provisions infrastructure ever sees a price. The version that works treats cost as an engineering signal, visible in the same places engineers already look and enforced by the same machinery that enforces everything else.
In practice that means four mechanisms. Tagging enforced at provision time, so an untagged resource fails the Terraform plan instead of becoming next quarter's mystery spend. A unit metric both finance and engineering watch — for the payments client, cost per 1,000 transactions — so growth stops hiding waste and waste stops masquerading as growth. Budget alerts routed to the team that owns the spend, in the channel it already reads. And paved-road Terraform modules, so the cheapest sane configuration is also the laziest one to choose.
The honest tradeoff is that the savings curve flattens. The first quarter's wins are mechanical — right-sizing, schedules, storage tiering — and each gain after that is a small architecture project: moving batch work to spot instances with checkpointing, collapsing cross-zone chatter, consolidating databases. Commitment discounts like Savings Plans typically add another 15–20%, but only once usage has stabilized; buy them first and you lock in the waste at a discount. A practice, unlike a one-off cut, is what holds the line — six months after the payments engagement, spend was still within 4% of the post-project baseline.
Reliability standards that make on-call sustainable
Cost and reliability get pitched as a tradeoff. In practice they fail together, because both are symptoms of infrastructure nobody fully owns. Our DevOps consulting engagements usually open the reliability work with an alert audit: how many times did a human get paged last month, and how many of those pages required action? The lopsided answer — hundreds of pages, a few dozen actionable — is a pattern the industry knows well, and it is how real incidents get missed: the rotation learns that a page usually means nothing.
The standards we install are unglamorous and specific. Every alert that pages a human maps to a user-facing symptom and links to a runbook; if nobody can write the runbook, it becomes a ticket instead of a page. SLOs are set from what users actually tolerate, and error budgets turn the ship-faster-versus-stabilize argument into arithmetic rather than a standing meeting. Deploys move into business hours, because a rollback you have rehearsed is safer at 2pm with the team present than at midnight with one tired engineer.
Choosing an SLO below 100% is the uncomfortable part, and we say so upfront. The payments client holds 99.99% on the payment path because a failed payment is a lost merchant — while internal tooling runs a full tier looser, because the last nine costs more than the first three combined and buys almost nothing. Writing that distinction down is what frees money and attention for the systems where reliability is the product.
The result is measurable in pages per engineer per week, and it holds after we leave because none of it lives in anyone's head: alert rules and SLOs are versioned in Terraform, runbooks are linked from the alerts themselves, and postmortems produce tracked actions instead of resolutions to be more careful next time.
Tools we reach for
A boring, hireable stack
Chosen so your team can maintain, extend, and hire for everything we leave behind.
- AWS
- Kubernetes
- Terraform
- Karpenter
- GitHub Actions
- Datadog
- Kubecost
- PostgreSQL
FAQ
The questions buyers actually ask
Answered the way we’d answer them on a call — specifics included.
The audit is fixed-price. Implementation runs time-and-materials against the ranked backlog you approved, so every invoice maps to specific savings or reliability items. On cost work, the plan includes the payback math for each item before you sign it — you see when the engagement is projected to pay for itself, not just what it costs.
You do — everything is built in your cloud accounts and your repositories from day one. There's no proprietary tooling and no dependency on us to run any of it. IP assigns to you on payment, and the handover includes runbooks your on-call engineers can follow at 3am.
Four or more contracted hours with your day, and production-affecting changes happen only inside agreed windows with your team present. For incident-sensitive work we align to your on-call calendar — nobody changes your infrastructure while you sleep.
We spend the first two weeks measuring, not changing — that's the ramp-up, and it produces the plan. The earliest production changes are the reversible, low-risk ones, and everything routes through your existing change-control process. Speed comes from sequencing, not from skipping review.
Yes — it's most of our cloud work. We design engagements so the compliance scope doesn't move: changes ride your existing change-control process, including in PCI DSS Level 1 environments, and we're comfortable being named in your audits. Access is least-privilege under your SSO, and we never take production data off your infrastructure.
Every backlog item carries its own estimate, so you see variance item by item — not at the end. Changes ship with rollback plans and availability monitoring, and you can stop at any point with 30 days' notice keeping everything already shipped. On past engagements the estimates have been conservative; we'd rather under-promise in the plan.
Buy one — we usually recommend it, and we leave Kubecost wired in at handover. But a tool reports what you spend; it can't rank what's safe to change. Deciding whether 22% utilization is waste or headroom for a settlement spike means reading the workload, and the follow-through needs someone accountable for shipping the changes. Teams that buy the dashboard without the engineering get a very detailed picture of a bill that keeps growing.
Usually the opposite. Your infrastructure person almost always knows where the waste and fragility are — what they lack is time and political cover to fix them while keeping the lights on. We work as their surge capacity: they set context and veto anything that smells wrong, we do the measurement and the mechanical work, and they own the guardrails afterward. If the engagement ends with your one platform engineer still a single point of failure, it failed — reducing that risk is an explicit deliverable.
No — we're deliberately not a managed service provider. We join your on-call rotation during the engagement for anything we've changed, and we offer a defined support window after handover, but the end state is your team operating the platform with runbooks, dashboards, and Terraform it understands. If what you actually need is permanent operations capacity, that's a dedicated team conversation — and we'll say so in the audit rather than let the scope drift into it.
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