How do I move workloads from one cloud provider to another?

Treat it as a planned migration and cutover, not a direct copy. A safe approach is:

1. Set the goal and boundaries. Decide why you’re moving, which workloads are in scope, your deadline, acceptable downtime, and what “success” means—such as performance, reliability, security, and cost.
2. Inventory and map dependencies. Document applications, VMs, databases, storage, network routes, DNS, identity, secrets, monitoring, backups, licensing, and external integrations. Record baseline performance and costs. Missing dependencies are a common source of migration trouble. (learn.microsoft.com)
3. Choose an approach for each workload. You might rehost it largely as-is, replatform it with modest changes, refactor it to use the new provider’s services, replace it with SaaS, keep it where it is for now, or retire it. Don’t assume every source-cloud service has a direct equivalent. (docs.aws.amazon.com)
4. Build the destination foundation first. Set up accounts/projects, network and connectivity, identity and access controls, security policies, logging, backups, monitoring, and infrastructure-as-code before production workloads arrive.
5. Pilot, then migrate in waves. Start with a low-risk workload to test your process. Group later migrations by dependencies and business criticality, and validate each wave before moving on. (cloud.google.com)
6. Plan data movement and cutover carefully. Choose a transfer method based on data volume and downtime tolerance. For databases, a typical pattern is initial copy, ongoing replication, validation, a short write freeze or controlled cutover, then redirect traffic. Test rollback before the production cutover.
7. Verify before decommissioning the source. Test functionality, data integrity, performance, security, recovery, and cost against your baseline. Keep the old environment available until the new one is stable; then retire it deliberately. (learn.microsoft.com)
8. Optimize after the move. Right-size resources, tune performance, review service choices and costs, and update runbooks and on-call procedures.

A practical starting point: choose one non-critical application, map its dependencies, run it in the target cloud, rehearse migration and rollback, and use what you learn to shape the larger plan. The specifics depend heavily on the workload—especially whether it’s a VM-based app, Kubernetes service, or database—and on which providers you’re moving between.

Moving a workload from one cloud to another is usually a planned project with five broad phases: assess, design, move data, move compute, then cut over and clean up.

1. Inventory and classify each workload
List every application, database, and service, along with its dependencies, data volumes, and compliance requirements. Once there is inventory, put each workload into one of three buckets. Cloud-ready workloads can move with minimal changes. Workloads that need refactoring require redesign before they suit the target environment. Some may be better left where they are. Common migration strategies are rehost (lift-and-shift), replatform, and refactor.

2. Map equivalent services on the target cloud
Translate each component to its counterpart on the new provider: compute (VMs, containers, Kubernetes), managed databases, object storage, queues, load balancers, IAM, and DNS. Note the gaps. Managed services rarely map one-to-one, so some work may involve swapping in a different service or adjusting code.

3. Reduce lock-in before you move
Containers, Terraform or other infrastructure-as-code, and open-source databases (PostgreSQL, MySQL) make rebuilding on the new cloud far easier than re-clicking through a console. Identity, networking, logging, and monitoring also need a plan so they work consistently across both environments.

4. Move the data and do it in stages

• Estimate egress fees from the source cloud, which can be large for big datasets. Check whether the provider offers waived or reduced transfer pricing for migrations.
• Use the provider's transfer tools or a managed transfer service for bulk copies, or a physical transfer device for very large datasets.
• For databases, run an initial bulk copy, then continuous replication to keep the target in sync until cutover.
• Verify checksums and row counts after each copy.

5. Migrate compute with a pilot first
Start with a low-risk, non-critical system and move it end to end. Then move the rest in waves. Each cloud has migration services for VMs, such as AWS Application Migration Service, Azure Migrate, and Google Cloud's VM migration tooling; check each vendor's current docs for availability and supported sources.

6. Test, cut over, and roll back if needed

• Test functionality, performance, and security in the new environment against the original.
• Plan the cutover window: lower DNS TTLs ahead of time, freeze writes or sync the final delta, switch traffic, then monitor.
• Keep the source environment running for a set period so you can roll back.

7. Decommission and optimize
After you've confirmed stability, shut down the old resources, delete leftover snapshots, buckets, and keys, and rightsize the new environment. Cancel the old contracts only after the data is verified.

Practical tips

• Plan for a temporary period of running both clouds, which raises costs.
• Test restores, not just backups.
• Confirm compliance requirements (such as HIPAA, PCI-DSS, or data residency) for the target region and services before moving regulated data.
• If you're a small team, consider a migration partner or a managed migration service rather than doing everything in-house.

If you tell me which two providers you're moving between, what workloads you run (VMs, Kubernetes, databases, serverless), and roughly how much data is involved, I can help you build a more specific plan.

Moving workloads between cloud providers (often called cloud-to-cloud migration) is a strategic process that requires careful planning to avoid service disruption, data loss, and unexpected costs.

