Cloud & Operations
Cloud Computing Services
The cloud is not automatically cheaper or more reliable. It is a set of tools that reward good architecture and punish careless spend. We design for both, and we run what we build.
What Cloud Computing means in practice
Who it’s for: Teams building or running a system on the cloud where scale, reliability or the monthly bill has become something the business can feel, and who want the architecture, the resilience and the cost discipline handled by senior engineers who operate what they build.
Cloud computing is renting someone else’s data centre by the second and programming it like software. Instead of buying servers, you provision compute, storage, databases and networking on demand from AWS, Azure or Google Cloud, pay for what you use, and hand the physical estate (the racks, the power, the cooling, the hardware failures), to the provider. Done well, that means a system that scales when traffic arrives, recovers from a failed machine on its own, spans more than one data centre so a regional outage is survivable, and can be rebuilt from code rather than from someone’s memory. Done badly, it means a monthly bill nobody can explain, a pile of resources nobody remembers creating, and an architecture that is somehow both more expensive and less reliable than the servers it replaced.
This is the broad cloud-computing service: choosing and combining the right building blocks for a system and then operating them. That covers the service models: infrastructure as a service where you manage the operating system upward, platform as a service where the provider runs the runtime and you deploy code, and the managed and software-as-a-service pieces you consume rather than build. It covers the compute options: virtual machines for full control, containers for portability and density, serverless functions for spiky or event-driven work, and the managed databases, object and block storage, private networking, load balancing, auto-scaling, and the multi-region high-availability design that keeps a system up when a single zone or region does not. All of it declared as infrastructure as code, so an environment is reproducible and reviewable rather than clicked together once and feared thereafter.
We are honest about the two things the cloud is most often oversold on. First, it is not automatically cheaper: undisciplined cloud spend is one of the most reliable ways to burn money in modern engineering, through idle resources left running, over-provisioned instances sized for a peak that never comes, data-egress charges nobody modelled, and managed services chosen for convenience without checking the price at scale. Cost design and monitoring are therefore part of the architecture, not a spreadsheet exercise for later. Second, more cloud is not always better: a small, simple product is very often better and cheaper on a managed platform than on a full custom cloud build, and we will tell you when that is the case rather than sell you infrastructure you then have to operate for years.
What you get
- A cloud architecture chosen deliberately. The compute model (virtual machines, containers or serverless), the managed data and storage services, and the networking, matched to your actual workload, scale and team rather than to a reference diagram
- Everything declared as infrastructure as code, so every environment is reproducible, every change is reviewed before it lands, and the whole estate can be rebuilt or recovered from the repository instead of from tribal knowledge
- Auto-scaling and high-availability design, across availability zones, and across regions where the reliability requirement genuinely warrants it, so the system absorbs load and survives the failure of a machine, a zone or a data centre
- Managed databases, storage and networking configured for durability, backup, restore and least-privilege access, so the data layer is resilient and defensible from the start rather than after an incident
- A cost model built during design and monitored in production. The spend drivers named, budgets and alerts wired in, idle and over-provisioned resources caught early, because the bill is an architectural output, not a surprise
- Observability shipped with the system (logs, metrics, traces and dashboards), plus alerting and runbooks, so a production problem has a specific answer in minutes rather than a scramble
- Documentation and handover so your own team can operate, change and extend the environment safely, without the whole thing depending on the one person who built it
What Cloud Computing does for you
Elasticity that matches real demand
The single biggest reason to be on the cloud is that capacity can follow demand instead of being bought for a peak and paid for around the clock. We design auto-scaling against how your traffic actually behaves (the daily curve, the weekly pattern, the campaign spike), so the system adds capacity when load arrives and sheds it when the load leaves. That is the mechanism that turns the cloud’s pay-for-what-you-use model from a slogan into a lower bill, and it only works if the application and the scaling policy were built for it.
Resilience that is designed, not assumed
Cloud providers give you the raw materials for high availability (multiple availability zones, managed failover, cross-region replication), but none of it happens by default. We design where the system tolerates failure and where it does not: which components run across zones, which data is replicated and how, what the recovery time and recovery point actually are, and then we test the failover rather than trusting a diagram. The result is a system whose reliability you can state as a number and defend, not one that has simply not failed yet.
