Service
Cloud Services & Migration
Cloud architecture, migration planning, deployment, optimization, and managed cloud operations.
Overview
Cloud adoption fails when it is treated as a lift-and-shift exercise without architecture discipline. Costs rise, security rules become inconsistent, and teams inherit the same operational problems they had before migration.
We plan migrations around workload priority, dependency mapping, security controls, networking, identity, backup, monitoring, and cost visibility. Each phase is tested before production cutover so business systems stay predictable.
A well-structured cloud environment gives your organization faster deployment, better recovery options, and clearer operational control. Teams gain flexibility without losing governance, security, or budget discipline.
We work with organizations that need cloud systems suitable for local teams, regional availability, compliance needs, and predictable operating costs.
Book a cloud readiness assessment. We will review workloads, define migration waves, identify cost and security risks, and produce a practical path to cloud adoption or optimization.
Why teams choose Turn Global
- Migration planning with dependency and risk mapping
- Secure cloud foundations and identity controls
- Cost visibility and optimization guidance
- Managed cloud support after launch
Capabilities
What this service covers
11 areas we deliver under Cloud Services & Migration. Open any of them for the full scope and deliverables.
AWS Cloud Migration
Plan and execute production-grade AWS migrations with secure landing zones and right-sized workloads.
Amazon Web Services gives you the broadest catalogue of managed compute, storage, and data services, but a rushed migration leaves you with sprawling bills and brittle architecture. We assess each workload, design a secure multi-account landing zone, and move applications in waves so your teams keep shipping throughout the project.
Discovery and migration strategy
We start by inventorying every server, database, and dependency, then map each workload to the right approach: rehost, replatform, or refactor. Application-discovery tooling surfaces traffic patterns and undocumented links so nothing is missed at cutover. The output is a wave plan that sequences low-risk workloads first, defines rollback steps for each move, and sets clear success criteria. You get a costed roadmap before a single resource is provisioned, so budget owners can approve with confidence.
Secure landing zone and account structure
Before workloads arrive we build a multi-account foundation using AWS Organizations, with separate accounts for production, staging, and shared services. Guardrails enforce encryption, logging, and tagging from day one, while IAM roles and permission boundaries replace long-lived keys. Centralised CloudTrail, GuardDuty, and Config give you an audit trail and continuous compliance checks. This structure keeps blast radius small, simplifies cost allocation per team, and means later workloads slot into a governed environment instead of an ungoverned sandbox.
Right-sizing compute, data, and storage
We match each workload to appropriate EC2 instance families, move relational databases to RDS or Aurora, and shift object data to S3 with lifecycle policies that tier cold data to cheaper storage. Auto Scaling groups and load balancers replace fixed capacity so you pay for demand, not peak guesses. Where it fits, we introduce managed services such as ElastiCache or SQS to remove undifferentiated maintenance. The result is a footprint sized to real usage rather than a like-for-like copy of old hardware.
Cutover, validation, and handover
Each migration wave runs through a rehearsed cutover with data sync, smoke tests, and a defined rollback window. We validate performance against baselines captured before the move and confirm monitoring, alerting, and backups are live before declaring a workload done. After go-live we document the architecture, hand over runbooks, and optionally train your engineers on day-two operations. You leave the engagement with a stable AWS estate your own team can confidently run.
Deliverables
- Workload discovery report with dependency mapping
- Wave-based migration plan with rollback procedures
- Multi-account landing zone with IAM and guardrails
- Migrated and right-sized EC2, RDS, and S3 workloads
- Monitoring, alerting, and backup configuration
- Architecture documentation and operational runbooks
Microsoft Azure Migration
Move Windows, SQL Server, and Microsoft 365 estates to Azure with identity-first governance.
If your organisation already runs Windows Server, SQL Server, and Microsoft 365, Azure offers the most natural path to the cloud with licence benefits that lower the bill. We use Azure Migrate to assess your estate, anchor everything to Entra ID for identity, and move servers and desktops without disrupting the tools your staff use every day.
