Cloud Migration Checklist: Plan, Move and Optimize

Why a checklist matters

Cloud migrations rarely fail because of the cloud itself. They fail because of something nobody listed: a hard-coded IP address, a nightly batch job that runs on a forgotten server, a licence tied to physical hardware, or a database that was much larger than anyone thought.

A checklist forces those details into the open before they become outages. Use the lists below as a starting point and adapt them to your own systems, whether you are moving to AWS, Azure or Google Cloud.

Before you start, agree on why you are migrating. Common reasons include ending a data centre lease, scaling for growth, improving resilience, or reducing the time spent maintaining hardware. The reason shapes every later decision, from which workloads go first to how much refactoring is worth doing.

1. Assess what you have

You cannot plan a move without an accurate inventory. Spend real time here, because every surprise found later costs more to fix.

Then group the inventory into migration waves. Start with a low-risk workload that still exercises your network, security and deployment setup, so the team learns on something that will not hurt the business if it takes longer. Save the most critical and most tangled systems for later waves.

• List every application, server, database, storage volume and scheduled job, with an owner for each.
• Map dependencies: which systems call which, including external APIs and file transfers.
• Record current performance and usage: CPU, memory, storage growth and peak traffic times.
• Check software licences for cloud restrictions.
• Identify compliance needs, such as data that must stay in India or specific audit requirements.
• Note what is already broken or fragile, so you do not blame the migration for existing problems.

2. Choose a strategy per workload

Not every workload should be moved the same way. Rehosting, often called lift and shift, moves servers as they are and is the fastest route. Replatforming makes small changes, such as moving a self-managed database to a managed service. Refactoring rebuilds parts of the application to use cloud-native services like containers or serverless functions.

A common pattern is to rehost first to exit a data centre on schedule, then replatform and refactor the workloads where the payoff is clear. Some applications are better retired or replaced with a SaaS product. Decide this per workload and write the reason down.

Also decide early how you will deploy applications in the cloud. Defining infrastructure with Terraform or the provider's own templates from the first workload makes environments repeatable, simplifies audits and avoids a pile of hand-configured resources that nobody can recreate.

3. Security, access and compliance

Set up the foundation before moving any workload. Retrofitting security after go-live is slower and riskier.

Network design belongs here too. Plan private subnets for databases and internal services, limit what is exposed to the internet, and decide how your office or existing data centre connects to the cloud, whether through a VPN or a dedicated link.

• Create a landing zone: separate accounts or subscriptions for production, staging and development.
• Use single sign-on and role-based access, with multi-factor authentication for every human user.
• Avoid long-lived access keys. Use roles and short-lived credentials for applications and pipelines.
• Encrypt data at rest and in transit, and manage keys through the provider's key management service.
• Choose regions that meet your data residency needs, for example Indian regions for data covered by RBI localisation rules.
• Turn on audit logging and centralise logs before the first workload goes live.
• Define backup schedules and test restores, not just backups.

4. Plan data migration and cutover

Data migration needs its own plan. For small databases, a scheduled downtime with a dump and restore may be fine. For large or always-on systems, use replication so the cloud copy stays in sync until cutover. Validate row counts, checksums and key business totals after each test run.

Plan for large data transfers early. Moving many terabytes over an ordinary internet connection can take far longer than expected, so check bandwidth, compress where possible, or use the provider's offline transfer service for very large datasets.

Rehearse the cutover at least once in a staging environment. Write a runbook with every step, who does it and how long it should take. Lower DNS time-to-live values a few days in advance so traffic switches quickly. Most importantly, define a rollback point: the exact condition under which you switch back, and the steps to do it.

5. Test and go live

Before cutover, test the migrated workloads the way users will actually use them. Run functional tests, load tests at expected peak traffic, and failover tests for anything that must stay available.

Plan go-live during a low-traffic window, keep the old environment available until the new one has run cleanly for an agreed period, and have the team on call with dashboards open. Communicate the schedule to users and support staff in advance.

After go-live, watch error rates, response times and costs daily for the first two weeks, and compare them against the baseline you recorded during assessment. Small issues such as a missing scheduled job or a timezone setting usually surface in this window, and they are cheap to fix while the team is still focused on the migration. Record what went wrong and what took longer on each workload, so every wave goes faster than the one before.

6. Optimize after the move

The first cloud bill is often higher than expected, usually because servers were sized for the old hardware rather than real usage. Optimization is not a one-time task, so build it into monthly operations.

Review the architecture too, not just the bill. Once workloads are stable in the cloud, look for components that would be simpler and cheaper as managed services, such as queues, caches and databases, and plan those changes as normal product work.

Nexzem's cloud team helps businesses plan and run migrations to AWS, Azure and Google Cloud, including post-migration cost reviews. Whether you migrate with a partner or in-house, treat the checklist as a living document and update it after every workload you move.

• Rightsize instances based on a few weeks of real metrics.
• Schedule non-production environments to shut down outside working hours.
• Use savings plans or reserved capacity for steady workloads once usage is predictable.
• Tag every resource with an owner and project so costs can be traced.
• Set budgets and alerts so unexpected spend is caught in days, not at month end.
• Move rarely accessed data to cheaper storage tiers.