On-premises infrastructure has a fixed cost regardless of whether you use it: hardware refresh every 3-5 years, facilities costs, backup infrastructure, and the IT overhead of keeping it running. When a server fails at 2am, someone gets called. When you need to scale for a peak period, you are limited by what is in the rack. We migrate businesses to cloud infrastructure on AWS, Azure, or GCP. From assessment and planning through application migration, data migration, and post-migration optimisation. The transition from infrastructure you manage to infrastructure that manages itself.
Recent outcomes
Voice AI · Research
Text-based interviews converted to automated phone calls
6× deeper insightsAI Automation · Ops
Manual invoice OCR across 40+ gas stations
20k+ txns day oneLoyalty · Retail
SuperValu & Centra loyalty platform with receipt validation
1,062 users in 4 weeksSaaS · Logistics
Multi-carrier shipping hub for Indonesian eCommerce
2,000+ shipments yr 1RaftLabs provides cloud migration services -- cloud readiness assessments, lift-and-shift migrations, application re-architecting for cloud-native deployment, database migrations to managed services, infrastructure-as-code with Terraform, and post-migration cost optimisation. Cloud costs typically drop 20-40% below equivalent on-premises spend within 12 months. Every engagement is scoped at a fixed price after an assessment maps your infrastructure, application dependencies, and target environment on AWS, Azure, or GCP.
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On-premises infrastructure has a fixed cost profile: capital expenditure on hardware, facilities (power, cooling, physical security), IT time for maintenance and upgrades, and the operational burden of running your own infrastructure. This cost is constant whether you are at 20% utilisation or 100%.
Cloud infrastructure has a variable cost profile: you pay for what you use. Scaling up for demand peaks does not require a hardware purchase. Scaling down after a migration does not leave you with idle capacity. Backup, disaster recovery, and geographic redundancy are built-in services rather than separate infrastructure projects.
The migration is a project. The operational savings are permanent.
Capabilities
A structured assessment of your current infrastructure, applications, and data before migration starts. We map every workload against the 6R migration strategies -- rehost (lift-and-shift to cloud VMs), replatform (move to a managed service with minor changes), refactor (re-architect for cloud-native deployment), repurchase (replace with SaaS), retire (decommission unused systems), or retain (keep on-premises where cloud provides no benefit) -- and assign each workload to the strategy that balances migration cost against long-term operational benefit.
Application dependency mapping documents what connects to what so migration sequencing follows dependency order rather than convenience, preventing the scenario where you migrate an application before the database it depends on. AWS Migration Hub, Azure Migrate, or Google Transfer Service provides automated discovery of running workloads, network flows, and resource utilisation data as the baseline for the dependency map. Database sizing and migration complexity assessment covers schema size, transaction throughput, replication requirements, and any Oracle-to-PostgreSQL or MSSQL-to-Cloud-SQL schema compatibility issues that add scope.
TCO calculator methodology compares current on-premises fully-loaded cost (hardware amortisation, power, cooling, facilities, IT labour, software licensing) against projected cloud spend at equivalent utilisation plus reserved instance discounts. The WAF (Well-Architected Framework) review scores your target architecture against the five pillars -- operational excellence, security, reliability, performance efficiency, and cost optimisation -- and identifies gaps in the landing zone design before migration begins. The assessment produces a migration roadmap with phasing, estimated effort, and risk areas identified before you commit to the migration project. No surprises mid-migration.
Application migration from on-premises servers to cloud infrastructure. Lift-and-shift migration for stable applications moving as-is to cloud VMs. Containerisation of applications for deployment on Kubernetes for teams ready to move to a more operationally efficient runtime. Configuration management using Ansible or similar so application configuration is code, not manual server setup. Load balancer and auto-scaling configuration for applications that need elastic capacity. SSL certificate migration and DNS cutover. Post-migration smoke testing against a defined test plan before the old environment is decommissioned.
Relational database migration from on-premises servers to managed cloud database services: RDS, Cloud SQL, Azure Database, or self-managed cloud databases. PostgreSQL, MySQL, MSSQL, Oracle, and MongoDB supported. Schema migration with compatibility validation. Data migration using replication for continuous sync during the migration window. Cutover using a planned low-traffic window with replication lag minimised before switchover. Full data validation comparing source and destination post-migration. Managed database configuration for automated backups, point-in-time recovery, and read replicas.
Cloud infrastructure defined and deployed using Terraform. Every resource -- compute, networking, storage, security groups, IAM, DNS -- defined in version-controlled code rather than created manually through the console. Infrastructure that is reproducible, auditable, and diffable. Environments (development, staging, production) created from the same codebase with environment-specific configuration. State managed in remote backend (S3 with DynamoDB locking or Terraform Cloud). Delivery means your team inherits infrastructure they can modify, extend, and destroy-and-recreate rather than a black box of manually configured resources.
Cloud network architecture designed with security and least-privilege access as the starting point. Landing zone design establishes the account structure, network topology, and guardrails before any workloads are migrated -- fixing a poorly designed landing zone after production workloads are running is expensive and disruptive.
VPC design separates public subnets (load balancers, NAT gateways) from private subnets (application servers, databases), with security group rules permitting only the specific port and source combinations required by each service. Network ACLs add a stateless second layer of subnet-level filtering. IAM role design follows least privilege: each service or application component gets a role with only the permissions it needs to operate, never a wildcard policy. Secrets management uses AWS Secrets Manager, Azure Key Vault, or Google Secret Manager with automatic rotation for database credentials and API keys, replacing hardcoded credentials in application configuration that are a frequent source of breaches. For hybrid architectures, AWS Direct Connect, Azure ExpressRoute, or Google Cloud Interconnect provides dedicated private connectivity between cloud and remaining on-premises systems -- avoiding the public internet for traffic that should stay internal and delivering predictable latency that VPN over the public internet cannot guarantee. RTO and RPO targets for disaster recovery are defined during the assessment and implemented: multi-AZ deployment for sub-minute RPO, cross-region replication for DR targets with RPO measured in minutes, and documented runbooks for recovery procedures. Logging, audit trails, and alerting (CloudTrail, Azure Monitor, GCP Audit Logs) are configured from day one so your security posture is visible from the first workload deployed, not retrofitted after an incident.
Cloud cost management from the first day of migration rather than after the bill arrives. Right-sizing compute resources based on actual utilisation, not peak theoretical capacity. Reserved instance and savings plan purchasing recommendations for stable workloads. Auto-scaling configuration for variable workloads so you do not pay for idle capacity. S3 storage lifecycle policies. CloudWatch, Azure Monitor, or GCP Monitoring configured with cost anomaly alerts. Monthly cost review during the first three months post-migration to identify optimisation opportunities as real usage patterns emerge. Cloud cost typically drops 20-40% below equivalent on-prem spend for the same workload within 12 months of optimisation.
Bring us your current infrastructure details and operational constraints. We will assess the migration scope and give you an honest cost and timeline estimate.
DevOps as a Service -- CI/CD, containerisation, and infrastructure automation post-migration
Data Engineering -- data pipelines that live in the cloud environment you migrate to
Legacy Modernisation -- re-architecting legacy applications alongside or after migration
Software Modernisation -- modernising the applications you migrate to the cloud
Enterprise Software Development -- building new applications for your cloud environment
Frequently asked questions
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.
Work with us
We scope Cloud Migration Services in 30 minutes. You walk away with a clear cost, timeline, and approach. No commitment required.