Azure Migrate assessment and sizing
We deploy Azure Migrate appliances to discover servers, databases, and their dependencies, then collect performance data over time rather than guessing at capacity. The assessment recommends target VM sizes, flags workloads suited to Azure SQL Managed Instance, and estimates monthly cost including Azure Hybrid Benefit savings on existing Windows and SQL licences. You receive a readiness report that separates lift-and-shift candidates from those needing remediation, so the plan reflects your real estate rather than a generic template.
Identity-first foundation with Entra ID
Azure security begins with identity, so we establish Microsoft Entra ID as the control plane before workloads move. We configure conditional access, multi-factor authentication, and role-based access control, and where appropriate connect on-premise Active Directory through Entra Connect for a smooth hybrid state. Privileged Identity Management adds just-in-time elevation for admin roles. This identity foundation means access policies follow users consistently across migrated servers, SQL databases, and Microsoft 365, closing the gaps that ad-hoc permissions usually leave behind.
Servers, databases, and virtual desktops
We replicate Windows and Linux servers with Azure Site Recovery for a low-risk cutover, and migrate databases to Azure SQL or to VMs depending on compatibility. For distributed or remote teams, Azure Virtual Desktop delivers managed Windows desktops that scale with headcount and remove the cost of maintaining physical machines. Storage moves to managed disks and Azure Files with the redundancy tier each workload needs. Every resource lands inside governed resource groups with tagging and policy applied automatically.
Governance, monitoring, and cost management
Once workloads are live we enforce standards through Azure Policy, centralise logs and metrics in Azure Monitor, and surface security findings via Microsoft Defender for Cloud. Cost Management budgets and alerts give finance early warning before spend drifts. We document the landing structure and hand over dashboards your team can read at a glance. The outcome is an Azure environment that stays compliant and predictable rather than one that quietly accumulates orphaned resources and surprise charges.
Deliverables
- Azure Migrate assessment with sizing and cost estimate
- Entra ID identity and conditional access configuration
- Migrated servers, SQL databases, and storage
- Azure Virtual Desktop deployment where required
- Azure Policy governance and tagging standards
- Azure Monitor dashboards and cost budgets
Google Cloud (GCP) Migration
Migrate data, analytics, and containerised apps to Google Cloud with BigQuery and GKE.
Google Cloud is a strong choice when analytics, data warehousing, and containers sit at the centre of your roadmap. Its serverless data services and managed Kubernetes let small teams run ambitious platforms. We migrate your data and applications into a well-organised GCP project hierarchy, with BigQuery and GKE doing the heavy lifting where they earn their place.
Project hierarchy and resource organisation
We structure your environment using a clear folder and project hierarchy under a single organisation node, separating production, non-production, and shared services. IAM roles are granted at the right level so teams get access without over-permissioning, and organisation policies enforce constraints such as approved regions and disabled service-account key creation. Billing is split per project for transparent cost attribution. This foundation keeps a growing GCP estate tidy and auditable instead of becoming a flat collection of unrelated projects.
Data warehouse migration to BigQuery
For analytics-heavy workloads we migrate existing warehouses and reporting pipelines into BigQuery, where serverless scaling removes cluster management entirely. We model partitioned and clustered tables to keep query cost low, rebuild ingestion using Dataflow or scheduled transfers, and validate row counts and aggregates against the source. Existing dashboards are repointed and tested so analysts see consistent numbers on day one. The result is a warehouse that scales with query demand rather than fixed nodes you pay for around the clock.
Compute, containers, and GKE
Stateful and legacy workloads move to Compute Engine with right-sized machine types and committed-use discounts where usage is steady. Containerised applications run on Google Kubernetes Engine, using its autopilot and autoscaling capabilities so you provision capacity to actual load. We set up Artifact Registry for images, configure workload identity so pods authenticate without static keys, and wire deployments into CI. This gives you a modern, managed runtime for new services while older workloads migrate at a comfortable pace.
