A practical GCP migration plan delivers six things: a workload inventory with dependency maps, a landing zone built before any workload moves, a wave-based schedule broken into sprints, a runbook for every workload, a validation and rollback plan for each cutover, and executive sponsorship that holds through delays. We recommend starting with low-dependency, standalone applications as your pilot wave, using Migration Center for discovery and cost estimates, and treating large estates as a multi-year programme, not a weekend cutover.
TL;DR:
- Building a comprehensive workload inventory with dependency maps and risk scores is crucial to avoid delays during migration planning.
- Starting with low-dependency, standalone applications as pilot waves simplifies validation and builds team confidence before tackling complex workloads.
- Automating discovery, validation, and rollback procedures reduces errors and facilitates repeatable execution across multiple migration waves.
- Designing the landing zone, including resource hierarchy, identity controls, and networking, before migration ensures governance and security baseline consistency.
- Post-migration, active monitoring, right-sizing, and security validation are essential to optimize performance and maintain compliance over time.
Table of Contents
- Discovery and assessment: build the inventory and dependency map
- Plan your migration waves, move groups and sprints
- Execute waves: tools, runbooks, automation and rollback
- Design the landing zone and enterprise foundation before you move workloads
- Migration tooling and cost estimation
- Validation, testing and rollback: how to prove a migration is safe
- Why Nexus is a practical partner for enterprise GCP migrations
- Migration types: choosing between the R's for each workload
- Post-migration optimization and performance monitoring
- Security considerations and compliance during and after migration
- Change management and communication strategies for stakeholders
- Backup and disaster recovery planning
- Risk assessment and mitigation strategies
- Training and skill development for teams involved in migration
- Author perspective: what separates migrations that hold up from ones that don't
- How Nexus can help you plan and run your migration
- FAQ
- Sources
Discovery and assessment: build the inventory and dependency map
You cannot plan a wave you cannot see. The first job in any enterprise migration is building a complete, verified inventory of what you run today: servers, databases, network dependencies, licensing terms and the people who own each system. Skipping this step is the single most common reason migrations stall: teams discover a dependency six weeks into execution that should have surfaced in week one.
Automated tools do the heavy lifting here. Migration Center ingests infrastructure data and produces asset inventories, utilization baselines and cost estimates in one workflow, and it accepts imports from RVTools exports for VMware environments. Agent-based scanners fill in process-level and network dependency detail that static exports miss. None of this replaces a human conversation with application owners, though: automated tools see traffic and resource consumption, not business context, so manual verification against owner knowledge catches the dependencies that only exist on a whiteboard somewhere.
Your assessment phase should produce a defined set of artifacts, not just a spreadsheet of server names.
- A workload catalogue listing every application, its technology stack, owner and business criticality.
- Dependency maps showing which systems talk to which, including hidden integrations like scheduled jobs and shared file mounts.
- A resource consumption baseline (CPU, memory, storage, network throughput) to support right-sizing decisions later.
- An owner RACI so every workload has a named decision-maker during planning and cutover.
- A complexity and risk score per workload, factoring in technical debt, data gravity, SLA commitments and regulatory scope.
That scoring step is what turns a pile of discovery data into a usable plan. A workload with no external dependencies, modest data volume and no regulatory constraints is a pilot candidate. A workload tied to a core banking ledger with strict uptime requirements and compliance obligations belongs in a later wave, with more testing time built in. The assessment output feeds directly into wave planning: you cannot group workloads into sensible move groups until you know which ones are tightly coupled and which stand alone.
Plan your migration waves, move groups and sprints
Wave planning turns your assessment into a schedule you can actually execute. A wave is a logical grouping of workloads, usually defined by business capability, shared dependencies or similar risk profile, moved together over a defined period. Each wave then breaks down into move groups (the specific set of resources migrating together) and sprints (the execution windows, typically one to four weeks, where a move group actually cuts over).
Here is a practical sequence for structuring this:
- Group workloads by dependency clusters first; anything that shares a database connection or a synchronous API call generally belongs in the same wave.
- Layer in risk profile: separate workloads with hard compliance or SLA requirements from general-purpose applications, even if they share dependencies.