Cost as a first-class design constraint
Undisciplined cloud spend is a notorious, expensive problem, and it is almost always architectural: instances sized for an imagined peak, environments left running overnight and at weekends, storage tiers chosen without thought, and data-egress charges that nobody modelled until they appeared on the bill. We treat cost the way we treat performance and security: designed for, measured, and owned. That means right-sizing, the appropriate use of committed-use and spot pricing where it fits, budgets and alerts, and cost attributed back to teams and services so the numbers are visible to the people who can change them.
Why teams choose us for Cloud Computing
- You want the architecture chosen for your workload, your scale and the size of the team that will operate it, including being told when a managed platform or a small, simple setup is the right answer and a full custom cloud build would be over-engineering you pay to run for years.
- You need cost governance treated as engineering rather than a finance afterthought: the spend modelled during design, the drivers named, and monitoring, budgets and right-sizing built in, because the teams that get burned on the cloud are the ones who found out about the bill after it arrived.
- You want resilience that is designed and actually tested (availability zones, failover, backup and restore, multi-region where it is genuinely warranted), by engineers who have operated systems through real outages, not a high-availability diagram that has never been exercised.
- You want the whole environment as infrastructure as code, reviewed and reproducible, so your estate is something your own team can understand, audit and change safely, not a hand-built configuration that only one person understands and everyone is afraid to touch.
What Cloud Computing includes
The concrete pieces of work this covers, scoped to what your problem actually needs.
Compute: virtual machines, containers and serverless
We choose the compute model on the workload, not on fashion. Virtual machines when you need full control of the operating system or are running software that expects a conventional host. Containers when you want portability, density and a consistent artifact from a developer’s laptop to production, orchestrated on a managed platform so you are not running the control plane yourself. Serverless functions for spiky, event-driven or bursty work where paying per invocation and scaling to zero beats keeping a machine warm. Most real systems use a mix, and the skill is putting each piece of work on the model whose cost and operational shape fit it.
Managed databases and storage
We use the provider’s managed data services (relational databases, managed caches, object storage, block volumes), so that backups, patching, replication and failover are handled by the platform rather than by you at two in the morning. We configure them deliberately: the right instance size and storage tier for the access pattern, automated backups with a tested restore, encryption at rest, and least-privilege access so the data layer is durable and defensible. Managed services are convenient, but their pricing at scale has to be checked rather than assumed, and we do that before it becomes a line on the bill.
Networking, load balancing and delivery
A cloud system lives inside a private network you design: subnets, routing, security groups and the boundary between what is public and what must never be reachable from the internet. We build load balancing and health checks so traffic goes only to healthy instances and drains cleanly from failing ones, place content delivery and caching in front of the workload where it lowers both latency and egress cost, and pay deliberate attention to data-transfer paths, because cross-zone, cross-region and internet egress charges are one of the most commonly overlooked drivers of a surprising bill.
Auto-scaling and high availability
We design the system to add and remove capacity automatically against real demand signals, and to keep serving when parts of it fail. That means running across multiple availability zones by default, health-checked so a failed instance is replaced without a human, and (where the reliability requirement genuinely justifies the cost and complexity), spanning multiple regions with a considered story for data replication and failover. We are clear-eyed that multi-region is expensive and non-trivial, so we recommend it when the cost of downtime actually warrants it and not as a reflex.
Infrastructure as code and environments
Every part of the environment is declared in code and provisioned from it, so a change is reviewed before it lands, an environment is reproducible rather than a one-off, and the entire estate can be rebuilt or recovered from the repository. That is what makes staging genuinely resemble production, what turns a disaster-recovery plan from a hope into a procedure, and what stops infrastructure from being a fragile thing that only one person understands. Clicking resources together in a console is faster on day one and a liability on every day after.
Cost governance and FinOps
We treat cost as an ongoing engineering discipline. During design we model the expected spend and name its drivers; in production we tag and attribute cost back to services and teams, set budgets and alerts so a runaway resource is caught in days rather than at month end, right-size instances against actual utilisation, apply committed-use discounts and spot capacity where the workload fits them, and hunt down the idle, forgotten and over-provisioned resources that quietly accumulate. The point is that the bill is something you steer continuously, not a number you receive and flinch at.
