Cloud & DevOps

AWS, Google Cloud, and Azure — the right choice depends on your existing infrastructure and requirements, not a default we apply to every project.

Yes — auditing and improving an existing cloud setup is common work, not just greenfield builds.

Yes — real production monitoring is a standard part of any infrastructure engagement, not a separate add-on.

Capacity is sized to your actual traffic and growth trajectory, with monitoring on cost as well as performance, rather than provisioning by guesswork.

Yes — automated build, test, and deploy pipelines are part of how we ship every project, not just something we set up on request.

Whether it’s a greenfield setup or an audit-and-improve of existing infrastructure is the biggest factor — auditing and fixing an existing setup is often faster than it sounds, but depends on what’s found.

Provisioning by guesswork instead of real usage data. Overprovisioning wastes budget and underprovisioning breaks under real load — both come from skipping the monitoring that would have shown the real number.

Below the scale where you have enough services or traffic to benefit from automated scheduling and self-healing, a simpler managed-platform deployment is often the more proportionate choice — Kubernetes earns its real operational complexity once you’re genuinely past that threshold, not before.

Access to your existing cloud accounts and infrastructure (if any exist), a rough sense of current traffic and growth trajectory, and clarity on your actual availability requirements — a system that can tolerate brief downtime has very different infrastructure needs than one that can’t.

Whether it’s a greenfield setup or an audit-and-improve of an existing, possibly undocumented environment. Auditing and fixing existing infrastructure can be faster than a from-scratch build, but the real scope only becomes clear once the audit reveals what’s actually there.

Cost and capacity get right-sized on an ongoing basis as real traffic data comes in, and monitoring coverage tends to expand as new failure modes are discovered in production — infrastructure work doesn’t really end at "it deployed successfully," which is exactly what the Launch & Growth phase of our process covers.