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Cloud Engineering Services

Cloud Infrastructure That Fits How Your Applications Run

Design cloud environments around how your applications handle traffic, scale and recover, with the architecture, infrastructure and controls needed for dependable production workloads.

Discuss your cloud environment

Start with the workload

Where Is Your Cloud Straining?

Pick the situation closest to yours. We’ll show what we would build for it and where that sits in the cloud engineering capabilities below.

Pick where the cloud is straining

Where it strains

Growth Is Outrunning the Architecture

Traffic, workloads or data volumes have grown past what the environment was designed for, and every increase now costs more than the last one did.

What we would build
What you could end up with
  • An architecture sized to how the application actually behaves
  • High availability and recovery designed in, not added afterwards
  • Cost and performance decided together instead of traded blindly
What it works with
  • How the application handles traffic today
  • What has to stay available, and for how long
  • The recovery you are already expected to meet
Best fitCloud Architecture & Infrastructure
Connected engineering
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Where it strains

The Platform Choice Is Still Open

AWS, Azure or Google Cloud — and how much control to keep — is still being argued, and the systems you already run have a say in the answer.

What we would build
What you could end up with
  • A platform decision made on workloads and existing systems, not on preference
  • Landing zones with accounts, networking and guardrails settled once
  • A hybrid or multi-cloud boundary that is deliberate rather than inherited
What it works with
  • The systems that cannot move, and why
  • Your existing licensing and vendor commitments
  • The level of control this application actually requires
Best fitCloud Platforms & Environment Design
Connected engineering
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Where it strains

Every Workload Is Heading for One Runtime

Kubernetes is being chosen by default, including for work that needs no orchestration at all, and for work that runs a few times a day.

What we would build
What you could end up with
  • A runtime chosen per workload rather than per platform
  • Container orchestration where isolation and scale earn it
  • Serverless where the work is event-driven and intermittent
What it works with
  • What each service actually does, and how often it runs
  • Who will operate the cluster once it is live
  • The parts already containerized, and what that cost
Best fitContainers, Kubernetes & Serverless Architecture
Connected engineering
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Where it strains

Environments Have Drifted Apart

Production, staging and test were each set up by hand at different times, and rebuilding any of them depends on somebody remembering what was done.

What we would build
What you could end up with
  • Environments defined in code, so one can be rebuilt from nothing
  • Provisioning that is repeatable instead of remembered
  • Identity and access designed with the architecture, not bolted on after
What it works with
  • How environments get created today, and by whom
  • Who holds which access, and why they still have it
  • The controls you are audited against
Best fitInfrastructure as Code & Cloud Security
Connected engineering
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What we build

Cloud Engineering Capabilities

We do not start with a cloud product or architecture pattern. We start with how the application behaves, what needs to stay available and how your team will operate it.

01

Cloud Architecture & Infrastructure

Plan the cloud foundation, infrastructure model and application architecture around workload, scale and availability needs.

Cloud ArchitectureCloud InfrastructureCloud-Native Application ArchitectureNetwork ArchitectureHigh AvailabilityResilience & RecoveryCost & Performance

02

Cloud Platforms & Environment Design

Design cloud environments across AWS, Azure, and Google Cloud based on workload needs, existing systems and the level of control the application requires.

AWSMicrosoft AzureGoogle CloudCloud Landing ZonesHybrid CloudMulti-Cloud

03

Containers, Kubernetes & Serverless Architecture

Choose and structure the right runtime model for each workload, whether it needs container orchestration, independent scaling, or event-driven execution.

ContainersDockerKubernetesContainer OrchestrationServerless ArchitectureCloud Runtime Design

04

Infrastructure as Code & Cloud Security

Make environments repeatable and controlled through automated infrastructure setup and security-aware architecture.

Infrastructure as CodeTerraformAutomated ProvisioningCloud Security ArchitectureIdentity & Access Controls

Cloud engineering for AI

AI Changes What the Cloud Has to Handle

AI applications put a different kind of pressure on cloud infrastructure. Compute demand can change quickly, access to enterprise data needs tighter control, and poor scaling choices can push costs up fast.

01

Compute Where It Matters

Match CPU, GPU and accelerated compute to the workload instead of overprovisioning everything.

ExampleUse GPU resources only for AI inference while the rest of the application runs on standard cloud compute.

02

Scale Each Workload Independently

Let AI services, APIs and the rest of the application use resources based on their own demand instead of scaling everything together.

ExampleScale an AI processing service during peak usage without increasing resources for the entire application.

03

Keep Data Access Controlled

Design identity, networking and permissions so AI services can reach approved data without opening unnecessary access.

ExampleGive an internal AI assistant access to selected enterprise services while keeping sensitive systems isolated.