Networking, security, and observability
We design VPCs with private connectivity, Cloud NAT for controlled egress, and firewall rules scoped to least privilege. Sensitive data is protected with customer-managed encryption keys and, where needed, VPC Service Controls to limit data exfiltration paths. Cloud Logging and Cloud Monitoring provide unified observability, with alerting policies tied to the metrics that matter to your services. We hand over dashboards and documentation so your team understands the topology rather than inheriting an opaque environment.
Deliverables
- Organisation, folder, and project hierarchy with IAM
- BigQuery data warehouse with validated migration
- Compute Engine and GKE workload deployment
- VPC networking and least-privilege firewall design
- Cloud Monitoring dashboards and alerting policies
- Architecture documentation and cost attribution setup
Docker Containerization
Package applications into lean, reproducible Docker images with secure, multi-stage builds.
Containers solve the perennial problem of code that works on one machine but not another. By packaging an application with its exact dependencies, Docker makes deployments reproducible from a developer's laptop through to production. We containerise your applications with lean, secure images and a sensible local workflow, so every environment runs the same artefact.
Dockerising applications cleanly
We write Dockerfiles tailored to each application rather than reaching for bloated base images. Multi-stage builds compile and bundle in one stage, then copy only the runtime artefacts into a slim final image, cutting size and attack surface dramatically. Dependencies are pinned for reproducible builds, and layers are ordered so caching speeds up rebuilds. Non-root users and read-only filesystems are applied where the workload allows. The outcome is an image that starts fast, stays small, and behaves identically wherever it runs.
Local development with Docker Compose
Reproducibility matters most when developers can run the full stack locally. We define Docker Compose configurations that spin up your application alongside its databases, caches, and supporting services with a single command. Environment variables and volumes are structured so local data persists between runs without leaking into images. New engineers go from clone to running stack in minutes instead of fighting setup documentation. This local parity means bugs surface during development rather than after deployment to a shared environment.
Image security and registries
Containers are only as safe as their images, so we scan for known vulnerabilities in base layers and dependencies and rebuild on a regular cadence to pick up patches. Images are stored in a private registry with access controls, and tags follow a clear convention so you always know what is deployed. Secrets stay out of images entirely, injected at runtime instead. We document the build and publish process so your team produces consistent, auditable images long after the engagement ends.
Build pipelines and handover
We wire image builds into your CI so every merge produces a tagged, scanned image ready to deploy, removing manual build steps and the inconsistencies they cause. The pipeline pushes to your registry and can trigger downstream deployment to your hosting or orchestration platform. We provide documentation covering the Dockerfile design decisions, the local workflow, and the build pipeline, so your developers can extend the setup confidently rather than treating the containers as a black box.
Deliverables
- Optimised multi-stage Dockerfiles per application
- Docker Compose configuration for local development
- Private registry setup with access controls
- Image vulnerability scanning in the build pipeline
- CI-integrated build and publish workflow
- Documentation of image design and workflows
Kubernetes Orchestration
Run resilient, self-healing workloads on Kubernetes with autoscaling, Helm, and GitOps delivery.
When you are running many containers across changing demand, manual deployment stops scaling and Kubernetes earns its keep. It schedules workloads, restarts failed containers, and adjusts capacity automatically. We design clusters that are resilient without being over-engineered, and we package your applications so deployments become a repeatable, version-controlled process rather than a risky event.
Cluster architecture and node design
We design clusters around your workload profile, choosing managed control planes where possible to avoid operating Kubernetes internals yourself. Node pools are separated by workload type, with taints and tolerations keeping batch jobs off latency-sensitive nodes. Namespaces, resource quotas, and limit ranges stop one team or service from starving the cluster. Network policies enforce which pods may talk to each other. The result is a cluster with clear boundaries and predictable behaviour rather than a single shared pool where noisy neighbours cause unexplained slowdowns.