Where it fits
A growing product that has outgrown its launch infrastructure
A product that launched on a single server or a hand-built setup and is now straining: traffic peaks cause slowdowns, scaling means manual intervention, and there is no real failover. We rebuild it as a code-defined, auto-scaling, multi-zone architecture on the compute model that fits the workload, with the data layer on managed services and the whole thing recoverable from the repository, so growth stops being a recurring emergency and becomes something the system handles on its own.
A system where downtime has a real, countable cost
A platform where an outage means lost revenue, missed transactions or a reputational hit that the business can put a number on. Here the engineering centres on high availability and recovery: running across availability zones, health-checked failover, backups with a tested restore, defined recovery-time and recovery-point objectives, and (where the numbers justify it), a multi-region posture. We design the resilience to the actual cost of downtime rather than to a generic promise of five nines nobody costed.
A cloud bill that has grown past anyone’s understanding
An estate whose spend is climbing faster than usage, where nobody can fully say what each dollar is buying. We map the environment, attribute the cost to services and teams, and find the drivers: the oversized instances, the idle and forgotten resources, the environments running around the clock, the egress nobody modelled, then right-size, apply the appropriate pricing models, and wire in the budgets, tagging and alerts that keep it under control. The outcome is a lower, explained bill and a system that stays that way.
Modernising ageing or on-premises infrastructure
An organisation moving off on-premises servers or a rigid rented estate onto the cloud, wanting to modernise rather than merely relocate. We design the target architecture, decide honestly what should be rehosted as-is, what should be re-platformed onto managed services, and what genuinely warrants rebuilding, and we deliver it as infrastructure as code so the new estate is reproducible and legible from day one, rather than recreating the old server-by-hand problem in a more expensive place.
How we approach Cloud Computing
We start from the workload and the constraints, not the service catalogue. What actually has to run, how its traffic behaves over a day and a week, how much a minute of downtime costs, where the data is allowed to live, and how big the team that will operate it really is: those answers decide whether the right shape is a couple of virtual machines, a container platform, a set of serverless functions, or a managed platform that means you build far less cloud than you think. We would rather talk you out of infrastructure than hand you an architecture that is impressive on a diagram and a burden to run.
From there we build the environment as code and prove it in thin, real slices rather than in one big provisioning event. Cost, resilience and security are designed in from the first slice: an instance is right-sized when it is created, a database has backups and least-privilege access from the day it exists, egress paths are understood before they appear on a bill, because these are exactly the things a cheaper build defers and you discover missing during the first outage or the first invoice. Because the engineers who design the environment are the ones who operate it, the architecture is shaped by what it is actually like to run at three in the morning, not only by how it reads in a proposal.
How the engagement runs
We open by understanding the workload and the constraints before we touch a console. What has to run and how its traffic behaves, what a minute of downtime actually costs, where the data is legally allowed to live, what the compliance requirements are, and how large the team that will operate the result really is. Those answers decide the architecture: the compute model, the managed services, the networking, and the degree of high availability the reliability requirement genuinely warrants. This is also where we model the expected cost and name its drivers, and where we say plainly if a managed platform or a deliberately simple setup would serve you better than a full custom cloud build. We write the design down so your team can review the trade-offs while they are still cheap to change.
From there we build the environment as infrastructure as code and prove it in thin, real slices: a working piece of the system, provisioned from the repository, right-sized, backed up, least-privilege, observable, rather than one large provisioning event you cannot inspect. Cost controls, resilience and security go in from the first slice, not a hardening phase at the end, because those are the parts a rushed build skips and an outage or an invoice later exposes. Because the people building the environment are the ones who will operate it, the architecture is shaped by what it is like to run and pay for, and we finish with the observability, runbooks and documentation your team needs to own it.
How we architect it
We architect against the five pillars the major providers all converge on, because they encode hard-won operational reality: reliability, security, performance efficiency, cost optimisation and operational excellence. Reliability means designing for failure: multiple availability zones, health-checked failover, tested backup and restore, and recovery objectives you can state as numbers. Security means least privilege and the shared-responsibility model taken seriously. Performance efficiency means the compute and data services fit the workload’s shape rather than being oversized for comfort. Operational excellence means everything is code-defined, observable and runnable by a team, not a person. And cost optimisation sits alongside the other four as a first-class concern rather than beneath them.