Rather than just a technical "move," this should be treated as a business project that evaluates whether a workload should be migrated, re-architected, or left where it is.

1. The Strategic Framework (The "R's")

Before moving anything, categorize each workload to determine the best approach. Most organizations use the "7 R's" framework:

• Rehost (Lift and Shift): Moving applications as-is to the new cloud with minimal changes. Fastest, but often doesn't take advantage of cloud-native benefits.
• Replatform: Making minor optimizations (e.g., switching to a managed database service) without changing the core architecture.
• Refactor/Rearchitect: Rewriting code or changing the architecture to be cloud-native (e.g., moving to serverless or containers).
• Repurchase: Switching to a different SaaS product.
• Retire: Decommissioning applications that are no longer needed.
• Retain: Keeping the application in its current environment.
• Relocate: Transferring virtualized workloads while preserving the existing platform setup.

2. The Migration Process

A successful migration generally follows these five stages:

1. Assessment & Discovery: Create a full inventory of your applications, databases, and dependencies. Use monitoring tools (e.g., Datadog) to understand performance baselines and identify what can be left behind.
2. Planning: Define your objectives (e.g., cost savings, better performance) and map out the order of migration. Design the target architecture in the new cloud (e.g., VPC setup, security, and access policies).
3. Migration Execution: Perform the actual data and application transfer using specialized tools.
4. Testing & Validation: Verify that the workload functions correctly in the new environment before the final switch.
5. Cutover & Optimization: Switch traffic to the new environment and perform post-migration monitoring to ensure performance meets your requirements. Optimize infrastructure to stop unnecessary spending.

3. Essential Tools & Considerations

• Migration Tools: Many providers offer native tools, such as Azure Migrate, AWS Application Migration Service, and Google Cloud Migrate. Third-party solutions like RackWare, MigrationWiz, and VMware HCX are also commonly used for complex or multi-cloud environments.
• Data Security: Always ensure data is encrypted in transit and at rest. Establish identity and access management (IAM) policies before data is moved.
• Avoid "Accidental" Multi-Cloud: Moving workloads should be intentional. Avoid moving things simply to follow a trend; ensure there is a clear business case (e.g., avoiding vendor lock-in, data sovereignty, or cost optimization).
• Dependency Mapping: One of the biggest pitfalls is failing to account for how applications talk to each other. Ensure your networking and service dependencies are mapped to avoid "outage cascades".

Summary Checklist

• [ ] Inventory: Have you mapped every dependency?
• [ ] Strategy: Have you applied the "7 R's" to every workload?
• [ ] Testing: Is there a Proof of Concept (PoC) phase?
• [ ] Security: Is encryption and compliance (GDPR, etc.) planned for the target environment?
• [ ] Optimization: Do you have a plan to right-size resources after the move to avoid overspending?

Moving workloads between cloud providers is best treated as a planned series of small migrations—not a bulk copy. A practical approach:

1. Set goals and constraints. Define why you’re moving, what success means, and acceptable downtime, performance, security, compliance, and cost. Include the ongoing cost of operating both providers during transition.

1. Inventory workloads and dependencies. Catalog applications, databases, storage, network connections, identities, integrations, and operational tooling. Map which services depend on each other; use that map to group workloads into migration waves. ¹²

1. Choose a migration strategy for each workload. Some can move largely unchanged (“rehost”); others may need adjustment to use the target provider’s services (“replatform” or “refactor”). You might instead replace a system with a SaaS product, keep it where it is, or retire it. The right choice depends on business value, technical constraints, and the reason for moving. ³

1. Prepare the destination. Set up identity and access, networking, security controls, logging, backups, monitoring, and cost governance before moving production systems. Pilot the setup with a small, representative workload first. ¹

1. Plan data movement and cutover. Estimate transfer time, bandwidth, and costs; consider an initial bulk copy followed by ongoing replication to limit downtime. Test data completeness and application behavior, and decide in advance how you’ll switch traffic and what conditions trigger a rollback. ⁴⁵

1. Migrate in waves and validate. Move a low-risk workload first, then expand. Check functionality, performance, integrations, security, backups, and operational alerts on the destination. Cut over only after the checks pass; keep the source available until the new environment is stable and rollback is no longer needed. ⁵⁶

1. Optimize and decommission. Monitor reliability and costs after each cutover, tune the new setup, and remove old resources only when data retention, recovery, and business requirements are satisfied. ²⁵

For a first move, choose a workload with manageable dependencies and a clear owner. A simple migration runbook should record the migration sequence, responsible people, validation steps, cutover window, rollback procedure, and criteria for shutting down the source. ⁶

Sources
¹ AWS Prescriptive Guidance: Migration overview
² Google Cloud: Prepare workloads for migration
³ AWS: Cloud migration strategies
⁴ Google Cloud: Data migration
⁵ Google Cloud: Database migration overview and best practices
⁶ Microsoft Learn: Plan your migration