Cloud & Operations
DevOps Engineering Services
We make releasing boring, automated pipelines, infrastructure as code, real observability and safe rollout strategies, so shipping goes from a scheduled, nerve-wracking event to something you do whenever a change is ready.
What DevOps means in practice
Who it’s for: Teams whose releases have become slow, manual or frightening (where deploying is a scheduled event people brace for), and who want to ship more often with less risk, built by engineers who run production themselves.
DevOps is not a job title you hire for or a tool you buy. It is a way of working, and treating it as anything else is how organisations end up with a "DevOps engineer" who is really just the person who owns the deploy script nobody else understands. The actual substance is automation, ownership and feedback loops: getting a change from a developer’s machine into production quickly, safely and often, with a fast signal when something goes wrong and an easy way to undo it. Everything else (the pipelines, the Terraform, the containers, the dashboards), is in service of that, and none of it is worth building for its own sake.
The work is concrete. We build the CI/CD pipeline that tests and ships every change, the infrastructure-as-code that makes an environment reproducible rather than a hand-clicked artefact only one person can recreate, the containerisation and orchestration that make what runs in production the same thing that ran in testing, the observability that turns "something feels slow" into "here is what, and where, in ninety seconds", and the release strategies that let you roll out to a slice of traffic and pull back instantly if it misbehaves. The goal throughout is faster, safer delivery, and it is measurable, which matters, because DevOps attracts more hand-waving than almost any other discipline.
Our angle is that we operate what we build. Our DevOps is grounded in running production systems and being on the hook when they break, not in a slide deck about the ideal pipeline. That experience mostly shows up as restraint: we will right-size the tooling to your team rather than reaching for the full Kubernetes-and-GitOps stack a five-person team does not need and cannot maintain. Heavy machinery that nobody can operate is not sophistication, it is a liability wearing a nicer badge, and we would rather leave you with something boring that works than something impressive that pages someone at 3am.
What you get
- A CI/CD pipeline that runs your tests, builds a versioned artefact and deploys it on every change, so shipping is a push, not a runbook someone follows by hand
- Infrastructure defined as code in Terraform, so every environment is reproducible, reviewable in a pull request and recoverable, rather than a thing someone clicked together once and hopes nobody touches
- Containerisation with Docker and, where the scale genuinely justifies it, orchestration with Kubernetes, so what runs in production is the same artefact that passed testing (see our Docker and Kubernetes technology pages)
- Automated quality and security gates in the pipeline (tests, dependency and secret scanning, static analysis), that stop a bad or unsafe change before it reaches production, not after
- Observability shipped with the system: metrics, logs, traces and alerting that page a human on symptoms that matter and stay quiet otherwise, so an incident is diagnosed in minutes rather than archaeology
- A safe release strategy (blue-green or canary as fits your system), with a rollback that is one command or one click, so a bad deploy is an inconvenience rather than an outage
- A baseline of your DORA metrics (deployment frequency, lead time for changes, change-failure rate and time to restore), so the improvement is measured, not asserted
What DevOps does for you
Small changes, shipped often, are safer
The counter-intuitive heart of DevOps is that deploying more often makes releasing safer, not riskier. A release that batches a month of changes is a large, tangled thing where a failure is hard to diagnose because so much moved at once. Ship each change on its own, the moment it is ready, and a failure points straight at a small, recent, understandable diff. Frequent, small releases are how you get both speed and safety at the same time, rather than trading one for the other.
Reproducible environments end a whole class of bug
When infrastructure is defined as code and what runs in production is a container built once and promoted through your environments, "it works on my machine" and "it worked in staging" stop being things anyone says. The environment is no longer a hand-assembled snowflake that drifts from every other one; it is a reviewable, version-controlled definition you can stand up identically anywhere. That reproducibility eliminates the configuration-drift bugs that eat days and never reproduce where you can catch them.
Fast feedback keeps engineers in flow and fixes cheap
A problem caught by the pipeline minutes after a developer pushes, while the change is still fresh in their head, costs minutes to fix. The same problem discovered in production a week later costs an incident, a context-switch back into forgotten code, and the trust of whoever hit it. Automated gates and quick, clear feedback keep the cost of a mistake at the cheap end of that range, and keep your engineers shipping rather than firefighting.
Why teams choose us for DevOps
- You want DevOps from people who operate what they build and have been on the end of the pager, so the pipelines and rollout strategies are shaped by what actually goes wrong in production at 3am, not by the tidy diagram in a conference talk.