04

See Where AI Spend Is Going

Separate AI-related compute and infrastructure usage so teams can see what is driving cloud spend and adjust resources as demand changes.

ExampleTrack AI inference and GPU usage separately from the rest of the application to spot expensive workloads early.

Explore AI & Machine Learning

Industry context

Cloud Engineering Across Industries

Different industries place different demands on availability, security, scale and infrastructure control. We shape the cloud environment around those operating realities.

01 / 09

FinTech

01High-Availability Architecture
02Secure Cloud Infrastructure
03Autoscaling
04Identity & Access Controls
05Cloud Cost Visibility

How we work

How a Cloud Environment Is Built

We start from how the application behaves under load rather than from a platform, then build the environment so it can be rebuilt, changed and paid for predictably.

01

Profile the Workload

Start with how the application behaves: what it does under load, what has to stay available, what recovery it owes, and how the team that will operate it works today.

Focus
BehaviourLoadAvailabilityOperations
02

Design the Environment

Choose the platform, the network shape and the runtime for each workload, and settle landing zones, identity and access boundaries before anything is provisioned.

Focus
PlatformNetworkRuntimeAccess
03

Provision From Code

Build through infrastructure as code so every environment comes from the same definition, with security controls part of the provisioning rather than a later pass over it.

Focus
IaCProvisioningControlsEnvironments
04

Operate and Right-Size

Run it with the availability and recovery it was designed for, and keep compute, storage and scaling matched to real demand instead of to a peak somebody once guessed at.

Focus
AvailabilityRecoveryScalingCost
Profile01 Profile the WorkloadDesign02 Design the EnvironmentProvision03 Provision From CodeOperate04 Operate and Right-Size

Insights

Thinking Behind Cloud Decisions.

Field notes on cloud architecture, migration and the infrastructure choices that decide what a production environment costs to run.

On-Premise to AWS Migration guide for UK businesses planning cloud transformation
Cloud & Platform Engineering

A Strategic Guide to On-Premise to AWS Migration for UK Businesses

· Akash Thakor · 6 min read

Multi-Geography AML SaaS Architecture for Regulatory Compliance
Software Engineering

How We Architect Multi-Geography SaaS Platforms for AML Compliance

· Kartik Gajjar · 6 min read

Modern Data Stack 2026 architecture unifying cloud data for scalable SaaS applications
Data & Analytics

Building the Modern Data Stack in 2026: A CTO’s Guide to Unifying Cloud Data for Scalable SaaS Products

· Charmi Shah · 5 min read

All Cloud & Platform Engineering writing

FAQ

Frequently Asked Questions About Cloud Engineering

Straight answers on what cloud engineering and cloud development services cover, cloud-native versus cloud-hosted, when AWS development services fit better than Azure, and how to control cloud cost without weakening reliability.

01What do Cloud Engineering Services include?

Cloud Engineering Services can include cloud architecture, infrastructure design, AWS, Azure and Google Cloud environments, containers, Kubernetes, serverless architecture, Infrastructure as Code, cloud security and hybrid cloud. The exact scope depends on how the application runs, what it needs to scale, and how the environment is operated.

02What is the difference between cloud-native and cloud-hosted applications?

A cloud-hosted application mainly runs on cloud infrastructure, often with limited architectural change. A cloud-native application is designed to use cloud capabilities such as containers, managed services, serverless functions, and independent scaling. The right approach depends on the application, operating model, and how much change is justified.

03AWS vs Azure: Which is better for enterprise workloads?

Neither AWS nor Azure is automatically the better choice. The decision depends on your existing technology stack, workload requirements, security model, internal skills and cloud strategy. Azure often fits naturally into Microsoft-heavy environments, while AWS may align better with other architecture and service requirements.

04When should we use Kubernetes or serverless?

Kubernetes works well when applications need container orchestration, workload isolation and more control over how services scale and run. Serverless can be a better fit for event-driven or intermittent workloads where managing infrastructure adds little value. Some cloud environments use both for different parts of the same application.

05How do you control cloud costs without affecting reliability?

Cloud cost control starts with understanding how workloads actually use compute, storage and network resources. Right-sizing, autoscaling, workload scheduling, visibility into usage and choosing the right cloud services can reduce waste without weakening reliability. The goal is to make infrastructure spending reflect real application demand.

06How is Cloud Engineering different from Platform Engineering?

Cloud Engineering focuses on the architecture and infrastructure applications run on, including cloud environments, compute, networking, containers and security. Platform Engineering focuses more on the shared tools and self-service experience development teams use to work with that infrastructure. The two often connect, but they solve different engineering problems.

Is Your Cloud Environment Getting Harder to Manage?

Whether the issue is architecture, workload growth, infrastructure complexity, or rising cloud costs, we can help you work out what needs attention first and what is actually worth changing.

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