In Flexera's 2024 State of the Cloud survey, 59% of organizations reported using more than one public cloud provider, a figure drawn mostly from large enterprises but reflective of a broader pull away from single-vendor dependency.
Multi-cloud migration means moving or placing workloads across two or more public cloud providers, think AWS and Azure, or AWS and Google Cloud, working together. That's different from a single-cloud migration (moving everything into one provider) and different from hybrid cloud (connecting on-premises infrastructure to a single public cloud, such as through AWS Outposts or Azure Arc).
This guide covers why multi-cloud makes sense, how to categorize workloads for placement, a six-phase migration roadmap, the technical pitfalls that trip up most teams, and the tools that make execution manageable without a massive IT department.
Key Takeaways
- Multi-cloud means workloads on two or more public clouds—not a single cloud tied to a private data center
- The 7 Rs framework decides where and how each application should move across providers
- A phased roadmap from assessment through FinOps cuts costly mid-migration surprises
- Unmanaged data egress and inter-cloud latency are the costs most likely to blow your budget
- Specialized AWS partners close skills gaps without enterprise consulting fees
Strategic Advantages of Adopting a Multi-Cloud Architecture
Avoiding Vendor Lock-In and Gaining Leverage
Spreading workloads across providers means no single vendor holds all the leverage. Google's own guidance on multicloud cites workload fit and reduced dependence on one provider as core reasons companies diversify. That same guidance notes cross-cloud performance can be inconsistent and duplicate deployments add cost.
For a mid-market business, the practical upside is the option to move a future workload somewhere cheaper or better suited, without a full re-platform. That option only pays off once you've tested the move technically, not just assumed it will work.
Eliminating Single Points of Failure
A second provider removes one company's bad day from taking down your entire operation. If one region of one cloud goes dark, a workload replicated elsewhere keeps running.
That resilience isn't free, though:
- Duplicating an application for failover adds compute, storage, and licensing costs
- Data replication across providers introduces lag that needs testing, not assuming
- Disaster recovery should target workloads where downtime is genuinely expensive, not every app in the portfolio
Matching Workloads to the Right Platform
Different providers excel at different things. A common multi-cloud split looks like:
- AWS for compute-heavy, customer-facing applications, using EC2 for scalable compute and S3 for durable object storage
- Google Cloud for analytics workloads that benefit from BigQuery's query performance
- Azure for machine learning and AI workloads built around its AI services
Placing each workload on its best-fit platform beats forcing everything onto one vendor just because "it's already there."
The 7 Rs Framework Adapted for Multi-Cloud Workload Placement
AWS's prescriptive guidance for large migrations defines seven strategies for moving applications: Rehost, Replatform, Refactor, Repurchase, Retire, Retain, and Relocate. These aren't sequential steps every app passes through. They're seven different destinations, and your job is picking the right one for each workload.
Assessing Cloud Readiness
Before categorizing anything, assess three things for every application:
- Legacy dependencies - what does this app need to function, including databases, licensing servers, or on-prem authentication?
- Data gravity - where does the bulk of the data already live, and what does moving it cost?
- Latency sensitivity - does this workload need response times that cross-cloud networking can't reliably guarantee?
This kind of assessment, mapping interdependencies and compliance requirements like HIPAA or PCI-DSS before assigning destinations, is typically the first deliverable in any serious migration engagement.