Deployments, Helm, and GitOps
We package applications as Helm charts so configuration is templated, versioned, and consistent across environments. Deployments roll out gradually with readiness and liveness probes, so traffic only reaches healthy pods and bad releases roll back automatically. With a GitOps workflow, the cluster's desired state lives in Git and a controller reconciles reality to match, giving you an auditable history of every change. Promoting a release becomes a reviewed pull request rather than an ad-hoc command run against production by hand.
Autoscaling, ingress, and traffic
Horizontal Pod Autoscaling adjusts replica counts to CPU, memory, or custom metrics, while the Cluster Autoscaler adds and removes nodes as pods demand, so you pay for capacity you actually use. An ingress controller terminates TLS and routes external traffic to services with sensible health checks and rate limits. For internal communication we configure service discovery and, where it adds value, a service mesh for retries and observability. Together these keep your platform responsive under load and economical when traffic subsides.
Observability, security, and operations
We instrument the cluster with metrics, logs, and traces so problems surface before users notice them, and define alerts tied to symptoms rather than noise. Role-based access control, pod security standards, and scanned images keep the cluster hardened, while secrets are managed through a proper store rather than plain manifests. We hand over runbooks for common operational tasks and, if you wish, train your team so day-two operations stay in-house rather than depending on us indefinitely.
Deliverables
- Cluster architecture with isolated node pools
- Helm charts for repeatable application deployment
- GitOps delivery pipeline with audit history
- Horizontal pod and cluster autoscaling configuration
- Ingress, TLS, and observability stack
- Security policies, RBAC, and operational runbooks
Cloud Disaster Recovery & Backup
Protect data and services with tested backups, defined RPO and RTO, and rehearsed failover.
Backups you have never restored are an assumption, not a safety net. Real resilience comes from knowing exactly how much data you could lose and how long recovery would take, then proving it with rehearsals. We design backup and disaster-recovery plans around your business priorities, set measurable targets, and test them so an outage becomes a managed event rather than a crisis.
Defining RPO, RTO, and priorities
We begin by working out, for each system, how much data loss you can tolerate (the recovery point objective) and how quickly it must be back (the recovery time objective). Not every workload deserves the same protection, so we tier services by business impact and design accordingly. This avoids overspending on hot standby for systems that can wait, while ensuring critical revenue paths recover fast. The result is a recovery strategy aligned to business reality rather than a blanket policy that is either too costly or too weak.
Automated, immutable backups
We implement automated backups across databases, virtual machines, and object storage, with retention schedules that satisfy operational and regulatory needs. Crucially, we keep copies in a separate account or region and use immutable, write-once storage so ransomware cannot encrypt or delete your recovery data. Encryption protects backups at rest and in transit. Backup success is monitored with alerting on failures, because a silently broken backup job is worse than none. You gain a recovery copy you can actually trust when it matters.
Failover architecture and rehearsals
Depending on your targets, we build the right recovery posture: pilot light, warm standby, or active-active across regions. Infrastructure is defined as code so a recovery environment can be rebuilt predictably rather than improvised under pressure. Most importantly, we run scheduled recovery drills, restoring data and failing services over to confirm the plan works and to measure actual recovery time against your objectives. Each drill produces findings that tighten the runbook, so confidence in recovery grows with evidence rather than hope.
Runbooks, monitoring, and handover
A recovery plan is only useful if people can execute it calmly during an incident. We produce clear runbooks covering who does what, in what order, with the exact commands and decision points. Monitoring alerts you to the conditions that trigger failover, and dashboards show backup health at a glance. We hand over the full plan, train your team on the drills, and schedule periodic re-tests, so resilience stays current as your systems evolve rather than decaying into a stale document.