- You want the tooling right-sized to your team, with someone who will talk you out of Kubernetes and GitOps when a simpler deploy would serve you better, rather than selling you the heaviest stack because it is fashionable and billable.
- You want the improvement measured against DORA metrics from a real baseline, so you can see deployment frequency and recovery time move, not take it on faith that things are better because there is a pipeline now.
- You want to be left self-sufficient: pipelines, infrastructure and observability your own engineers understand and can operate and extend, documented and handed over, not a bespoke platform that makes you dependent on us to ship.
What DevOps includes
The concrete pieces of work this covers, scoped to what your problem actually needs.
CI/CD pipelines that test, build and deploy
We build the pipeline that takes a change from a merged pull request to running in production without a human running commands. It runs your test suite, builds a versioned, immutable artefact once, and promotes that same artefact through your environments so what you tested is exactly what you ship. We build it on whatever you already run (GitHub Actions, GitLab CI, or the equivalent), staged so fast checks gate every commit and slower ones run where they earn their place, and we keep it fast, because a pipeline that takes forty minutes is a pipeline people learn to route around.
Infrastructure as code with Terraform
We define your infrastructure as Terraform, so an environment is a reviewable, version-controlled definition rather than a set of console clicks nobody wrote down. That means a change to infrastructure goes through the same pull request and review as a change to code, a new environment is a plan-and-apply rather than a fortnight of manual setup, and a lost environment is recoverable from the definition rather than from someone’s memory. Managed state, sensible module structure and a plan you read before you apply, so you always know what is about to change before it does.
Containerisation and orchestration
We containerise your applications with Docker so the unit that runs in production is the same self-contained artefact that passed testing, which retires an entire family of environment-mismatch bugs. Where the scale and team genuinely justify it we orchestrate with Kubernetes for self-healing, rolling updates and horizontal scaling, and where they do not, we say so plainly and reach for something lighter, because Kubernetes is real operational weight and imposing it on a small team is a common and expensive mistake. Our Docker and Kubernetes technology pages go deeper on where each fits.
Automated testing and quality gates
The pipeline is where you stop bad changes cheaply, so we wire the gates in: your automated tests run on every change, and a failure blocks the merge rather than reaching production. Where you do not yet have the test coverage to gate on, we are honest that a pipeline shipping untested code faster is not an improvement, and we will point you at building that safety net first. The gates are only as good as what they check, automation shipping broken code quickly is just a faster way to fail.
Observability, metrics, logs, traces and alerting
You cannot operate what you cannot see, so we ship observability with the system rather than bolting it on after the first outage. Metrics for the health and throughput of the system, structured logs you can actually search, distributed traces that follow a request across services to find where the time went, and alerting tuned to page a human on symptoms that matter (a rising error rate, a breached latency budget), while staying quiet the rest of the time. Alert fatigue is its own failure mode; an alert nobody trusts is worse than no alert, so we tune for signal, not noise.
Release strategies with fast, safe rollback
A safe deployment is one you can undo instantly, so we build the rollout strategy that fits your system. Blue-green, where a fully new version stands up alongside the old and traffic switches over in one step, and switches straight back if it misbehaves. Or canary, where a change goes to a small slice of traffic first, watched against your metrics, and is either promoted or pulled before it reaches everyone. Either way the point is the same: a bad release is a fast, contained rollback, not an outage and a frantic hotfix.
Where it fits
Replacing a manual, one-person deploy process
A team where releasing means one engineer SSHing into servers or running a script only they understand, so shipping stops when they are away and every deploy carries the risk of a missed step. We build a pipeline that makes deployment a reviewed, automated push anyone on the team can trigger, with the steps that used to live in someone’s head encoded, tested and repeatable, turning the deploy from a person into a process.
Taming slow, scary, infrequent releases
An organisation that deploys once a month because releasing is painful, which makes each release a large batch of accumulated change, which makes it riskier and more painful still. A vicious cycle. We break it by automating the path to production and adding a safe rollout with easy rollback, so the team can ship small changes frequently and safely, shrinking each release until deploying becomes routine rather than an event to brace for.
Adding observability after being burnt by a blind outage
A team that learns about outages from customers and then spends hours grepping logs across servers to work out what happened, because there is no real visibility into the running system. We instrument it (metrics, logs, traces and alerting tuned to matter), so the next incident announces itself on a dashboard, points at what changed, and is diagnosed in minutes rather than reconstructed after the fact.
