Cloud Migration

What is cloud migration?

Cloud migration means moving workloads that run on servers a company owns or rents, often in its own office or a data center, onto infrastructure provided by cloud vendors such as AWS, Microsoft Azure or Google Cloud. It can also mean moving from one cloud to another, or from self-hosted software to a SaaS product that does the same job.

A migration usually covers three things: the applications themselves, the data they use, and the surrounding setup, such as networking, identity, backups and monitoring. For example, a company might move its file servers to cloud storage, its on-premise database to a managed cloud database, and its reporting into a cloud data warehouse such as Snowflake or BigQuery.

Businesses migrate to reduce hardware upkeep, scale with demand, improve resilience, and make systems easier to connect with modern tools and AI services. Cloud migration often forms part of wider legacy system modernization or digital transformation work.

What are the main cloud migration strategies?

• Rehost ("lift and shift"): move applications as they are onto cloud servers. Fastest, but keeps old inefficiencies.
• Replatform: make small changes to use managed services, such as moving a database to a managed version.
• Refactor: redesign the application to use cloud-native services. Most effort, most long-term benefit.
• Repurchase: replace the application with a SaaS product.
• Retire or retain: switch off what is no longer needed, and keep on-premise what has good reason to stay.

Most migrations mix these approaches. A practical plan assigns a strategy to each application based on its value, complexity and risk, rather than applying one approach to everything. Systems with little remaining value are often better retired than moved.

What are the risks of cloud migration?

Cost surprises are the most common complaint. Cloud bills are based on usage, and systems moved without changes, or left running when idle, can cost more than expected. Tagging resources, setting budgets and alerts, and reviewing usage regularly help keep spending visible.

Other risks include downtime during cutover, data loss or corruption in transfer, performance problems when applications that used to sit next to each other end up far apart, and security gaps from misconfigured storage or overly broad access.

Plan migrations in waves, starting with less critical systems. Test data integrity before and after each move, keep the old environment available until the new one is proven, and apply clear data security rules from the first day rather than retrofitting them.

How do you plan a cloud migration?

Start with an inventory: every application, database and integration, who uses it, what it depends on, and how critical it is. Dependencies matter most, since moving one system can break another that quietly relies on it.

Then decide the target setup and a strategy per workload, group systems into migration waves, and define success criteria for each wave, such as performance, cost and error rates. Set up the foundations first: accounts, networking, identity and access, backups, monitoring and cost controls.

Run a pilot with a low-risk system to test the process end to end. Then migrate wave by wave, validate each one, and decommission the old infrastructure only once the new setup has run reliably. Document the new environment as you go, so the team can run it confidently afterwards.

How does cloud migration affect data and analytics?

Moving to the cloud often changes how a company handles data. Cloud data warehouses and lakehouses separate storage from computing power, so teams can keep more history and run heavier queries without buying hardware. Managed connectors make it easier to pull data from SaaS tools into one place.

That creates a chance to fix old problems rather than copy them. A migration is a good moment to clean up duplicate tables, define which system owns each piece of data, and set up proper access controls and documentation.

It also makes AI projects more practical. Cloud platforms offer managed AI services and connect more easily with modern tools, and AI models need well-organized, accessible data to produce useful results. A migration that ends with a tidy, connected data platform supports far more than infrastructure goals.

Where does cloud migration fit in Vizio AI's work?

Our DX Studio focuses on the data and integration side of cloud migration. We design and build data platforms on cloud tools such as warehouses and databases, move data from on-premise or legacy systems into them, and rebuild the integrations, workflow automations and BI dashboards that depend on that data. We work around the tools a business already uses and support the systems after launch.

The starting point is mapping which systems and data sources exist today, what depends on them, and which reports or processes the migration needs to keep running or improve.