Cloud Infrastructure
Setup

Cloud infrastructure management services cover the full spectrum of cloud operations: consulting on cloud strategy and architecture, designing and implementing cloud networks and security controls, provisioning and managing compute and storage infrastructure, handling cloud migration from on-premise or legacy environments, and establishing operational practices around infrastructure monitoring, alerting, and cost optimisation. This includes managing public cloud infrastructure across AWS, Microsoft Azure, and Google Cloud Platform, as well as hybrid cloud and multi-cloud scenarios where workloads span multiple providers or environments.

We provide cloud infrastructure design and development, establish monitoring and maintenance practices, implement disaster recovery and business continuity planning, handle cloud migration services from existing systems, set up automation and CI/CD pipelines for infrastructure deployment, manage infrastructure orchestration and service discovery, implement security and authentication controls, design for high availability with failover and redundancy, support hybrid cloud and edge computing scenarios, and select and configure cloud infrastructure managed services appropriate to your workload. Every service is built with the operational reality of your team in mind.

The value comes in four dimensions: scalability without operational complexity, flexibility to adapt architecture as requirements change, cost optimisation through right-sizing and managed services, and security built into the architecture from the start. Cloud infrastructure also enables deployment automation and faster deployment velocity, eliminates manual infrastructure management overhead, provides visibility through infrastructure monitoring, establishes clear identity and access management, simplifies migration to cloud from legacy systems, improves business continuity through redundancy and automated backup, and delivers container orchestration for teams ready to adopt it. The biggest value is often operational efficiency — your team spends time building features, not managing infrastructure.

Start with cloud architecture design and optimisation grounded in your operational reality, not theoretical best practices. Implement DevOps consulting and practices that make sense for your team, not just for the sake of it. Use autoscaling and load balancing to handle variable load without manual intervention. Establish cost monitoring and spend optimisation as ongoing practices, not one-time exercises. Implement continuous integration and continuous delivery for infrastructure changes, treating infrastructure as code rather than undocumented manual configurations. Plan for disaster recovery and business continuity explicitly, not as an afterthought. For organisations running multi-cloud or hybrid cloud, manage workload placement decisions consciously rather than letting them happen by accident. Build in cloud security hardening and compliance controls from the start, not as a bolt-on.

Multi-cloud and hybrid cloud scenarios — where workloads span AWS, Azure, Google Cloud Platform, or on-premise infrastructure — require explicit decisions about workload placement and inter-cloud connectivity. Direct interconnections with Microsoft Azure and Google Cloud, or Oracle Exadata Cloud@Customer for enterprise workloads, enable secure, low-latency communication between environments. Cost efficiency in multi-cloud requires visibility into spending across providers and conscious decisions about where workloads actually belong. The complexity of multi-cloud grows quickly, so successful organisations start with clear policies about which workloads go where and why, rather than letting workload placement happen organically.

Infrastructure-as-code tools like AWS CloudFormation, Azure Resource Manager, and Google Cloud Deployment Manager automate infrastructure provisioning and make changes reproducible and versionable. Monitoring platforms like Datadog, Splunk, and AWS CloudWatch provide visibility into system health, performance, and cost. Identity and access management tools enforce role-based access control and prevent unauthorised access. Resource allocation strategies become visible and manageable when infrastructure is code. Without these tools, infrastructure management becomes manual, undocumented, and error-prone.

Security starts with identity and access management that controls who can access what, enforced through role-based access control rather than broad admin access. Encryption at rest and in transit protects data from unauthorised access. Threat detection and continuous security audits catch issues before they become breaches. Compliance audits ensure infrastructure meets regulatory requirements in your industry. Infrastructure-as-code enables security audits by making infrastructure visible and reviewable. Role-based access control prevents the "one person knows the password" problem. Secrets management keeps credentials out of version control and reduces the blast radius of credential leakage. Data loss prevention is built through automated backup strategies and cross-region replication. DevSecOps practices integrate security into the infrastructure deployment pipeline rather than treating it as a separate concern. The result is infrastructure that's secure by design, not secured after the fact.