Right-sizing an over-engineered platform back down
A small team that adopted full Kubernetes and a heavy GitOps stack because it was the done thing, and now spends more time operating the platform than shipping the product it was meant to serve. We assess honestly what the scale actually requires and simplify towards something the team can run, often keeping the containers and the pipeline while retiring the orchestration weight that was never earning its keep.
How we approach DevOps
We start by measuring, not building. Before touching a pipeline we establish where you actually are on the four DORA metrics: how often you deploy, how long a change takes to reach production, how often a deploy causes a problem, and how long it takes to recover when it does, because those numbers tell us which bottleneck is worth attacking first. There is no point automating deployments if the thing killing you is a two-day manual regression pass, and no point optimising build speed if every third release causes an incident. We fix the constraint that is actually hurting, in the order the metrics point to.
From there we automate the path to production incrementally, keeping you shipping the whole time. Each step: a pipeline that runs the tests, then one that builds a reproducible artefact, then one that deploys it, then a safe rollout with an easy rollback, is delivered as working machinery you use immediately, not a big-bang platform unveiled at the end. And we right-size deliberately: we reach for the simplest thing that solves your problem and resist the pull towards heavy tooling that would look good on a CV and be a burden on your on-call rota. The test for every piece is whether your team can operate it after we are gone.
How the engagement runs
We open by measuring where you are, because DevOps is one of the few disciplines with genuinely good metrics and it would be a waste not to use them. We establish your DORA baseline, how often you deploy, how long a change takes to reach production, how often a deploy causes a failure, and how long recovery takes, and we look at how you release today, warts and all: the manual steps, the tribal knowledge, the places people hold their breath. That baseline tells us which constraint is actually costing you, so we attack the thing that hurts rather than the thing that is fashionable to build.
Then we automate incrementally, keeping you shipping throughout. We build the pipeline in layers: tests running automatically, then a reproducible artefact, then automated deployment, then a safe rollout with rollback. Each delivered as machinery you use straight away, so value lands continuously rather than at the end of a long platform project. We wire in observability early, so you can see the system you are changing, and we right-size every decision to your team and scale. Finally we hand over properly: documented pipelines and infrastructure, the knowledge to operate and extend them, and the DORA numbers showing what moved, because the goal is a team that ships well without us, not a dependency on us to deploy.
The delivery pipeline, end to end
The organising idea is a single, automated path from a commit to running in production, with quality and safety built into the path rather than inspected in afterwards. A change is pushed and reviewed; the pipeline runs the tests and the security gates; if they pass, it builds one immutable, versioned artefact (typically a container image), and that exact artefact is what gets promoted through every environment. Building once and promoting the same thing is the crux: it means the version you tested in staging is byte-for-byte the version that reaches production, which retires the whole category of "it passed testing but broke in prod" failures that plague pipelines that rebuild at each stage.
Underneath the pipeline, the environments themselves are defined as code in Terraform, so they are reproducible and reviewable rather than hand-assembled and drifting. The applications run as containers, so the runtime is consistent from a developer’s laptop to production; where scale warrants it, an orchestrator schedules and heals those containers, and where it does not, something simpler runs them. Observability wraps the whole system: metrics, logs and traces flowing out, alerting watching for the symptoms that matter, so the pipeline that ships a change and the dashboards that watch its effect are two halves of the same feedback loop.
The release strategy is the last, crucial link, because the safety of the whole system comes down to how fast you can undo a bad change. A rolling, blue-green or canary deployment means a new version is introduced gradually or alongside the old one and watched against real metrics before it takes all the traffic, and can be pulled back in one step the moment it misbehaves. That easy rollback is what makes frequent deployment safe: when undoing a change is cheap and fast, shipping often stops being a gamble, and the fast feedback loop from commit to production to dashboard to rollback closes cleanly.
DevSecOps, shifting security left into the pipeline
Security handled as a gate at the end (a scan the week before release, a review nobody has time for), finds problems when they are most expensive to fix and slows everyone down doing it. DevSecOps shifts that work left, into the pipeline and into how software is built, so security checks run automatically on every change alongside the tests. Dependency scanning flags known-vulnerable libraries as they are introduced, rather than discovering them in a breach months later. Secret scanning stops API keys and passwords being committed in the first place, because a credential in git history is a credential leaked. Static analysis catches the common code-level flaws early. These are fast, deterministic checks that belong next to the tests, gating the same pull request.
