Technologies / AWS
Serverless, container, and data workloads on AWS designed with cost, observability, and operability as first-class concerns — from the first commit.
AWS is the deepest cloud toolbox available, and also the easiest place to build something that quietly bankrupts a project. We work in AWS the way it rewards: deliberate service choice, right-sized resources, tagged cost lines, and infrastructure as code. What you launch is what you can still afford, operate, and defend six months later.
We use AWS where it earns its place. These are the situations we see most often.
Line items nobody can attribute, region choices nobody documented, and no forecast of next quarter's spend.
IAM, VPC, and account structure configured at launch and never revisited. Nothing is broken, but everything is exposed.
The organisation defaults to VMs because that's what the team knows, even where serverless, managed, or containerised would be cheaper and simpler.
We start with your business problem — the process, the users, the constraints — before naming any technology, including this one.
A clear architecture decision record: where this technology fits, where it doesn't, and what it integrates with.
Small, demonstrable increments on real data. Real users, real feedback, real outcomes — not a proof of concept nobody uses.
Operational metrics, documentation, and a runbook your internal team can operate. We're not a lock-in vendor.
Cost dashboards tied to workload — you know what each system costs and can forecast the next quarter.
IaC-managed environments (Terraform or CDK), reproducible across dev, staging, and prod.
IAM, network, and secret management aligned to least-privilege, with evidence for audit.
Observability — CloudWatch, X-Ray, and third-party — that lets an on-call rota triage in minutes.
Lambda, API Gateway, Step Functions, EventBridge for spiky and event-driven workloads.
ECS Fargate for steady-state services; EKS when Kubernetes earns its complexity.
RDS Postgres, Aurora, DynamoDB, Redshift, and S3-based data lakes — chosen for the workload.
VPC design, transit gateway, private networking, and security controls that survive an audit.
GitHub Actions or CodeBuild pipelines deploying via Terraform, CDK, or SAM — reviewed, versioned, and reproducible.
Tagged cost, reserved capacity, savings plans, and periodic optimisation reviews as an engineering discipline.
A UK operator was running a spiky workload on always-on EC2 with a bill twice what it should have been. We re-architected onto Lambda, EventBridge, and Aurora Serverless, cutting the monthly spend meaningfully while improving cold-path performance and observability.
A B2B operator had a single AWS account holding dev, staging, and prod with widely-scoped IAM. We restructured into a multi-account landing zone with SSO, least-privilege roles, and central logging — passing a security review that had previously blocked a customer contract.
Vignettes are anonymised composites drawn from engagements and product work. No client names, logos, or performance figures are implied.
Serverless for spiky and event-driven; containers for steady-state services or shared runtimes. Sometimes both in the same platform.
Terraform by default for multi-cloud portability; CDK when the estate is TypeScript-heavy and AWS-only. Both are supported.
eu-west-2 (London) by default. eu-west-1 (Ireland) for services not in London when the workload allows.
For a defined period after go-live, yes. The goal is always an internal team able to operate the workload.
Yes — often via a lift-and-shift first phase, followed by targeted re-architecture. We recommend based on the workload, not the trend.
AWS bill or account structure keeping you up? Those are the problems we most often fix. Let's have a look.
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