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AWS · Azure · Kubernetes · CI/CD · FinOps
The goal is a release that nobody schedules a Friday around, an environment that can be rebuilt from a repository, and a monthly bill where every line has an owner.
The problem
Two failure patterns show up repeatedly. The first is the lift-and-shift that moved virtual machines into a cloud account without changing anything, and now costs more than the rack it replaced, because a server that ran at eight percent utilisation in a data centre still runs at eight percent utilisation when it is billed by the hour.
The second is the modernisation that went too far. A four-person team running nine microservices, a service mesh and a managed Kubernetes cluster for an application with a few thousand users. Every deployment involves three repositories, nobody can reproduce a bug locally, and the platform now needs a full-time person it does not have.
The right shape is usually in the middle and it depends on facts rather than preference: how the application holds state, whether it can run more than one instance, what the release frequency actually is, and how much operational capacity exists in the team. A migration plan written without those answers is a guess with a diagram attached.
There is also the part nobody enjoys discussing. Backups that have never been restored are not backups, and a disaster recovery document that has not been rehearsed is an essay. Both come up in every assessment we run, and both are cheap to fix compared with finding out during an incident.
What is included
9 areas of work. Most engagements start with one or two and widen once the first release is in use.
Account and subscription structure, networking, identity and permissions, managed databases and storage lifecycle. Landing zone work that is dull to do and expensive to retrofit once forty resources have been created by hand in one flat account.
EKS, AKS and GKE where the workload genuinely calls for it: multiple services, autoscaling, mixed runtimes. We will also say when ECS Fargate, App Service or a couple of well-managed virtual machines will serve you better for less operational cost.
Build, test, scan and deploy on every merge, with environment promotion, approval gates where they matter and one-command rollback. Blue-green or canary releases where downtime is expensive, and a pipeline fast enough that people do not start avoiding it.
Terraform or Bicep with remote state, modules and plan review in pull requests, so staging and production differ by variables rather than by history. Manual console changes stop being the way things get done, which is what makes an environment reproducible.
Structured logs, metrics and traces that connect a slow request to the query behind it. Alerts tied to symptoms users feel rather than to every CPU spike, service level objectives agreed with the business, and an on-call runbook that names who does what.
Tagging and cost allocation so spend has owners, right-sizing against real utilisation, storage lifecycle rules, savings plans and reserved capacity for steady workloads, and a budget alert that arrives before the invoice does.
Dependency and container scanning, secret detection before commit, signed images, least-privilege deployment roles instead of long-lived keys, and infrastructure policy checks in the pipeline. Findings triaged into what to fix now and what to accept, rather than a thousand-row report.
Recovery objectives agreed in plain terms, backups that are immutable and off-account, and restore drills on a schedule with the results written down. The number that matters is how long a restore took last time it was tried, not how often the backup job succeeds.
Ongoing patching, monitoring response, cost review and capacity planning under a defined arrangement, for teams without a platform engineer. Scoped so your developers keep ownership of the application and we keep the platform boring.
How we work
We start with an assessment: what is running, what it costs, how it is deployed today, where state lives, and what happens when a specific machine dies. The output is a written picture of the current state with the risks ranked, and often two or three cost reductions that need no architectural change at all.
Then we move in the order that reduces risk fastest, which is rarely the order that looks best on a slide. Usually that means infrastructure described in code and a repeatable pipeline first, monitoring and alerting second, and only then the architectural changes, because making changes to an environment you cannot rebuild or observe is how a migration becomes an outage.
Every change lands as reviewed code with a rollback path. Cutovers are rehearsed in a non-production environment, run with a written runbook, and scheduled at a time the team can watch. Handover is a working session with your engineers plus documentation they can act on, not a slide deck and a set of credentials.
In practice
We start with an assessment: what is running, what it costs, how it is deployed today, where state lives, and what happens when a specific machine dies. The output is a written picture of the current state with the risks ranked, and often two or three cost reductions that need no architectural change at all.
Where it comes up
Stack
We pick from a stack we actually run in production. Where your team already has a preference and it is a reasonable fit, we work in yours instead of arguing for ours.
Sector context
Domain knowledge shortens discovery. These are the sectors where we already understand the vocabulary and the failure modes, so the first version of the build is closer to right.
Shift-based production data, downtime causes, quality records and the daily gap between what the ERP holds and what the floor actually did.
Appointments, records, billing and reporting, where patient data handling and clinical workflow set the constraints before anything is designed.
Lot and parcel movement, job-work with karigars, grading records and the reconciliation that follows a stone through five pairs of hands.
We have not published a case study for this service yet. When we do, it will name the client with written permission and quote numbers the client can defend. Nothing else is worth reading.
Until then, the useful version of proof is a conversation about your problem. Ask us how we would approach it, what we would refuse to do, and where the estimate is soft.
Ask about comparable workQuestions
Probably not yet. Kubernetes pays for itself when you run several services with different scaling profiles and have someone who can operate it, and it is a heavy tax on a small team running one application. Managed container services or well-automated virtual machines cover a large share of business workloads at a fraction of the operational cost. If you already run it and it is causing pain, the useful question is which parts you actually use.
We will not put a percentage on it before looking, because savings depend entirely on what is running and how it was set up. What we can say is where the money usually sits: instances sized for a peak that never arrives, non-production environments running at night and at weekends, storage with no lifecycle policy, orphaned volumes and load balancers, and on-demand pricing for workloads that never turn off. The assessment names your specific items with the saving attached to each.
Often with a very short, planned window rather than none at all, and the honest version depends on the data layer. Stateless application tiers can be moved with parallel running and a DNS switch. Databases need either replication with a brief cutover or an accepted maintenance window. We would rather plan a twenty-minute window at 2am on a rehearsed runbook than promise zero and improvise.
Yes, that is the normal arrangement. Accounts, subscriptions, domains and repositories stay in your name and we work with scoped access that can be revoked in a minute. Where access has to be broad for a migration, it is time-limited and logged. We do not resell hosting or place your infrastructure under our billing.
That is worth surfacing early, because a pipeline nobody trusts gets bypassed and the problem returns. We involve the engineers who will live with it in the design, keep the pipeline fast enough that it does not feel like an obstacle, and prefer a simple setup the team can debug over a sophisticated one they cannot. Adoption is part of the work, not an afterthought.
Works well with
Send a paragraph about what is not working today. You will get a written reply from someone who would be on the build, covering how we would approach it, roughly what a first phase looks like, and whether cloud & devops is even the right place to spend the budget.
What happens next