Secrets management is its own discipline and we treat it as one: credentials never live in the repository or the pipeline definition, they are injected at run time from a proper secret store, scoped tightly to the environment that needs them, and rotated rather than shared forever. And because your software is built from a supply chain of dependencies and base images you did not write, we scan that chain (the container base images and the third-party packages), so a vulnerability someone else shipped does not become one you unknowingly deploy. What none of this replaces is genuine, human-led security testing: the authorised work of finding the flaws that chain together into a real compromise, which is a separate discipline covered by our cybersecurity service. The automated scans in the pipeline are the cheap, constant first line of defence, not the whole of it.
Signs it’s time
- Releases are slow and scary. A deploy is a scheduled, all-hands event people brace for, so you ship rarely, which makes each release bigger and riskier, which makes it scarier still
- Deployments are manual, someone follows a runbook, SSHes into a box, or runs a script only they understand, and the day they are on holiday nobody can safely ship
- Outages are frequent or slow to resolve, changes break production more often than anyone is comfortable with, and when they do, there is no fast way to roll back or even to tell what changed
- You are flying blind, when something goes wrong you find out from a customer, not a dashboard, and diagnosing it means grepping logs across servers because there is no real observability
How we think about DevOps
Our first principle is that DevOps is a way of working, not a tool you install, so we optimise for the outcome (faster, safer delivery), rather than for having built an impressive-looking stack. That means every piece of tooling has to justify itself against the constraint it removes, measured by the DORA metrics, and anything that does not earn its keep does not get built. It also means we treat the cultural half as real: automation only pays off when the team that ships also owns the running of what they shipped and gets fast feedback on it, so we build the pipelines and dashboards that make that ownership practical, not just the infrastructure.
Our second principle is right-sizing, and it is where operating what we build makes us more conservative than most. The instinct in DevOps is to reach for the powerful tool (full Kubernetes, GitOps, a service mesh), because it is what the big engineering blogs describe. But that machinery carries real operational weight, and imposing it on a team that cannot maintain it does not make them more sophisticated, it makes them fragile and slow. So we reach for the simplest thing that solves the actual problem, and we are willing to argue against complexity you are asking for if we think it will hurt you. The measure of a good DevOps setup is not how advanced it looks but whether your team can run it confidently once we have gone.
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 DevOps?
A short call with a senior engineer, before you write a brief. If DevOps is the wrong answer for your situation, we will say so and tell you what we think is right.
What changes
Releasing gets boring
Shipping goes from a scheduled, nerve-wracking event to a routine push, deploying more often, in smaller, safer increments, because the pipeline does the work and a bad change is easy to undo.
Faster recovery when it breaks
Real observability and a one-command rollback turn an incident from hours of archaeology into minutes. You see what broke, and you can pull the change back before most users notice.
Measured, not asserted
Your DORA metrics move in the right direction and you can see it (deployment frequency and lead time up, change-failure rate and recovery time down), so the improvement is a number, not a feeling.
Industries we serve
Domain knowledge changes what gets built. A few of the sectors we know before the first meeting.
How pricing works
- A fixed-scope build for a defined outcome (a CI/CD pipeline, infrastructure defined in Terraform, a safe release strategy), quoted once we have measured your DORA baseline and agreed which constraint we are removing first, so you are paying to fix the thing that actually hurts.
- A monthly senior engagement where the delivery platform is built out and matured alongside your product, with the continuity of the people who built it. The right shape when improving delivery is ongoing rather than a one-off deliverable, and when you want a hand operating it while your team gets comfortable.
- A focused audit and roadmap, measuring your DORA metrics, assessing how you release today, and returning a prioritised plan of what to fix in what order, priced by the assessment, and a sensible way to start if you are not yet sure where the biggest bottleneck is.
- A right-sizing engagement to simplify an over-built platform back down to something your team can actually operate, scoped once we have understood what your scale genuinely requires versus what was adopted out of fashion.
Typical timeline
- 01
Baseline and assessment
Around a week measuring your DORA metrics, mapping how you release today (the manual steps, the tribal knowledge, the places people brace), and identifying the constraint that is actually costing you the most, so the work targets the real bottleneck.
- 02
First automated pipeline in use
One to two weeks standing up a working CI/CD pipeline that tests, builds a reproducible artefact and deploys it, so the most painful manual part of releasing is automated and in daily use rather than sitting on a branch.
- 03
Infrastructure, observability and safe rollout
Defining your environments as Terraform, wiring in the metrics, logs, traces and alerting that make the system legible, and adding a blue-green or canary release with an easy rollback. The pieces that make frequent deployment safe as well as fast.