- Convert each wave into sprints, with a defined runbook, checklist and named team roster per sprint.
- Schedule your pilot wave around standalone, low-dependency applications, a pattern Google's own migration planning guidance recommends specifically because it builds process confidence before anyone touches a tightly coupled legacy system.
- Scale sprint cadence once the pilot wave proves out your runbooks, tooling and team coordination.
Prioritization within this sequence follows a few consistent rules. Low-dependency, high-value workloads move first because they deliver visible wins with the least coordination overhead. Regulatory-driven workloads sometimes need to move early regardless of complexity, when a compliance deadline or contract renewal forces the timing. Cost-benefit analysis decides the rest: a workload costing a fortune to keep on aging hardware gets priority over one that is cheap to leave in place a little longer.
For a large estate, expect this to run three to five years rather than months. Google's guidance on enterprise migration waves notes that later waves typically require more time because they involve application refactoring or re-architecture rather than straightforward lift-and-shift. A realistic multi-year plan might target a smaller percentage of the estate migrated in year one while pilot waves and foundation work are underway, then accelerate as patterns repeat and teams gain confidence. Build that ramp into your timeline from the start rather than promising a flat migration rate that front-loads unrealistic expectations on your pilot wave.
Sprint runbooks matter as much as the wave structure itself. Each sprint needs its own checklist, a named team roster (infrastructure, application owner, database administrator, security reviewer), and a clear go/no-go gate before cutover. A wave plan without sprint-level detail is just a list of intentions; the sprint runbook is what actually gets executed on a Tuesday night by an on-call engineer.
Execute waves: tools, runbooks, automation and rollback
Execution is where plans meet production, and it rewards repeatability over heroics. The teams that run dozens of successful migrations build what is often called a migration factory: an integrated set of tooling spanning discovery, cost estimation, infrastructure-as-code and CI/CD, so that each new workload migration reuses the same pipeline rather than reinventing the process. Google Cloud's migration execution guidance recommends exactly this separation of discovery, planning, execution and validation tooling, because it lets you run repeatable sprints at scale instead of treating each migration as a bespoke project.
A per-workload runbook is the operational backbone of each cutover. At minimum it should include:
- Pre-cutover checks confirming backups are current, dependencies are mapped and stakeholders have signed off.
- Step-by-step cutover instructions, written specifically enough that someone other than the architect who wrote them could execute it.
- Validation tests to run immediately post-cutover, covering functional correctness and performance against baseline.
- A documented rollback procedure for every step, not just a generic "revert if something breaks" note.
- An audit trail capturing who executed what, when, and what the validation results showed.
Automation earns its keep here. Templated infrastructure-as-code, standardized migration scripts and a build pipeline that promotes the same configuration through test and production environments cut down the chance of a one-off manual error during cutover. Best-practice guidance on validating a migration specifically calls out failure-mode analysis and a documented rollback strategy per step as core to reducing risk, not optional extras bolted on after the fact.
Run cadence matters more than most teams expect going in. A daily execution standup keeps the sprint team aligned on blockers, but it should stay short and tactical: genuine troubleshooting belongs in a separate session so the daily standup does not turn into an hour-long debugging call. Weekly retrospectives after each sprint catch process gaps early, before they repeat across five more sprints.
Pro Tip: Run your rollback procedure once in a non-production environment before the actual cutover window; a rollback plan that has never been tested is a hope, not a plan.
A practical cloud migration checklist that maps each of these runbook steps to a reusable template can save your team from rebuilding this structure from scratch for every wave.
Design the landing zone and enterprise foundation before you move workloads
Nothing should migrate into an environment that has not been deliberately designed. A landing zone, the foundational set of projects, identity controls, networking and governance policies, needs to exist before your pilot wave moves a single workload. Google's foundation guidance is direct about this: building the foundation first prevents cloud sprawl and establishes governance that makes later waves repeatable instead of chaotic.
Four areas need deliberate design work up front.
- Resource hierarchy: structure your organization, folders and projects to reflect business units or environments, with billing separation clean enough that finance can track spend without reverse-engineering it later.
- Identity and access: configure Cloud Identity, scope IAM roles tightly and use service accounts with least-privilege permissions rather than broad project-level access granted out of convenience.