Deliverables
- RPO and RTO targets defined per workload tier
- Automated, encrypted, immutable backup configuration
- Cross-region or cross-account recovery copies
- Infrastructure-as-code recovery environment
- Documented and rehearsed recovery runbooks
- Backup health monitoring and alerting
Cloud Cost Optimization
Cut cloud spend through rightsizing, commitment discounts, and a sustainable FinOps practice.
Cloud bills creep upward through oversized instances, forgotten resources, and on-demand pricing for steady workloads. The fix is rarely a single switch; it is disciplined analysis followed by ongoing governance. We audit your spend, capture the quick wins, then put a lightweight FinOps practice in place so costs stay accountable rather than drifting back up after the first clean-up.
Spend audit and waste identification
We start with a detailed analysis of your billing data, broken down by service, team, and environment, to see where money actually goes. The audit surfaces idle and orphaned resources, unattached storage volumes, oversized instances running at low utilisation, and old snapshots quietly accumulating charges. Untagged resources are flagged because you cannot govern what you cannot attribute. The output is a prioritised list of savings ranked by effort and impact, so you can act on the easy, high-value items immediately and plan the rest.
Rightsizing and architectural savings
Many savings come from matching resources to real demand. We rightsize compute based on observed utilisation, move suitable workloads to cheaper instance families or to serverless where it fits, and apply storage lifecycle policies that tier or expire data automatically. Auto-scaling and scheduled shutdowns stop non-production environments billing around the clock. Where architecture allows, we replace self-managed components with managed services that cost less to operate. These changes reduce the bill without compromising performance, because they remove waste rather than capacity you need.
Commitment-based discounts
For workloads with predictable baseline usage, on-demand pricing is the most expensive option. We analyse usage patterns and recommend the right mix of Reserved Instances, Savings Plans, or committed-use discounts, balancing commitment term against flexibility so you are not locked into capacity you might outgrow. We model the break-even and projected savings before you commit, and stage purchases as confidence in the baseline grows. This converts steady, unavoidable spend into substantially lower rates without betting on usage you cannot predict.
FinOps governance and accountability
One-off clean-ups erode unless cost becomes a shared habit. We establish a consistent tagging strategy so spend maps to teams and projects, set budgets with alerts that fire before overruns, and provide dashboards that make cost visible to engineers, not just finance. Lightweight review cadences keep optimisation continuous rather than reactive. The aim is a FinOps culture proportionate to your size, where teams see the cost of their choices and waste is caught early instead of discovered in next quarter's invoice.
Deliverables
- Detailed spend audit with prioritised savings
- Rightsizing recommendations and implementation
- Reserved Instance or Savings Plan strategy
- Idle resource cleanup and lifecycle policies
- Tagging strategy with cost allocation reporting
- Budgets, alerts, and FinOps dashboards
Cloud Security & Hardening
Harden cloud estates with least-privilege identity, encryption, and continuous compliance monitoring.
Most cloud breaches trace back not to exotic exploits but to misconfiguration: over-permissioned identities, public storage buckets, and unmonitored access. Hardening is about closing those gaps systematically and keeping them closed. We assess your environment against established benchmarks, remediate the real risks, and put continuous monitoring in place so drift is caught before it becomes an incident.
Posture assessment against benchmarks
We assess your environment against recognised standards such as the CIS Benchmarks and the cloud provider's well-architected security guidance, producing a clear picture of where you stand. The review covers identity, network exposure, encryption, logging, and storage configuration, and ranks findings by severity rather than dumping an undifferentiated checklist. Public-facing resources and over-broad permissions get particular scrutiny because they carry the most risk. You receive a remediation plan that distinguishes urgent fixes from longer-term improvements, so effort goes where it reduces risk fastest.
Identity, access, and least privilege
Identity is the new perimeter, so we tighten it first. We replace long-lived access keys with short-lived roles, enforce multi-factor authentication, and pare permissions down to least privilege using the provider's access analysers to find and remove unused grants. Privileged actions move behind just-in-time elevation and approval where appropriate. Service-to-service access uses scoped identities rather than shared credentials. The effect is that a compromised account or key grants an attacker far less, shrinking the blast radius of the most common attack path into cloud environments.