- 04
Right-sizing and handover
Tuning the setup to your team and scale, documenting the pipelines and infrastructure, and handing over the knowledge to operate and extend them, with the DORA numbers showing what moved, so your team ships well without depending on us.
What working with us actually means
We operate what we build
Our DevOps comes from running production systems and being on the pager when they break, not from theory. That experience shapes the pipelines and rollout strategies around what actually goes wrong at 3am, and it is why we build for easy rollback and real observability from the start, because we know from being woken up what it costs when they are missing.
We right-size and will argue against complexity
The common, expensive mistake in DevOps is adopting heavy tooling a team cannot maintain because it is fashionable. We reach for the simplest thing that removes your bottleneck, and we will talk you out of Kubernetes or GitOps when a lighter setup would serve you better. Machinery your team cannot operate is a liability, and we would rather leave you with boring and reliable.
We measure with DORA, not vibes
DevOps attracts more hand-waving than most disciplines, so we anchor to the four DORA metrics from a real baseline (deployment frequency, lead time, change-failure rate, time to restore), and show them moving. The improvement is a number you can see, not a feeling that things are better now that there is a pipeline.
Senior engineers, and you keep the keys
The people building your delivery platform are senior engineers who have built and run them before, not juniors following a tutorial. And we leave you self-sufficient: pipelines, infrastructure and observability your own team understands, documented and handed over, running in your accounts, not a bespoke platform that makes you dependent on us to ship.
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.
Related terms
Weighing the options
The decisions people are usually making at the same time as this one.
Common questions
Do we need Kubernetes?
Probably less often than you have been led to believe. Kubernetes is genuinely powerful for orchestrating containers at scale (self-healing, rolling updates, horizontal scaling), but it carries real operational weight, and running it well is close to a full-time discipline. For a small team or a modest number of services, it is usually over-engineering: you spend more time operating the platform than the product it serves. We will containerise you with Docker either way, because that value is nearly universal, but we reach for Kubernetes only when your scale and team genuinely justify it, and we will say so plainly when they do not. Our Docker and Kubernetes technology pages go deeper on where the line sits.
Isn’t deploying more often riskier?
It is the opposite, which is the counter-intuitive heart of DevOps. The risk in a release comes from how much changed and how hard it is to undo, not from how frequently you ship. A monthly release batches a large, tangled set of changes, so when something breaks it is hard to tell which of them did it. Deploy each change on its own, the moment it is ready, and a failure points at a small, recent, understandable diff, and with an easy rollback, undoing it is fast and cheap. Frequent, small deployments with safe rollback are how you get speed and safety together, rather than trading one for the other. The DORA research bears this out: teams that deploy more often also have lower change-failure rates.
What are DORA metrics and why do you keep mentioning them?
They are the four measures that research has found actually distinguish high-performing software teams, and they give DevOps something rare. An honest scoreboard. Deployment frequency: how often you ship. Lead time for changes: how long from a commit to it running in production. Change-failure rate: how often a deploy causes a problem. Time to restore service: how long recovery takes when it does. We keep mentioning them because DevOps attracts a lot of hand-waving, and these numbers let us target the constraint that is actually hurting you and then prove the improvement rather than assert it. We measure your baseline before we build, and we show you the numbers moving after.
We deploy manually and it mostly works, is this worth it for us?
It depends on what "mostly" is costing you, and we will give you a straight answer rather than an automatic yes. If a manual deploy runs fine every time, takes ten minutes, and anyone on the team can do it, you may not need much. But manual deployment usually hides real costs: it stops when the one person who knows it is away, it carries the risk of a missed step under pressure, and it makes releasing painful enough that you do it rarely, which makes each release bigger and riskier. If those are biting (if deploys are slow, scary, or bottlenecked on one person), automating the path to production pays for itself quickly. If they genuinely are not, we will tell you the money is better spent elsewhere.
Can you improve our delivery without ripping out what we already have?
Yes, and that is usually how it goes. We work with the tools you already run (your CI system, your cloud, your existing scripts), and improve incrementally rather than arriving with a platform to impose. We start by measuring where you are and finding the one constraint costing you the most, then fix that, keeping you shipping throughout. Often the highest-value change is small: automating one painful manual step, adding the observability you are missing, or putting a safe rollback where there is none. A big-bang platform rebuild is rarely the right answer, and it is where a lot of DevOps effort goes to die. We would rather make a series of concrete improvements you feel immediately.
Thinking about DevOps?
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 DevOps 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.