- Networking and connectivity: decide on Cloud Interconnect or VPN for hybrid connectivity, confirm MTU settings match across your hybrid links, and plan DNS resolution between on-premises and cloud environments before workloads depend on it.
- Governance guardrails: establish naming and labelling conventions, organizational policies and cost controls early, because retrofitting them across a hundred already-migrated projects is far more painful than enforcing them from project zero.
A landing zone built with infrastructure-as-code and policy-as-code from the outset pays off well past the migration itself: new projects inherit the same guardrails automatically, and your security team has one consistent posture to audit rather than a patchwork of manually configured environments. Background on infrastructure patterns that informed this foundation work is worth a look if your team is newer to infrastructure-as-code practices generally.
Migration tooling and cost estimation
Three tools do most of the heavy lifting for planning and validating a GCP migration, and each answers a different question. Migration Center handles discovery, asset inventory and rapid cost estimation in one platform, and it can be activated at no cost for assessment work once you select a region for Migration Center data and enable the required APIs, according to Google's setup documentation.
The Quick TCO Estimator inside Migration Center answers the cost question specifically. Feed it an RVTools export or manual server specifications, and it returns a 1-year and 5-year total cost of ownership comparison against your current infrastructure, along with right-sizing recommendations that flag over-provisioned VMs before you pay to migrate waste.
Database Migration Service handles the database layer, supporting MySQL, PostgreSQL, SQL Server and Oracle through guided conversion workspaces. The pricing split matters for budgeting: homogenous migrations (same engine, moving into Cloud SQL or AlloyDB) carry no additional service charge, while heterogeneous migrations, where the source and destination engines differ, use Perugia pricing with tiered rates and some free backfill allowance.
| Tool | Primary use | Key output |
|---|---|---|
| Migration Center | Discovery, inventory, assessment | Asset catalogue and cost estimate |
| Quick TCO Estimator | Cost modelling for Compute Engine moves | 1-year and 5-year TCO, right-sizing suggestions |
| Database Migration Service | Database engine migration | Homogenous (no added charge) vs heterogeneous (Perugia) migration |
Beyond the raw tooling output, your cost model needs to account for committed use discounts, any enterprise agreement pricing your organization has negotiated, and partner funding programs, since multi-year cost guidance recommends building these into your projections alongside expected completion percentages per year, rather than treating the estimator's baseline number as the final budget line.
Validation, testing and rollback: how to prove a migration is safe
A migration is not done when the cutover completes; it is done when you have evidence it works at least as well as what it replaced. Google's validation best practices frame this around a few concrete steps worth following in order:
- Run a complete pre-cutover validation checklist covering smoke tests, functional tests against known use cases, and a performance baseline comparison against the source environment.
- Stage the rollout using canary releases, blue/green deployment, or a dark-launch pattern where new infrastructure runs in parallel before taking live traffic.
- Document a rollback plan for every migration step individually, not a single rollback plan for the whole cutover, and automate the rollback trigger where the risk and volume justify it.
- Define explicit safe-retire criteria for the source environment: a minimum observation period, confirmed backup validity and sign-off from the application owner before decommissioning anything.
- Keep post-cutover monitoring and observability active well past the first day, since performance regressions under real production load sometimes surface only after a few days of genuine traffic.
A proof of concept earlier in the process, run against a representative but non-critical workload, surfaces most of the surprises a full validation checklist would otherwise catch for the first time during an actual cutover window. It is cheaper to learn your DNS cutover plan has a flaw during a POC than during wave three of a live migration.
Why Nexus is a practical partner for enterprise GCP migrations
We consolidate migration planning, cybersecurity and compliance under one engagement, which removes the coordination gap that opens up when a migration vendor, a security team and a compliance auditor are all working from different assumptions. Our real-time threat detection carries directly into migration work: when a workload moves, our incident response and remediation stay integrated rather than waiting on a handoff to a separate security vendor.
For teams building out runbooks and sprint checklists, our Eight Step Cloud Migration Checklist maps closely to the wave and sprint structure described above. We offer both assessment engagements and fully managed execution, depending on how much of the migration your internal team wants to run directly.