Categorizing Workloads Across Providers
Once you know what you're working with, sort applications into the seven buckets:
| Strategy | Best for | Multi-cloud consideration |
|---|---|---|
| Rehost | Apps with no urgent need to change | Fast move to a compatible IaaS environment |
| Relocate | Platform-level moves (such as VMware-based instances) | Keep within compatible infrastructure types |
| Replatform | Apps needing minor upgrades | Good fit for managed databases or containers |
| Refactor | Apps that benefit from cloud-native rebuilds | Strong candidate for multi-cloud Kubernetes |
| Repurchase | Apps better replaced with SaaS | Removes a migration dependency entirely |
| Retain | Apps with heavy on-prem ties or ultra-low latency needs | Leave in place; revisit later |
| Retire | Redundant or unused systems | Decommission instead of migrating |
Match surviving workloads to a provider based on:
- Compute and storage pricing for that specific workload profile
- Access to specialized AI/ML toolsets, such as Bedrock, Vertex AI, or Azure OpenAI Service
- Geographic coverage where customers or compliance rules require data residency
A legacy ERP might get Replatformed onto managed database services. A machine learning pipeline might get Refactored and placed wherever the AI toolset actually fits. Not every app needs a dramatic rebuild to belong in a multi-cloud footprint.
A Step-by-Step Multi-Cloud Migration Roadmap
Phase 1: Pre-Migration Assessment and Baseline KPI Definition
Start with a full application inventory: owners, dependencies, databases, and traffic patterns. Document baseline KPIs now so you have something to measure against after migration:
- Response time
- Monthly spend
- Uptime
Skipping this step is the most common reason migrations run over budget.
Phase 2: Target Architecture Design and Inter-Cloud Connectivity
Design the actual network path between providers before projecting costs or latency. Google's Cross-Cloud Interconnect connects Google Cloud directly to AWS or Azure; AWS Direct Connect and Azure ExpressRoute primarily connect customer networks into their own cloud. These aren't interchangeable, so map the real route your data will travel, including API gateway routing for cross-cloud service calls.
Phase 3: Security, Governance, and Unified Identity Frameworks
Apply a Zero Trust baseline: no implicit trust based on network location, least-privilege access everywhere. Centralize role-based access control and federate identity across providers so a user or service account doesn't need separate credentials per cloud. This step determines whether your security team can actually see what's happening across both environments.
Phase 4: Staged Migration Execution and Pilot Testing
Move one low-risk, non-critical workload first. Test failover, measure replication lag, and confirm data pipelines behave as expected before scaling up. This is where theoretical planning either holds up or falls apart.
For SMB clients, Cloudtech typically runs this phase with AWS Fault Injection Simulator to test failover before anything touches production. Pre-packaged migration frameworks and AWS-certified architects help compress timelines that would otherwise stretch for months.
As an AWS Advanced Tier Partner, Cloudtech can also help clients capture AWS Migration Acceleration Program (MAP) funding to offset costs.
Phase 5: Cross-Cloud Automation and Orchestration
Keep multi-cloud builds repeatable with a few core practices:
- Provision with Infrastructure as Code (Terraform or Pulumi) and review every change before deployment
- Pair IaC with automated CI/CD pipelines so releases stay consistent instead of manual and error-prone
- Use Kubernetes to package containerized apps portably—not as a substitute for database replication tooling
Phase 6: Post-Migration Optimization and FinOps Governance
After cutover, run application performance monitoring against your Phase 1 baselines. Set up a small cross-functional group (engineering, finance, and a business owner) to track cost allocation and access policy.
Rather than building a full enterprise Cloud Center of Excellence from day one, start small and grow it as spend and complexity increase. Tag everything by application and cost center so you can see exactly where multi-cloud spend is going, including the traffic between providers.
Key Multi-Cloud Migration Challenges and Technical Solutions
Managing Inter-Cloud Latency and Data Egress Costs
Moving data between providers costs money and adds delay. Cut both with:
- Cache data locally so repeat requests stay inside one cloud
- Compress payloads before transfer to shrink egress volume
- Use direct interconnects instead of public internet routes for steadier latency
- Keep compute next to the data it processes instead of querying across clouds
Budget the full round trip, not just the advertised per-GB rate. Actual cost still depends on route, destination, and volume.
