Cloud Infrastructure
Cloud environments that are reproducible, observable and sized to real usage rather than to guesswork.
Cloud infrastructure fails quietly. It works at launch, drifts as it is hand-tuned, grows expensive as forgotten resources accumulate, and becomes something nobody wants to touch because nobody can reproduce it. The fix is defining infrastructure as code and sizing it against measurements rather than assumptions.
Who this is for
Companies whose cloud bill has grown faster than their traffic. Teams whose production environment was configured by hand and cannot be recreated. Products approaching a load their current setup will not survive.
Problems we solve
- Snowflake servers. Hand-configured production that cannot be reproduced or safely replaced.
- Environment drift. Staging and production diverged, so testing proves little.
- Unexplained cost. No attribution, no tagging, and resources nobody remembers creating.
- Over-provisioning. Capacity sized for an imagined peak that never arrives.
- Untested backups. Backups running, restores never attempted.
- Single points of failure. One instance, one zone, and no plan for its loss.
What we build
- Infrastructure as code, so environments are reproducible and reviewable
- Containerised deployment with parity between local, staging and production
- Managed databases with automated backups and tested restore procedures
- Caching and CDN layers placed where measurements show they help
- Autoscaling where load genuinely varies, fixed capacity where it does not
- Network and access hardening: security groups, secret management, least privilege
- Cost tagging and reporting, so spend is attributable
How we work
We measure before sizing, because most infrastructure is provisioned against a guess and stays that way. Environments are defined in code so they can be recreated and reviewed. Restores are tested, not assumed. We prefer managed services over self-managed where the price difference is smaller than the operational burden, and we size the whole setup to the team who will run it after we leave.
Technologies we use
AWS and comparable cloud platforms, Docker for containerisation, Nginx, managed PostgreSQL and MySQL, Redis, object storage with CDN, and infrastructure defined as code. Monitoring and log aggregation proportionate to the scale.
Business benefits
- Environments recreated in minutes rather than reconstructed from memory
- Spend matched to actual usage, with visible attribution
- Recovery from failure is a tested procedure
- Capacity that holds through growth and campaign peaks
Where it pays off
- Migrating from shared hosting or a hand-configured VPS
- Reducing a cloud bill that has drifted past its value
- Preparing for a launch or seasonal peak
- Adding redundancy to a single-instance production system
- Establishing a staging environment that matches production
Common questions
Which cloud provider should we use?
Usually the one your team already knows. The operational differences matter more than the feature differences for most workloads, and switching for marginal pricing rarely pays back the migration cost.
Do we need autoscaling?
Only if load genuinely varies. For steady traffic, fixed capacity is cheaper and simpler, and autoscaling adds complexity without benefit.
Whose account does this run in?
Yours, under your billing, with access you can revoke. We do not resell hosting or sit between you and your provider.
Cloud bill growing faster than your traffic? A cost and capacity review usually pays for itself.