Migration types: choosing between the R's for each workload
Not every workload deserves the same treatment, and picking the wrong migration type is a common source of wasted effort. The classic framework breaks into six approaches. Rehost (lift-and-shift) moves a workload to Compute Engine with minimal change, fastest to execute but it carries forward existing technical debt. Replatform makes targeted changes, such as moving a database to Cloud SQL or AlloyDB without rewriting the application, to capture some cloud-native benefit without a full rebuild. Refactor restructures application code to take fuller advantage of cloud services. Re-architect goes further still, redesigning the application around cloud-native patterns like managed services and serverless compute. Replace swaps the workload for a SaaS equivalent rather than migrating it at all. Retire simply decommissions workloads nobody actually needs anymore, which discovery frequently reveals once dependency maps make unused systems visible.
Most enterprise estates end up using several of these approaches in parallel. Pilot waves favour rehost and replatform because they are fast and low-risk, building team confidence before tackling the workloads that justify a full re-architecture. Mapping each workload to the right R during the assessment phase, rather than defaulting to rehost for everything, is what keeps later waves from inheriting the same problems the migration was meant to solve.
Post-migration optimization and performance monitoring
Moving a workload to GCP is the midpoint of the work, not the finish line. Once a workload lands, right-sizing becomes an ongoing exercise rather than a one-time decision made during planning: actual usage patterns in production often differ from what discovery data predicted, and Compute Engine instances provisioned conservatively during migration frequently have room to scale down.
Performance monitoring needs a baseline captured at cutover and a regular comparison cadence afterward, watching for latency regressions, cost drift and resource utilization trends that only show up under sustained production load. Operational cadence practices that apply broadly to IT operations are worth adapting specifically for the weeks immediately following a cutover, when attention naturally drops off just as subtle issues start to surface. Teams that treat the first 30 to 60 days post-migration as an active optimization window, rather than declaring victory at cutover, consistently catch cost and performance issues earlier.
Security considerations and compliance during and after migration
Security work during a migration splits into two distinct phases: protecting the migration itself, and establishing the security posture the workload will live under afterward. During migration, data in transit between on-premises and GCP needs encryption, and access to migration tooling needs the same least-privilege discipline as production systems, since migration windows are a common target for credential misuse precisely because attention is focused elsewhere.
Post-migration, compliance obligations do not pause for the move. A workload subject to SOC 2, HIPAA, PCI-DSS or ISO 27001 requirements needs its compliance posture re-validated in the new environment, not assumed to carry over automatically because the application code is unchanged. Guidance on turning Security Command Center findings into actionable SOC workflows is directly relevant here: once workloads move, security monitoring needs to be live from day one, not bolted on after the fact once something goes wrong.
Change management and communication strategies for stakeholders
A technically sound migration plan can still fail if the people affected by it are surprised. Application owners need advance notice of their workload's wave assignment, a clear escalation path during cutover, and a say in the go/no-go decision, since they often know operational quirks that discovery tooling missed. Executive stakeholders need a different cadence: a regular, concise status update tied to wave completion and budget tracking, not a blow-by-blow of every sprint.
Communication failures during migrations tend to follow a pattern: teams over-communicate technical detail to executives and under-communicate timing to the business units actually affected. A simple fix is a standing communication plan baked into the wave schedule itself, with defined checkpoints: wave kickoff, pilot results, go/no-go for each subsequent wave, and a post-migration summary. Treating this as part of the plan, rather than an afterthought handled informally, keeps surprises to a minimum on both sides.
Backup and disaster recovery planning
Every migration needs a backup strategy that holds at both ends: a verified, restorable backup of the source system before cutover, and a tested disaster recovery plan for the workload once it runs on GCP. Skipping backup verification before migration is a common and entirely avoidable failure mode: teams assume existing backups work until a rollback scenario proves otherwise at the worst possible moment.
Disaster recovery planning on GCP should define recovery time and recovery point objectives per workload during the assessment phase, since a workload with strict uptime requirements needs a different DR architecture than one tolerant of a few hours of downtime. Building this into the landing zone design, rather than retrofitting it after migration, means DR capability exists from the moment a workload lands rather than being added as a separate project later.