Network, encryption, and data protection
We segment networks so workloads are isolated by sensitivity, restrict ingress to only what is required, and route egress through controlled paths to limit data exfiltration. Encryption is enforced for data at rest and in transit, with keys managed in a dedicated key service and rotated on schedule. Storage buckets and databases are checked for public exposure and locked down. Security groups and firewall rules are scoped to least privilege rather than broad ranges. These controls protect data even if a single layer is bypassed.
Continuous monitoring and response
Hardening is not a one-time event, because environments drift as teams ship changes. We enable cloud-native security posture and threat-detection services so misconfigurations and suspicious activity raise alerts automatically. Centralised, tamper-resistant logging gives you the audit trail needed for investigation and compliance. We define alerting tied to genuine threats rather than noise, and provide an incident response runbook so your team knows how to react. Optionally we set periodic re-assessments to confirm the environment stays hardened as it grows.
Deliverables
- Security posture assessment against benchmarks
- Least-privilege IAM and MFA enforcement
- Network segmentation and firewall hardening
- Encryption and key management configuration
- Continuous posture and threat monitoring
- Incident response runbook and audit logging
Legacy System Cloud Modernization
Modernise ageing applications incrementally with the strangler-fig pattern and managed cloud services.
Ageing systems often still run the business, which makes them risky to touch and impossible to ignore. A full rewrite is tempting but frequently overruns and stalls. We favour incremental modernisation: stabilising what works, carving out new capability around the edges, and migrating to managed cloud services in safe steps, so value arrives continuously instead of after a multi-year gamble.
Assessment and modernisation roadmap
We begin by understanding the legacy system as it really is: its code, data model, integrations, and the business processes that depend on it. We identify which parts are stable, which are brittle, and where the highest business value sits. From this we build a roadmap that sequences modernisation by risk and return, rather than attempting everything at once. The plan makes explicit what stays, what is replaced, and what is retired, giving stakeholders a realistic path instead of an all-or-nothing rewrite proposal.
Incremental strangler-fig migration
Rather than replacing the system in one cutover, we apply the strangler-fig pattern: new functionality is built as separate cloud services, and a routing layer gradually redirects traffic from the old system to the new. The legacy application keeps running while it shrinks, slice by slice, until it can be safely retired. Each increment delivers working value and can be rolled back independently. This dramatically lowers the risk that defines big-bang rewrites, where months of work land at once with no proven path back.
Data, integration, and APIs
Legacy data is usually the hardest part, so we treat it carefully. We expose existing functionality behind clean APIs so new services can consume it without depending on internal legacy details, and we plan data migration in stages with reconciliation at each step. Where the old and new systems must coexist, we keep data consistent through synchronisation or an anti-corruption layer that shields modern code from legacy quirks. This lets the business keep operating while the underlying architecture is steadily replaced beneath it.
Cloud platform and operational uplift
As components move, we host them on managed cloud services that reduce operational burden, containerising where it helps and adopting managed databases, queues, and identity. Deployments shift from manual releases to automated pipelines, and observability is added so the new estate is far easier to run than the system it replaces. We document the evolving architecture and bring your team along, so modernisation leaves you with both better software and the capability to keep improving it.
Deliverables
- Legacy assessment and modernisation roadmap
- Strangler-fig migration with routing layer
- API layer wrapping legacy functionality
- Staged data migration with reconciliation
- Managed cloud hosting for new components
- Automated deployment pipelines and observability
Managed-to-Self-Managed Cloud Migration
Move from costly managed platforms to a self-managed cloud setup your own team can run.
Managed platforms and fully outsourced hosting are convenient at first, but their premiums and constraints often outgrow their value as you scale. Moving to a self-managed cloud estate can cut costs and remove lock-in, provided the migration is paired with automation and genuine knowledge transfer. We make that transition deliberate, so you gain control without inheriting fragility.