That last point is where we differ most sharply from the reflex to add cloud. Cost is an architectural property: the choice of compute model, the instance sizing, the storage tiers, the data-transfer paths and the decision to run something around the clock or scale it to zero all show up on the bill, and they are far cheaper to get right in the design than to unpick in production. So we right-size from the start, keep egress paths deliberate, use committed-use and spot pricing where the workload genuinely fits, and (crucially), resist over-engineering. A small, simple product does not need multi-region active-active and a container platform; it needs a managed service and a sensible amount of redundancy. We build the level of sophistication the system’s scale and reliability requirements actually justify, and no more, because every piece of infrastructure is something someone has to operate and pay for for years.
Security
Cloud security starts with understanding the shared-responsibility model, because most cloud breaches are not the provider’s failure. They are customer misconfigurations. The provider secures the physical data centres, the hardware and the underlying platform; you remain responsible for how you configure it: who can access what, how the network is segmented, whether storage is public or private, and how identities and secrets are managed. A publicly exposed storage bucket, an over-permissive access role, a database reachable from the internet: these are the failures that make the headlines, and every one of them is on the customer side of the line. We design so those mistakes are hard to make: the boundary between public and private is deliberate, and nothing is exposed that does not need to be.
The organising principle underneath that is least privilege. Every identity (human or machine), gets exactly the permissions it needs and no more, so a compromised credential or a mistaken action has a small blast radius rather than the run of the account. We use the provider’s identity and access controls to scope permissions tightly, keep secrets in a managed secret store rather than in code, configuration files or environment variables baked into an image, encrypt data at rest and in transit by default, and enable the audit logging that lets you see who did what. Because the environment is infrastructure as code, its security posture is reviewable in a pull request and reproducible across environments, rather than a set of console settings someone hopes is still correct, which is also what makes it survive an audit without a scramble.
Signs it’s time
- Your product is growing and the infrastructure it launched on cannot scale with it, traffic peaks cause slowdowns or outages, and adding capacity is a manual, anxious, all-hands event rather than something the system does on its own
- Reliability has become a business problem: a single machine, zone or data centre is a single point of failure, and an outage takes the whole product down because nothing was designed to fail over
- The cloud bill is climbing faster than usage and nobody can fully account for it (idle resources, oversized instances, unexplained egress), and you need the spend modelled, attributed and brought under control
- You are modernising ageing infrastructure, on-premises servers, a single rented box, or a cloud estate that grew by hand, and you want it rebuilt as reproducible, code-defined infrastructure that a team can reason about and change safely
Our working method
The organising belief is that the cloud is a set of powerful tools that reward good architecture and punish carelessness, so the discipline is in the decisions, not the dashboard. We fit the compute model, the managed services and the degree of resilience to the actual workload and the actual cost of downtime, rather than reaching for the most sophisticated pattern available. We treat cost as an engineering concern from the first design session (modelled, measured, attributed and owned), because undisciplined spend is the single most common way the cloud disappoints the businesses that adopt it, and it is entirely avoidable with design that takes the bill seriously. And we declare everything as code, because an estate you can review, reproduce and rebuild is the difference between infrastructure a team controls and infrastructure that controls the team.
Running through all of it is the honest call about how much cloud you actually need. Part of the method is being willing to recommend less: a managed platform over a bespoke build, a single well-designed region over a costly multi-region posture the reliability requirement does not justify, a smaller instance than the anxious default. The engineers who design your environment are the ones who operate it, so they have every incentive to build something that is calm to run and cheap to keep running, not something that is impressive to present and painful to own. We would rather talk you out of complexity than bill you for operating it for years.
Technologies we build it with
Chosen per problem, not per fashion. This is the stack we most often reach for on this work.
How we deliver
- 01
Discover
We map the system, the constraints and the business it serves, including the parts nobody documented.
Architecture brief
- 02
Architect
Decisions get made, written down and defended before a line of production code exists.
Decision records
- 03
Build
Short cycles against working software. You see progress in the product, not in a status deck.
Shipping increments
- 04
Operate
Monitoring, incident response and iteration. The system is alive, so the engagement is too.