Overcoming Fragmented Visibility and Security Gaps
Separate dashboards per provider leave no one with the full picture. Close the gap with two shared controls:
- Centralized observability that pulls AWS, Azure, and Google Cloud telemetry into one view
- Unified Cloud Security Posture Management that flags misconfigurations and identity risks in every cloud
Addressing Data Synchronization and Consistency Issues
Change Data Capture tools like AWS DMS replicate database changes across environments, but CDC is not real-time. There's always some lag, and no vendor guarantees a specific SLA for it. Before relying on a synced copy:
- Define an acceptable replication lag threshold
- Assign a single write owner per dataset
- Decide whether the workload needs strong consistency or can tolerate eventual consistency
- Test failover with real data, not a staged demo
Bridging the Cross-Platform Skills and Resource Gap
Few internal teams are equally fluent in AWS, Azure, and Google Cloud. Flexera's State of the Cloud research regularly ranks lack of expertise among the top cloud challenges, and multi-cloud multiplies that gap instead of splitting it.
Two responses work better than hiring a full multi-cloud team from scratch:
- Narrow the first migration to a workload your existing team can secure and recover confidently
- Bring in a boutique AWS partner for the AWS portion of the estate while your team stays focused on the rest
For SMBs, Cloudtech fills that AWS-side gap with human-first consulting, AWS-certified architects, and pre-packaged migration frameworks delivered in weeks—not enterprise-scale retainers.
Selecting the Right Multi-Cloud Migration and Management Tools
Native Provider Assessment and Migration Tools
Each major provider ships its own discovery and migration utilities, built to move workloads into that specific cloud:
- AWS Application Discovery Service and DMS: inventory on-prem servers and replicate databases into AWS
- Azure Migrate: discovers and assesses servers, including VMs in other clouds, for a move to Azure
- Google Cloud Migration Center: inventories source assets and estimates costs for a move to Google Cloud
None of these is a cross-cloud control plane. Pick the tool that matches each workload's destination—not one tool for everything. For the AWS leg of a multi-cloud move, Cloudtech typically pairs Migration Evaluator, Application Migration Service (MGN), and DMS with a Well-Architected review so the target design is validated before cutover.
Infrastructure Automation and Orchestration Platforms
Terraform and Pulumi both provision infrastructure declaratively across multiple providers from one codebase, with changes reviewed before they deploy. Kubernetes handles container orchestration portably across clouds, though it manages compute, not database replication. Used together, they give you one workflow to deploy and manage infrastructure no matter which provider a workload lands on.
Observability and FinOps Cost Management Suites
Single-pane-of-glass visibility matters once you're running on two or more bills. Platforms like Datadog correlate cost and performance telemetry across AWS, Azure, and Google Cloud, while CloudHealth focuses on financial reporting and cost recommendations. Choose based on whether your bigger gap is operational visibility or cost governance. Most SMBs eventually need both—just not on day one.
Frequently Asked Questions
How much does multi-cloud cost?
Costs vary widely based on workload scale, data transfer volume, and tooling choices. Without FinOps controls like tagging and regular spend reviews, it's easy to end up paying for redundant infrastructure you don't need.
What are the 7 types of cloud migration?
The 7 Rs are Rehost, Relocate, Replatform, Refactor, Repurchase, Retain, and Retire. Each describes a different way to handle an individual application, not sequential migration stages.
What are the top cloud migration tools?
Common options include AWS DMS, Azure Migrate, Google Cloud Migration Center, Terraform, Kubernetes, Datadog, CloudHealth, Pulumi, and AWS Application Discovery Service. The right mix depends on your destination cloud and whether you need cross-provider automation.
What is the difference between multi-cloud and hybrid cloud?
Hybrid cloud connects private or on-premises infrastructure with one public cloud provider. Multi-cloud uses services from two or more public cloud providers, such as AWS and Google Cloud, with no private environment required.
How do you manage data egress costs during migration?
Keep compute close to where data is stored, use dedicated interconnects instead of public internet routing, and cache frequently accessed data locally. Minimizing unnecessary cross-cloud "trombone" traffic is usually the biggest lever you have.
