If releasing takes a maintenance window and a nervous evening, the problem isn't your team's care — it's that the pipeline makes every release large and every rollback manual. We make releases small, reversible and unremarkable, and we bring the cloud bill down while we're in there.
Rollback tested, not assumed3–14 weeksAWS · Azure · Google Cloud
Cloud and DevOps engineering is the work that gets software into production reliably and keeps it there — migration, containers, deployment pipelines, monitoring, backups and cost control. Canopus does this on AWS, Azure and Google Cloud, and modernises legacy applications in staged slices rather than as a rewrite the business has to wait for.
Most clients start with one and add the next once the first pays for itself.
From on-premise, a colocation rack or an unmanaged VPS onto AWS, Azure or Google Cloud. We inventory what's actually running first — including the cron job on a machine nobody remembers provisioning — then move it in a sequence that keeps the business trading throughout.
Automated pipeline from merge to production: tests, container build, staging deploy, approval gate, production deploy, one-click rollback. Plus infrastructure as code, so environments are reproducible rather than reconstructed from memory.
Metrics, logs and traces in one place, alerts that fire on symptoms users feel rather than on CPU graphs, runbooks for the top failure modes, and a restore rehearsed rather than assumed. The goal is knowing before your customer tells you.
An application that still runs the business but nobody wants to touch — original developer gone, framework several major versions behind, deployment a manual ritual. We audit, then modernise in slices behind a routing layer, running old and new in parallel until outputs agree.
Six findings account for most of what we recover on a first cost review. None of them require re-architecting anything.
Scroll the diagram sideways to see all six findings.
The rewrite proposal is always attractive and almost always wrong. It asks the business to accept eighteen months of no new features in exchange for a promise, and it discards the accumulated edge cases that the old system quietly handles — the ones nobody documented because they were bug fixes, not requirements.
Instead: put a routing layer in front, move one capability at a time, run old and new in parallel and compare outputs until they agree, then cut that slice over. Slower on paper. Far more likely to finish.
All three will run your workload. The deciding factors are almost never technical.
The widest set of managed services we actually operate day to day, and region coverage for the data-residency cases named above. Our default unless something specific points elsewhere.
When you already run Entra ID, SQL Server and Microsoft 365. Identity integration and existing enterprise agreements are worth real money.
When analytics is the centre of gravity. BigQuery remains the strongest reason to choose GCP for a data-heavy platform.
If you run fewer than about ten services and have no platform engineer, Kubernetes will cost more in operational attention than it returns. A managed container runtime — ECS on Fargate, Azure Container Apps or Cloud Run — gives you the same deployment story with a fraction of the moving parts, and nothing stops you moving to Kubernetes later if scale genuinely demands it.
We say this on most first calls and it regularly reduces the size of the engagement. What we won't do is stand up a cluster you have no one to operate, hand over the kubeconfig and call that a platform.
Checkable claims, not "best practices applied".
Cost tracks three things: how many services move, whether you rehost as-is or re-architect into containers with CI/CD and infrastructure as code, and how much undocumented configuration lives only on the machines you're leaving. That last one is why we start with a two-week discovery rather than a quote. You leave discovery with a fixed written estimate, a cutover sequence and an inventory of what's actually running — yours whether or not we do the move.
AWS by default for breadth and hiring depth. Azure when you already run Entra ID, SQL Server and Microsoft 365 — the identity and licensing integration is genuinely worth money. Google Cloud when data and analytics are the centre of gravity, particularly with BigQuery. Any of the three will run your workload.
Usually, and the first pass is normally the largest. The recurring findings are the same: over-provisioned instances, non-production environments running nights and weekends, orphaned storage, unnecessary cross-zone transfer, and no savings plan on stable baseline load. We report what we found and what it saved rather than quoting a percentage upfront.
In slices, never as a big-bang rewrite. We put the legacy system behind a routing layer, move one capability at a time, run both in parallel until outputs agree, then cut that slice over. It takes longer on paper and it's the only approach that doesn't require the business to stop while engineering catches up.
Deploys stop being events. A merge runs tests, builds an image, deploys to staging and — once approved — goes to production with a one-click rollback. Teams that deployed monthly with a maintenance window usually end up deploying several times a week without one, because the risk per release drops as the release size does.
We set up monitoring, alerting and runbooks for every system we operate, with severity-tiered response times in the retainer. Genuine round-the-clock on-call is available as a specific engagement rather than assumed — we'd rather scope it honestly than let you believe someone is awake at 3am when the contract doesn't pay for it. See support tiers.
Yes, and it's common. We usually take the application delivery pipeline and observability while your team keeps network, identity and the platform boundary. The split works when it's written down in week one — including who is allowed to change what in production.
Region selection, encryption at rest and in transit, key management, access logging and retention policies are part of the build. Where you need SOC 2 or ISO 27001 evidence, we implement the technical controls and produce the artefacts — within the limits we're explicit about.
Those two things tell us most of what we need. An engineer replies within one business day with the specific things we'd look at first.