Cloud Computing Services
Most cloud bills are not high because traffic is high — they are high because nobody revisited a sizing decision made during the pilot. We design infrastructure for the load you actually expect, and design the application to make scaling a configuration change rather than a rewrite.
Cloud Computing done properly.
Three layers, from the vendor decision down to who watches it at 3am.
- Cloud Infrastructure
Selecting and configuring the right infrastructure for the workload in front of you — not the largest instance available, and not the cheapest one either, but the one sized to what the application will actually do.
- Cloud Application Architecture
Applications designed to scale horizontally from the start, so growth is a configuration change and a bigger bill rather than an emergency re-architecture under load.
- Managed Cloud Hosting
Ongoing monitoring, patching and incident response for infrastructure already in production, so scaling and uptime stay someone's job instead of nobody's.
Where this usually goes wrong.
Named up front, because avoiding them is most of the value.
- Sized once, never revisited
Infrastructure provisioned for a pilot and never resized as real traffic arrived is the single most common cause of an inflated cloud bill. Sizing is a recurring decision, not a one-time setup step.
- Scaling bolted on after launch
An application built assuming a single instance turns "we grew faster than expected" into a rewrite instead of a configuration change. Horizontal scaling is a design decision made before launch, not a patch applied during an outage.
- No one owns production at 3am
A launch with no monitoring or on-call plan behind it means the first anyone hears of an outage is a customer complaint. Alerting and an incident process are part of the build, not an afterthought.
- Vendor chosen by habit, not fit
AWS, Azure and Google Cloud are all capable; the deciding factors are usually your existing commitments, data residency requirements and where your team's skills already sit, not a feature comparison spreadsheet.
The engagement, phase by phase.
Every phase is run by senior engineers and ends in something you can see.
Workload assessment
What the application actually does, at what traffic and growth rate, before any infrastructure gets provisioned.
Vendor and architecture decision
AWS, Azure, Heroku or another provider, argued against your existing commitments and team skills, with the trade-off recorded rather than assumed.
Infrastructure build
Provisioning, networking and security configured for the workload sized in step one, not for the largest instance on the price list.
Scaling and cost controls
Autoscaling rules and budget alerts set up so growth shows up as a planned bill increase, not a surprise one.
Monitoring and handover
Alerting, dashboards and a runbook handed to your team, or an ongoing managed hosting engagement if you would rather we kept watching it.
What we build it with.
Chosen per project, and for what your team can maintain afterwards.
Questions we get asked.
Which cloud provider should we use?
Whichever you already run, in most cases. AWS, Azure and Google Cloud are all capable, and the deciding factors are usually your existing commitments, data residency and where your team's skills already sit — not a feature matrix.
Can you reduce our current cloud bill?
Often, starting with an assessment of what is actually provisioned against what is actually used. Right-sizing instances, adding autoscaling and cleaning up orphaned resources are the usual first wins, before any architecture change is on the table.
Do you manage infrastructure after launch, or just build it?
Both, depending on what you want. Some clients take the infrastructure and documentation and run it themselves; others prefer an ongoing managed hosting engagement. That choice is made at the start, not assumed.
Can you design for multi-cloud or is that overkill?
For most teams it is overkill — multi-cloud adds real operational cost for redundancy most products never need. We will say so plainly if that is the case, rather than sell complexity you do not need.
How do you prevent surprise bills?
Budget alerts and cost dashboards are part of the initial setup, not an add-on, plus autoscaling rules that cap growth at a ceiling you set rather than one AWS sets for you.
We are already on a cloud provider — can you take over from here?
Yes. It starts with an assessment of the existing setup — architecture, cost, security posture — so any changes are based on what is actually there rather than a guess.
Further reading.
What we have written about cloud computing and the decisions around it.
- Optimizing Cloud Costs for startups →
Practical, engineering-led strategies to cut cloud infrastructure spend without hurting performance or reliability — right-sizing, autoscaling and audits.
- Where QA automation pays off →
A practical guide to QA automation — what to automate first, which frameworks fit your stack, and how automated testing pays for itself within a few releases.
- Building a scalable SaaS: the architecture decisions that actually matter →
The SaaS architecture decisions that actually matter — multi-tenancy, data isolation, background jobs and the trade-offs that bite you at scale.
Other services.
- Mobile App Development →
iOS and Android products built to pass store review, launch on schedule and survive the annual OS churn.
- Web Application Development →
Custom platforms, portals and SaaS — including replacing a legacy system while the business running on it stays up.
- Agentic Software Development →
Senior engineers directing autonomous agents that plan, write and test in parallel — with a named human accountable for every release.