Risk assessment and mitigation strategies
Risk in a migration programme concentrates in a few predictable places: workloads with poorly understood dependencies, tight regulatory deadlines colliding with technical complexity, and single points of failure in the migration team itself. Scoring each workload for technical complexity, data gravity and regulatory exposure during assessment, as covered earlier, is the primary mitigation tool: it tells you where to add testing time and senior oversight before a sprint starts rather than after it stalls.
Mitigation works best as a standing practice rather than a one-time risk register. A failure-mode analysis for each wave, reviewed before sprint planning locks in, catches the scenarios a generic checklist misses. Per-step rollback plans, covered in the validation section above, are themselves a risk mitigation tool: the existence of a tested rollback changes the risk calculus for an entire sprint, because a failed cutover becomes a recoverable delay instead of an outage.
Training and skill development for teams involved in migration
A migration plan assumes a team capable of executing it, and that assumption often does not hold on day one. GCP-specific skills, from IAM configuration to Compute Engine right-sizing to Database Migration Service conversion workflows, take deliberate investment to build if your team has primarily operated on-premises or on a different cloud provider.
Pairing internal engineers with pilot wave work is one of the more effective ways to build this capability: the pilot wave's lower stakes make it a reasonable training ground before the same engineers run a sprint on a regulated, high-dependency workload. Formal training alongside hands-on pilot experience, rather than either alone, tends to close the skills gap faster, and documenting lessons learned from each sprint retrospective turns that knowledge into something the whole team can draw on for later waves rather than keeping it with whoever happened to run the first cutover.
Author perspective: what separates migrations that hold up from ones that don't
The migrations that stall are rarely the ones with the hardest technical problems. They are the ones without a named executive sponsor who will defend the timeline when a business unit pushes back, and without KPIs specific enough to show whether a wave actually succeeded. We have also seen speed treated as the only metric that matters, which quietly accumulates technical debt that someone else inherits two waves later. Staged modernization, with lessons from each sprint retrospective genuinely feeding the next one, beats a fast migration that just relocates the same problems to a new address.
— Nick - Sr. Executive
How Nexus can help you plan and run your migration
We offer services that consolidate migration scoping, landing zone design, wave execution and ongoing managed operations under one engagement, reducing the need to coordinate between different vendors for migration, security, and compliance. Our team follows a consistent runbook and governance model from assessment through post-migration monitoring, with pricing communicated up front.
- Request a migration assessment to get a workload inventory and risk-scored wave plan.
- Have our team build and validate your landing zone before your pilot wave moves.
- Get ongoing managed operations once your workloads are live on GCP.
Explore our full cloud solutions and managed IT services to see how an assessment engagement fits your migration timeline, or download our Eight Step Cloud Migration Checklist to see the runbook structure we use on our own engagements.
If your migration includes CRM or line-of-business application data, our partner's guide on planning a CRM data migration is a useful companion for mapping that specific data layer. And where a migration touches identity providers or regulated data flows, this identity verification upgrade checklist from our compliance partner is worth a look before cutover.
FAQ
What are the seven steps of a cloud migration model?
A common model covers assess, plan, migrate, validate, optimize, and recurring steps for governance and training, though the exact numbering varies by framework. In practice, enterprise GCP migrations compress these into discovery and assessment, landing zone design, wave planning, execution, validation and rollback, and post-migration optimization.
What are the five phases of cloud migration?
Most frameworks converge on discovery and assessment, planning, migration execution, validation and testing, and optimization as the five core phases. Google's own guidance frames this as an iterative cycle of discovery, planning, execution and optimization that repeats across successive migration waves rather than a single linear pass.
Is OCI better than GCP for enterprise migrations?
Oracle Cloud Infrastructure and Google Cloud serve different strengths: OCI is often chosen for Oracle database and applications workloads specifically, while GCP's tooling for discovery, cost estimation and managed database migration is built around broad enterprise estates across multiple engines. The right choice depends on your existing stack, workload mix and which provider's native tooling best matches your migration's complexity.
Does Nexus offer managed GCP migration services?
Yes, we offer migration scoping, landing zone design, execution and ongoing managed operations as part of our cloud solutions, with transparent pricing agreed during the assessment stage. Pricing is quoted per engagement since migration scope varies significantly between organizations.