Runbooks & SLOs
Want a straight answer on Cloud Computing?
A short call with a senior engineer, before you write a brief. If Cloud Computing is the wrong answer for your situation, we will say so and tell you what we think is right.
What changes
Scales without heroics
Auto-scaling and high-availability design so the system absorbs traffic and survives the loss of a machine or a zone on its own, instead of an on-call engineer adding capacity by hand during the incident.
A bill you can explain
Spend modelled during design and monitored in production, with the drivers named and the idle and over-provisioned resources found, so the invoice is a number you understand and control, not a monthly shock.
Rebuildable from code
The whole environment declared as infrastructure as code, so it is reproducible, reviewable and recoverable. A system a team can change with confidence rather than one nobody dares touch.
Industries we serve
Domain knowledge changes what gets built. A few of the sectors we know before the first meeting.
How pricing works
- Fixed-scope build for a well-defined environment. A new cloud architecture, a specific auto-scaling and high-availability setup, or an infrastructure-as-code rebuild of a known estate, quoted once the workload, the target architecture and the cost model are understood, so you are not paying for scope nobody has pinned down.
- Monthly senior engagement for cloud environments that evolve with the product, where you want continuity, ongoing cost governance and someone genuinely on call for the infrastructure rather than a one-off handover that drifts out of date.
- Focused cost or reliability review. A FinOps assessment that finds and attributes the spend drivers, a resilience and high-availability audit, or an infrastructure-as-code and security review of an estate that grew by hand, priced by the assessment.
- In every case the provider’s own charges, the AWS, Azure or Google Cloud bill for compute, storage, data transfer and managed services, are billed to your own accounts and paid directly by you, so the infrastructure is yours and its cost is fully visible to you rather than marked up through us.
Typical timeline
- 01
Architecture and cost model
One to two weeks understanding the workload, the reliability and compliance requirements and the traffic pattern, deciding the compute model, managed services, networking and level of high availability, and modelling the expected spend, captured as a design your team reviews before anything is provisioned.
- 02
First environment in code
Two to three weeks provisioning a real, working slice of the system from infrastructure as code (right-sized, backed up, least-privilege, observable, with cost controls and alerts in place), proving the architecture against real workload and real failure and cost behaviour.
- 03
Build-out, scaling and resilience
Iterative delivery of the rest of the estate in short cycles, auto-scaling, multi-zone and where warranted multi-region high availability, the managed data and networking layers, and the pricing optimisations. Each change code-reviewed and shippable rather than retrofitted at the end.
- 04
Hardening, observability and handover
A security and configuration review against the shared-responsibility model, tested failover and restore, dashboards, alerting and runbooks in place, cost monitoring and budgets live, and documentation so your team can operate, pay for and extend the environment without us.
What working with us actually means
Senior engineers only
The cloud is easy to stand up and easy to get quietly, expensively wrong. The public storage bucket, the over-permissive role, the instance sized ten times too large, the egress nobody modelled. The people designing yours have operated cloud systems where those mistakes had consequences, and they build to avoid them. No juniors learning high availability and cost control on your production account.
We operate what we build
We run the cloud environments we ship, so the parts that only matter under load, under failure and on the invoice (auto-scaling that actually fires, failover that actually works, cost that stays controlled), go in from the start rather than after the first outage or the first shocking bill. We optimise for the years a system has to stay up and affordable, not for a diagram that looks clean in a proposal.
Honest about cost and about scope
We will tell you the cloud is not automatically cheaper, model the spend before you commit, and recommend a managed platform or a deliberately simple setup when a full custom cloud build would be over-engineering you pay to operate for years. The infrastructure we do not add, and the spend we design out, is the operational cost you do not carry.
Cost and resilience as core engineering
Because the bill and the uptime are what a business actually feels, we treat cost governance and high availability as first-class engineering rather than things to bolt on later. That discipline is the difference between a cloud estate a business can depend on and afford, and one that is somehow both more expensive and less reliable than the servers it replaced.
How to engage us
Three ways to work with us on this, chosen to fit the problem, not our margin.
