Technology
AWS Testing& Development
We test applications where they actually run: AWS. That means environment-parity validation across dev/stage/prod, failover and scaling behavior checks, CI/CD quality gates in CodePipeline or GitHub Actions, and test infrastructure (parallel runners, ephemeral environments) that keeps suites fast.
Environment-parity and configuration Testing across stages
Auto-scaling, failover, and recovery validation
CI/CD quality gates wired into AWS pipelines
Cost-aware ephemeral test environments
- Environments
- Ephemeral and persistent test environments as code
- Pipelines
- GitHub Actions, GitLab CI, Jenkins and CodePipeline integration
- Focus
- Resilience, scaling and configuration parity, not just deployment
- Also covers
- Azure and GCP under the same practices
Testing the environment, not just the code
In a cloud product, a large share of the behaviour that reaches users is not in the application code at all.
In a cloud product, a large share of the behaviour that reaches users is not in the application code at all. It is in autoscaling thresholds, load-balancer health checks, IAM policy boundaries, managed database failover, queue retry semantics and infrastructure definitions that have quietly drifted from what is deployed. Functional Testing never touches any of it, which is why those behaviours are usually observed for the first time during an incident.
Our AWS work covers both halves: the infrastructure that test suites run on, and the infrastructure behaviour that itself needs Testing. The second half is the one most teams are missing, and it is covered in depth on our Cloud Testing page.
Test infrastructure that keeps suites honest
Slow, shared, drifting test environments are one of the most common reasons Automation loses credibility, a red build caused by a stale environment teaches a team to ignore red builds.
Slow, shared, drifting test environments are one of the most common reasons Automation loses credibility, a red build caused by a stale environment teaches a team to ignore red builds. We build environments as code so they are reproducible, ephemeral where the architecture allows, and identical between a developer's branch and the release candidate.
That typically means containerised suite execution on ECS or EC2 runners sized for parallel work, S3-hosted reports and traces retained long enough to diagnose an intermittent failure, Secrets Manager or Parameter Store for credentials so nothing sensitive lives in a repository, and CloudWatch metrics correlated with test runs so a performance regression can be traced to a specific change.
Resilience, access and cost behaviour
Then we test the platform itself, in controlled windows with an owner watching: instance termination and availability-zone loss, RDS or Aurora failover timing, dependency latency injection, and whether autoscaling arrives quickly enough to matter at a real spike.
Then we test the platform itself, in controlled windows with an owner watching: instance termination and availability-zone loss, RDS or Aurora failover timing, dependency latency injection, and whether autoscaling arrives quickly enough to matter at a real spike. Alongside that, IAM roles assessed against least privilege, including what a compromised service role could actually reach, and backup restore rehearsed end to end and timed, because an untested backup is a hypothesis.
We also watch the spend curve under load. A resilience improvement that triples the cost of a peak hour is a decision the business should make deliberately, not discover on an invoice.
Engagement path
How the engagement runs
How a AWS engagement runs from first call to handover.
Architecture and drift review
Services, dependencies, scaling policies and failure domains mapped, and infrastructure as code compared against what is actually deployed.
Environments as code
Reproducible test environments with seeded data, so a failing build means a failing product rather than a stale environment.
Pipeline and observability wiring
Containerised suite execution, artefact retention, secrets handled properly, and CloudWatch correlated with test runs.
Resilience exercises
Controlled termination, failover and latency injection with a defined blast radius, plus a timed restore rehearsal.
Deliverables
What you get
Artefacts you keep and can run without us. Everything lives in your repositories and your pipelines.
Handover pack
6 artefacts · yours to keep
Reproducible test environments defined as code, with seeded data
Containerised suite execution sized for parallel CI work
Report, trace and artefact retention long enough to diagnose intermittent failures
Secrets handled through Secrets Manager or Parameter Store, never in the repository
Autoscaling and failover results with timings rather than assumptions
IAM least-privilege findings and a timed backup restore rehearsal
Self-check
Signs your team needs this
If more than one of these is true, it is usually cheaper to fix now than after the next release.
Test environments are shared, slow, and blamed for failures nobody investigates
Staging and production have drifted and the gap is undocumented
High availability is in the diagram and has never been exercised
Backups run nightly and a restore has never been rehearsed or timed
Cloud spend moves unpredictably when traffic does
A Senior Engineer replies within one business day. NDA first.
Questions
Frequently Asked Questions
Straight answers, written the way we'd say them on a call.
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