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Technology · Cloud

Cloud & DevOps

AWS · Azure · GCP · Docker · CI/CD · IaC

Cloud is where every quality your product claims — reliability, speed, security, cost — is actually decided. We treat infrastructure as part of the product from the first sprint: deploys automated on day one, environments reproducible from code, and observability wired in before the first incident rather than commissioned by it.

How TrivialWorks uses it.

Every engagement ships on infrastructure we define as code. AWS is our deepest bench — our production work runs on Lambda, containers, CDNs and load-balanced Node services — with Azure and GCP where a client's estate or data residency points there. CI/CD is non-negotiable even inside a nine-week MVP: the first deploy happens in week one, not launch week.

Decision guide

Should your project use Cloud & DevOps?

Practical selection guidance — the conversation we would have with you before writing a line of code.

When it’s the right choice

  • Products that must hold real load — seasonal spikes, morning peaks, launch traffic — without a 3am pager story
  • Teams whose deploys are manual, fearful or monthly, and should be automated, boring and daily
  • Regulated or data-resident workloads needing auditable, reproducible infrastructure
  • Anyone whose cloud bill has become a line item nobody can explain

When it isn’t

  • A prototype validating an idea — a PaaS and a deploy hook are honest scope until real users arrive
  • Kubernetes as ambition rather than requirement — a container platform for three services is cost cosplaying as engineering

Consider Managed PaaS Pre-product-market-fit, single service, small team — graduate to orchestrated infrastructure when scale demands it, not before.

Best use cases

Where Cloud & DevOps makes practical sense.

Production architecture

Load-balanced services, CDNs, caching tiers and failover — the architecture under our school-transport work, sized for the morning peak.

CI/CD pipelines

Build, test, review gates and deploy on every merge — the delivery mechanics our AI-first pipeline depends on.

Serverless backends

Lambda and queue-driven processing for spiky, event-shaped workloads billed by execution, not by idleness.

Cost & observability engineering

Tracing, alerting and dashboards that catch regressions before users do — and a cloud bill someone can finally explain.

Technology pairings

Commonly paired with Cloud & DevOps.

Related services

Services that commonly use it.

Questions

Cloud & DevOps, asked straight.

AWS, Azure or GCP?

AWS is our deepest bench and the default absent other constraints; Azure wins where an organisation lives in Microsoft's estate; GCP where its data and ML tooling matter. The honest answer is that architecture discipline transfers across all three — vendor religion does not serve you.

Can you take over infrastructure someone else built?

Yes — it is a modernisation engagement. We map what exists, put it under infrastructure-as-code so it is reproducible, add observability, then improve incrementally. The lights stay on throughout; the heroics retire.

How do you keep cloud costs under control?

By treating cost as an engineering requirement with a budget, like latency: right-sizing, autoscaling, storage tiering and per-feature cost visibility from the start. Most runaway bills are architecture decisions nobody costed — we cost them.

Thinking about Cloud & DevOps?

Send the requirement and you get back a functional specification — screens, data model, stack and an estimate — at no charge. If Cloud & DevOps is the wrong choice for it, that will be in there too.