- Dedicated team A standing team that works only on your product, in your rituals and your tooling. Best when the roadmap outlives the project. Ongoing product development
- Staff augmentation Named senior engineers embedded into your existing team, reporting into your leads. Best when you know what to build and need capacity. Filling a capability gap
- Software outsourcing A defined outcome delivered end-to-end by an accountable team. Best when you want the result owned, not just the hours filled. Outcome-owned delivery
Related services
Part of Cloud Engineering. Other work we do alongside this.
Common questions
How is this different from your Cloud Migration, Serverless and DevOps services?
This is the broad service: designing, building and running applications and infrastructure on the cloud, across all the building blocks, compute, managed data, networking, scaling, high availability and cost governance. Cloud Migration is the specific project of moving an existing system onto the cloud, or between providers, with the least disruption. Serverless is one particular compute model within this service (functions and managed services that scale to zero), which we use where the workload fits it, not as the answer to everything. DevOps is about how software gets delivered onto whatever infrastructure exists: the pipelines, automation and release practices. They overlap and an engagement often touches several, but this service is the architecture and operation of the cloud environment itself. It sits under our broader cloud-engineering pillar and draws on our AWS, Azure and Google Cloud technology work.
Is the cloud actually cheaper than running our own servers?
Not automatically, and anyone who promises it is has not looked at enough cloud bills. The cloud can be cheaper when you genuinely exploit its elasticity: paying for capacity only when you need it, scaling to zero when you do not, and avoiding the cost of buying and operating hardware for a peak that is rare. But it is frequently more expensive than the servers it replaced, because undisciplined spend is the default outcome: idle resources left running, instances sized for an imagined peak, environments running around the clock, storage tiers chosen carelessly, and data-egress charges nobody modelled. The cloud pays off when the architecture is designed for its cost model and the spend is monitored and owned. That cost discipline is precisely what we build in, and it is why we model the bill during design rather than letting you discover it afterwards.
Should we use virtual machines, containers or serverless?
It depends on the workload, and most real systems use a mix. Virtual machines suit software that expects a conventional host or where you need full control of the operating system. Containers suit workloads that benefit from portability, density and a consistent artifact from development to production, orchestrated on a managed platform so you are not running the control plane yourself. Serverless functions suit spiky, event-driven or bursty work where paying per invocation and scaling to zero beats keeping a machine warm, though they bring their own trade-offs around cold starts, execution limits and vendor coupling. The skill is putting each piece of work on the model whose cost and operational shape actually fit it, rather than committing the whole system to one because it is the model of the moment. We choose against your workload and show you the trade-offs.
Do we need a multi-region, highly available setup?
Probably less than you think, and the honest answer is driven by the cost of downtime rather than by ambition. Running across multiple availability zones within a region is sensible for most production systems and is comparatively cheap insurance against a single machine or zone failing. Going further (spanning multiple regions with replicated data and cross-region failover), is genuinely expensive and operationally complex, and it only pays for itself when a regional outage would cost the business enough to justify it. For a great many products, a single well-designed region with multi-zone redundancy and a tested restore is exactly the right level, and multi-region would be paying to operate resilience you do not need. We design the reliability to the actual cost of downtime and tell you plainly where the sensible line is.
Who is responsible for security, us or the cloud provider?
Both, and understanding the split is where cloud security starts. Under the shared-responsibility model, the provider secures the physical data centres, the hardware and the underlying platform, while you remain responsible for how you configure it: identity and access, network segmentation, whether storage is public or private, secret management, and encryption settings. The uncomfortable truth is that most cloud breaches are not the provider’s failure: they are customer misconfigurations, like a public storage bucket or an over-permissive access role. So we design your side of the line to make those mistakes hard: least privilege for every identity, nothing exposed that does not need to be, secrets in a managed store, encryption on by default, audit logging enabled, and the whole posture declared as reviewable code rather than console settings someone hopes are still correct. That is also what lets the environment pass an audit without a scramble.
Thinking about Cloud Computing?
Tell us the problem in your own words, not in requirements. A senior engineer reads it and comes back with a straight view on whether Cloud Computing is the right answer here, or what would be.
- 01A senior engineer reads it. Not a form queue, and not an account manager.
- 02We reply either with questions or with a straight answer that we are not the right fit.
- 03If it looks like a fit, a technical call with the person who would actually run the delivery.
- 04Then scope, effort and risk in writing, before anyone signs anything.