Your on-prem servers are aging infrastructure you are paying to maintain, secure, and replace on a cycle you cannot escape.

Lift-and-shift (also called rehosting) moves your existing application to cloud infrastructure without changing the application itself. Your application runs on cloud VMs instead of physical servers. It gets the operational benefits of cloud -- no hardware to manage, easier backup, faster provisioning -- without the full cost and disruption of rebuilding the application. Lift-and-shift is faster, lower risk, and lower cost than a full re-architecture. It is the right approach for applications that are stable, not cloud-optimised, and where the operational benefits of cloud are the primary goal. Cloud-native migration (re-platforming or re-architecting) redesigns the application to use managed cloud services: containerisation with Kubernetes, serverless functions for appropriate workloads, managed databases instead of self-managed database servers, and auto-scaling infrastructure. It costs more and takes longer than lift-and-shift but delivers better ongoing scalability, resilience, and operational efficiency. For most migrations, we recommend a phased approach: lift-and-shift first to get off on-prem, then re-platform specific components where the cost-benefit of redesigning is clear.

Data migration is the highest-risk part of any cloud migration. Our approach has four phases. Assessment: we document every database, its size, schema, relationships, and data quality issues before touching anything. Planning: we design the migration strategy for each database -- which managed service it moves to, the migration method (dump and restore, CDC replication, or native migration tooling), and the cutover plan. Validation: we run the migration in a staging environment and run automated validation checks that compare row counts, checksums, and data samples between source and destination. Cutover: the production cutover uses a defined runbook with rollback steps if any validation check fails at any point. Data migration validation is not a manual spot-check. It is automated comparison of source and destination at the record level. We do not declare migration complete until validation passes.

AWS is the most mature platform with the broadest service selection and the largest ecosystem of third-party tools and integration partners. It is the default choice for organisations without a strong existing relationship with Microsoft or Google. Azure is the natural fit for organisations deep in the Microsoft ecosystem: Windows Server, Active Directory, MSSQL, Office 365. The integration between Azure and Microsoft's enterprise tools is tighter than what AWS or GCP offers, and licensing benefits for existing Microsoft customers are meaningful. GCP has the strongest managed data and analytics services (BigQuery, Dataflow, Vertex AI) and is worth considering for organisations where data processing and AI are central workloads. We assess your existing infrastructure, team expertise, existing licensing agreements, and primary use cases before recommending a platform. The recommendation is based on what fits your situation, not our familiarity.

For most migrations, taking systems offline for the full migration duration is not acceptable. We use phased migration and cutover strategies that minimise downtime. For applications, we run source and destination in parallel during a validation period and switch traffic when confidence is high, then decommission the source. For databases, we use replication: the destination database receives an ongoing stream of changes from the source until the cutover moment, at which point the replication lag is typically seconds. The cutover window -- when write traffic switches from source to destination -- is planned for the lowest-traffic period and is measured in minutes, not hours. The specific cutover strategy depends on your application architecture, acceptable downtime window, and business criticality. We design and document the cutover plan before migration starts and dry-run it in a staging environment.