Evaluating the move and its trade-offs
Self-managing is not automatically cheaper once you account for the engineering time it demands, so we start by being honest about the trade-off. We compare your current managed costs and constraints against the realistic operational effort of running the equivalent yourself, factoring in your team's capacity and skills. The assessment identifies which components are worth self-managing and which are better left on managed services. The outcome is a clear-eyed decision and a migration scope based on total cost of ownership, not just the headline platform fee.
Rebuilding on infrastructure as code
A self-managed estate only stays manageable if it is reproducible, so we rebuild your infrastructure as code rather than clicking it together by hand. Terraform or equivalent tooling defines every resource, so environments are consistent, reviewable, and rebuildable. Configuration management standardises servers, and secrets are handled through a proper store. This replaces the opaque convenience of a managed platform with transparent, version-controlled infrastructure your team can understand, change safely, and recreate, which is the foundation that makes self-management sustainable rather than a maintenance trap.
Operations, monitoring, and automation
Leaving a managed platform means taking on the operational duties it handled for you, so we build those in deliberately. Automated deployment pipelines remove manual release steps, monitoring and alerting give you the visibility the platform previously provided, and backups plus patching are scheduled rather than assumed. We automate the routine toil that would otherwise consume your engineers, so self-management does not become a constant firefight. The goal is an estate that runs predictably with modest day-to-day effort, not one that demands heroics to keep alive.
Knowledge transfer and handover
The biggest risk in going self-managed is that the capability lives only with whoever built it. We counter that with deliberate knowledge transfer: documented architecture, runbooks for common tasks, and hands-on sessions where your engineers operate the system with us alongside them. We taper our involvement as your team's confidence grows, so by the end you are genuinely self-sufficient. You finish with both a cost-effective estate and the in-house expertise to run and evolve it without ongoing dependence on us.
Deliverables
- Total-cost-of-ownership comparison and recommendation
- Infrastructure rebuilt as version-controlled code
- Automated deployment and configuration pipelines
- Monitoring, alerting, and backup setup
- Operational runbooks and architecture documentation
- Hands-on knowledge transfer to your team
Lift-and-Shift Cloud Migration
Move existing servers to the cloud quickly with rehost migration and minimal application change.
Sometimes the priority is simply to get out of a data centre before a lease ends or hardware fails, and a deep refactor would take too long. Lift-and-shift rehosts your servers to the cloud largely as they are, delivering speed and a clean exit. We do it methodically so the move is fast without being reckless, and we leave you positioned to optimise afterwards.
When lift-and-shift is the right call
Rehosting suits situations where time matters more than redesign: a looming data-centre exit, ageing hardware, or a need to prove cloud viability quickly. It moves applications with minimal change, so it carries less project risk than refactoring and delivers results sooner. We are candid about its trade-off, namely that you do not immediately gain cloud-native efficiencies, and we identify which workloads are genuinely good rehost candidates versus those better replatformed. This keeps the engagement focused on what lift-and-shift does well rather than overpromising transformation.
Discovery and migration planning
Even a straightforward rehost fails without knowing what depends on what, so we inventory servers, applications, and their interconnections, capturing performance baselines to size targets correctly. Dependencies are grouped into migration waves so tightly coupled systems move together and avoid mid-migration breakage. We define cutover windows, rollback plans, and validation tests for each wave. The result is an ordered plan that turns a daunting data-centre exit into a sequence of manageable, low-surprise moves with clear success criteria for every step.
Automated replication and cutover
We use block-level replication tooling to copy servers continuously to the cloud while the originals keep running, so the actual cutover is short. Each wave is tested in the cloud target before going live, confirming the application behaves and connectivity is correct. At cutover we sync the final changes, switch traffic, and run smoke tests, with a defined rollback if validation fails. This approach keeps downtime to a brief, planned window rather than an extended outage, which matters when the systems being moved still serve the business daily.