There's no universal right answer — it depends on your specific workloads, team skills, existing investments, and operational constraints. AWS is the market leader with the broadest service portfolio and largest ecosystem. Microsoft Azure integrates naturally if your organisation already runs Windows, Active Directory, or Office 365. Google Cloud competes on data analytics and machine learning capabilities. All three are production-grade. Choose based on where your team has existing expertise, which provider's managed services best match your workload characteristics, pricing for your specific workload profile, and which provider's tools and APIs align with how your team wants to work. A thorough cost analysis for your specific workloads often reveals significant differences.

Cost varies based on workload characteristics, required availability and redundancy, whether you're using managed services or building custom infrastructure, and the cloud provider. A small application on a single server might cost £50–200 per month. Production infrastructure with redundancy, backup, and monitoring across multiple regions could be £1,000–5,000+ per month depending on compute and storage consumption. Managed services typically cost more in monthly fees but eliminate operational overhead and the cost of hiring specialists to manage infrastructure. The best approach is to understand your workload profile (CPU, memory, storage, data transfer), then get pricing quotes from your chosen provider.

Planned downtime is often easier to execute than a zero-downtime migration, but zero-downtime migration is possible with careful planning. Blue-green deployments, where you run old and new infrastructure in parallel and switch traffic over, eliminate user-facing downtime. Data migration requires planning to ensure consistency — you can't just copy a database while it's in use. The safest approach is usually a planned downtime window measured in hours, not days. Some workloads can migrate with no downtime using replication and careful orchestration, but it requires more planning and testing. Discuss with your migration partner what's realistic for your specific systems.

Start with identity and access management — control who can access your infrastructure with role-based access control, not broad admin access. Implement encryption at rest for data storage and in transit for network communication. Deploy firewalls and network segmentation so not everything can talk to everything. Set up continuous monitoring and alerting for security events. Establish incident response procedures so your team knows what to do if something goes wrong. Run a security audit before going live — an external review from someone who doesn't know your architecture often surfaces issues that teams miss. After launch, threat detection and regular security audits should be ongoing practices.

Start with the metrics that matter: system availability and uptime, response latency for user-facing systems, error rates, resource utilisation (CPU, memory, disk), and cost metrics that show where money is being spent. Establish monitoring dashboards so your team can see system health at a glance. Configure alerting for problems that actually need human attention, not for every metric fluctuation — noisy alerting leads to alert fatigue and missed real issues. Document runbooks that tell your team exactly what to do when an alert fires. Test alerts and runbooks regularly so people know how to respond when a real incident happens.

Managed services shift operational complexity to the cloud provider — they handle patching, backup, scaling, and availability. The trade-off is reduced control and sometimes higher cost. Build custom infrastructure only when managed services can't meet your requirements or when the cost savings justify the operational burden. For most organisations, the answer is "use managed services wherever possible" because the operational overhead of managing custom infrastructure often outweighs the flexibility. Database replication, load balancing, and auto-scaling can all be handled by managed services so your team doesn't have to.

Document your recovery procedures in detail — who does what, in what order, with contact information for key people. Test recovery regularly (at least quarterly) in a controlled environment so you know procedures actually work. Automated backups with cross-region replication mean recovery is faster than manual backups. Recovery time objective (RTO — how fast you need to be back up) and recovery point objective (RPO — how much data loss you can tolerate) should be explicit, not assumed. The goal is that disaster recovery isn't a theoretical plan nobody has tested — it's a practiced procedure your team can execute.

Cost optimisation is ongoing, not one-time. Tag all resources with cost centre and application information so you know where money is actually spent. Implement budget alerts so you're notified when spending exceeds projections. Review cloud bills monthly, looking for unused resources, overprovisioned compute, or opportunities to use cheaper service tiers. Implement scheduled shutdown for development and test environments that don't run 24/7. Use spot instances or reserved capacity for baseline load to get volume discounts. Regularly audit and right-size compute resources based on actual utilisation. Some organisations save 20–40% through disciplined cost optimisation.