Cloud and infrastructure services cover the design, migration, management, and optimisation of the cloud environments where your software, data, and AI systems live. DevByte builds secure, scalable cloud environments for healthcare, agritech, and finance organisations — with HIPAA-aligned infrastructure, cost-optimised architectures, and the DevOps practices that keep systems reliable in production.
Cloud and infrastructure services span cloud strategy and architecture design, cloud migration (moving from on-premise or legacy environments to modern cloud platforms), CloudOps and DevOps (the ongoing operational practices that keep cloud environments healthy), and cloud cost management (FinOps practices that prevent cloud spend from growing unchecked as infrastructure scales).
For organisations building AI-powered products, cloud infrastructure is not a background concern — it is a core dependency. Large language models, data pipelines, ML training workloads, and real-time inference endpoints all have specific infrastructure requirements: GPU availability for training, low-latency networking for real-time AI responses, scalable storage for large datasets, and compliance-grade security controls for sensitive data. An AI system that is architecturally sound but deployed on inadequate infrastructure will underperform or fail in ways that are difficult to diagnose.
For healthcare organisations in particular, the infrastructure layer also carries compliance obligations. HIPAA requires that patient data is encrypted at rest and in transit, that access is logged and role-controlled, and that any cloud service provider processing PHI signs a Business Associate Agreement. These requirements do not change the choice of cloud platform — AWS, Azure, and GCP all offer HIPAA-eligible services — but they do require deliberate configuration that not every cloud team applies by default.
The most common pattern we see when inheriting an existing infrastructure: it was designed for a specific workload at a specific scale, and it was never revised as the organisation’s needs changed. A system that handles 1,000 API calls a day was not designed for 100,000. A database architecture that was sensible for a single application is not sensible for five. The performance and reliability problems appear gradually, are often misattributed to application bugs, and compound until a production incident makes the root cause obvious.
Cloud cost is a related problem. Cloud environments that are not actively managed tend to grow in cost faster than they grow in value. Unused resources accumulate, over-provisioned instances run at 10% utilisation, and development environments that were stood up for a specific project are never decommissioned. FinOps practices — rightsizing, reserved instance planning, cost anomaly detection — are not optional for organisations running meaningful cloud workloads.
A well-architected cloud environment is defined by five properties: operational excellence (automated deployments, monitored systems, documented runbooks), security (least-privilege access, encrypted data, network segmentation, audit logging), reliability (redundancy, automatic failover, capacity buffers), performance efficiency (right-sized resources matched to workload requirements), and cost optimisation (active management of spend against usage). These are not aspirational — they are the outcomes of specific architectural and operational decisions.
Infrastructure as Code (IaC) using Terraform or AWS CloudFormation is the foundation of operational excellence in cloud environments. When infrastructure is defined in code, it is version-controlled, reviewable, reproducible, and deployable without manual steps. A cloud environment that was built by clicking through consoles cannot be reliably replicated, audited, or recovered from a failure. An environment defined in code can be.
For AI workloads specifically, the infrastructure has additional requirements. ML training runs need GPU access and scalable compute that can be provisioned on demand and released when training is complete. Real-time inference endpoints need low-latency, high-availability deployment with auto-scaling. Data pipelines need reliable, monitored execution with alerting when upstream data quality changes. We design for these requirements as part of the initial architecture, not as additions after the AI system is built.
It depends on your workloads, existing technology investments, and compliance requirements. For healthcare organisations, all three offer HIPAA-eligible services. AWS has the broadest service catalogue and the largest ecosystem. Azure is the natural choice for organisations already using Microsoft products. GCP is strongest for organisations running large-scale data and ML workloads. We recommend after understanding your specific situation.
A focused migration for a single application typically takes 4 to 10 weeks. A full infrastructure migration for a complex, multi-application environment takes 3 to 9 months depending on the number of systems involved and the complexity of the current environment.
Yes. A cloud cost audit typically takes 1 to 2 weeks and identifies specific, actionable cost reduction opportunities. We typically find 20–40% cost reduction potential in unmanaged cloud environments.
HIPAA compliance in cloud environments requires specific configurations: encryption at rest and in transit, role-based access controls, VPC network isolation, CloudTrail or equivalent audit logging, and BAAs with all service providers processing PHI. We document and maintain all of these as part of our managed infrastructure service.
Yes, for clients on our managed cloud services plan. Monitoring runs continuously with automated alerting for infrastructure anomalies, and our team responds to incidents within defined SLA windows depending on severity.