Post-migration validation and next steps
Once workloads are running in the cloud, we validate performance against the baselines taken beforehand, confirm monitoring and backups are in place, and decommission source infrastructure only after a stabilisation period. Crucially, we hand over a prioritised list of post-migration optimisations: rightsizing, managed-service adoption, and resilience improvements you can pursue now that the urgent move is done. Lift-and-shift gets you to the cloud quickly, and this roadmap ensures it becomes a foundation to build on rather than a like-for-like copy frozen in place.
Deliverables
- Workload discovery with dependency grouping
- Wave-based rehost plan with rollback procedures
- Automated server replication to the cloud
- Tested cutovers with minimal downtime
- Post-migration performance validation
- Optimisation roadmap for after the move
FAQ
Common questions
Can you migrate systems without major downtime?
Yes. We use phased migration, testing, rollback planning, and cutover windows designed around business impact.
Do you manage cloud environments after migration?
Yes. We can provide ongoing monitoring, optimization, maintenance, and support for cloud environments.
Will our applications stay online during the migration?
Yes. We migrate in waves with data synchronisation and rehearsed cutovers, scheduling each move into a maintenance window with a tested rollback. Most workloads experience only a brief, planned switchover rather than extended downtime.
Can we keep using Microsoft 365 and on-premise AD together?
Yes. We support hybrid identity through Entra Connect, so on-premise Active Directory and Microsoft 365 stay in sync. Many clients run hybrid for an extended period, migrating workloads gradually while users keep a single sign-in experience.
Why choose Google Cloud over other providers?
GCP is particularly strong for data analytics and containers. If BigQuery, serverless data pipelines, or managed Kubernetes are central to your plans, it often delivers more capability per engineer-hour. We advise honestly when another provider would suit you better.
Can you containerise an older application?
Usually, yes. We assess the application's dependencies and runtime, then build a Dockerfile that captures them faithfully. Even legacy stacks benefit from reproducible images, though we flag any components that need adjustment to run cleanly inside a container.
Do we actually need Kubernetes for our workload?
Not always. Kubernetes pays off when you run many services, need autoscaling, or require self-healing across nodes. For a handful of containers, simpler hosting is often cheaper to run. We assess honestly and recommend it only when the complexity is justified.
What is the difference between backup and disaster recovery?
Backup is keeping recoverable copies of your data; disaster recovery is the broader plan to restore whole services after an outage, including infrastructure and failover. You need both. Backups protect against data loss, while disaster recovery restores the systems that use that data.
How much can we realistically save?
It varies with how the environment was built, but unoptimised estates commonly hold meaningful waste in idle resources and on-demand pricing. We quantify the achievable saving in the audit before any commitment, so the figure is grounded in your actual usage rather than a generic promise.
Isn't the cloud provider responsible for security?
Only partly. Under the shared responsibility model, the provider secures the underlying infrastructure, but configuration, identity, data, and access are yours. The majority of breaches stem from customer-side misconfiguration, which is exactly what our hardening work addresses.
Why not just rewrite the whole system at once?
Full rewrites carry high risk: they often overrun, and value only arrives at the very end if at all. Incremental modernisation delivers working improvements continuously and keeps the existing system running, so the business is never left waiting on an unfinished replacement.
Will self-managing actually save us money?
Often, but not always. The platform premium disappears, yet you take on operational effort. We model total cost of ownership, including engineering time, so the decision is based on your full picture rather than the headline saving alone.
Isn't lift-and-shift just copying our problems to the cloud?
It can be, if you stop there. We treat rehosting as the first step, getting you to the cloud quickly, then hand over a clear optimisation roadmap. The speed and lower risk are real benefits when timelines are tight, provided you plan to improve afterwards.
Ready to start?
Tell us about your cloud services & migration needs and we will reply with a practical implementation plan.