Moving SQL workloads to Azure is less about a one-off IT project and more about reimagining how your organisation uses data to create value.

Here are the main reasons companies are making the shift now:

• Prepare for AI-driven products and services
  Cloud-native, intelligent SQL databases in Azure give you an AI-ready foundation. Once your data is in Azure SQL, you can start using services like Azure OpenAI Service, Microsoft Copilot for Microsoft 365, GitHub Copilot and other Azure AI capabilities without first rebuilding your data platform.
• Control data sprawl and centralise operations
  Moving to Azure SQL helps you centralise data, compliance and security instead of managing fragmented databases across multiple servers and locations. This makes it easier to govern data, meet regulatory requirements and align data strategy with business goals.
• Reduce legacy infrastructure costs
  Azure SQL and SQL Server on Azure Virtual Machines help you mitigate the high costs of maintaining legacy hardware and licenses. You can scale resources up or down as needed, rather than overprovisioning on-premises capacity.
• Improve resilience and security
  Azure security services are backed by 3,500 cybersecurity professionals, and built-in resilience helps you avoid costly downtime. With Azure Arc, Defender for Cloud, Azure Monitor and Microsoft Purview, organisations like the World Bank have been able to centrally manage data and security and uncover new efficiencies.
• Scale on demand
  Azure SQL Database and Azure SQL Managed Instance provide near-limitless storage capacity and elastic compute. For example, E.ON moved to Azure SQL Database Hyperscale to support up to 100 TB of data and advanced analytics workloads.

Independent studies highlight the financial impact as well. For example, organisations have seen up to 40% lower costs running SQL Server workloads with Azure SQL, and one customer reported 150% improved productivity and work output by integrating Azure AI Services with their data platform.

In the short term, you can expect lower infrastructure overhead and better reliability. In the medium term, you gain the flexibility to launch new data- and AI-driven services faster, using the same SQL skills your teams already have.

Azure SQL is designed to meet you where you are. You don’t have to move everything the same way. Instead, you can apply the “four Rs” to each workload and choose the right Azure SQL service.

The four Rs of migration and modernisation

1. Rehost (lift and shift)
   Move your existing SQL Server workloads to SQL Server on Azure Virtual Machines with minimal changes.
   Best when: you need to move quickly, keep OS-level access, and avoid code changes.
   Benefits: fast migration, full SQL Server compatibility, ability to maximise existing licenses.
2. Refactor (repackage)
   Make small changes so your apps can use Platform-as-a-Service (PaaS) options like Azure SQL Managed Instance or Azure SQL Database.
   Best when: you want to reduce operational overhead but keep the core app logic intact.
   Benefits: lower management effort, faster and shorter updates, reduced data centre footprint.
3. Rearchitect
   Modify and extend your apps for the cloud, for example by breaking monolithic apps into microservices and using Azure SQL as a modern data layer.
   Best when: you need better scalability, agility and cost efficiency for strategic applications.
   Benefits: improved performance, easier scaling, better use of cloud-native services and automation.
4. Rebuild
   Completely reconstruct the application to fully leverage Azure services and modern development practices.
   Best when: the existing app is too constrained or costly to maintain and is blocking innovation.
   Benefits: faster innovation cycles, ability to mix technology stacks, and reduced long-term operational costs.

Choosing the right Azure SQL service

• SQL Server on Azure Virtual Machines – ideal for lift-and-shift scenarios and workloads that require OS-level access.
• Azure SQL Managed Instance – a fully managed PaaS option with broad SQL Server engine compatibility, suited for modernising existing SQL Server applications at scale.
• Azure SQL Database – a cloud-native relational database with serverless compute and auto-scaling storage, ideal for modern applications and new solutions.
• Azure SQL Edge – for extending SQL capabilities to IoT and edge scenarios.

Because the entire Azure SQL family is built on the same SQL Server database engine, your existing SQL skills and investments carry forward, which reduces risk and speeds up adoption.

Most organisations use an iterative, wave-based approach: they plan, discover and assess, then migrate and modernise groups of workloads in stages. The Microsoft Cloud Adoption Framework for Azure provides guidance, tools and templates to help you plan, govern and manage this journey with confidence.

Azure SQL is designed to be more than a place to store data. It acts as a foundation for AI, analytics and modern applications.

AI and analytics integration

• Built-in intelligence and AI-ready design
  Azure SQL services include built-in insights and AI that help optimise performance and operations. Once your data is in Azure, you can connect it to Azure AI, Azure OpenAI Service, Azure Machine Learning and Power BI to build richer experiences.
• End-to-end data and analytics pipelines
  Organisations like the UK’s Environment Agency use Azure SQL together with Azure Data Factory, Azure Data Lake, Azure Synapse Analytics, Power BI and Power Automate to streamline data flows and reporting. This has helped them manage time better and process applications more efficiently.
• Support for large-scale analytics
  E.ON moved to Azure SQL Database Hyperscale to support up to 100 TB of data, enabling deeper analytics and integration with Azure Machine Learning and Power BI for energy insights.
• AI-enhanced applications
  Azure AI Services can be combined with Azure SQL to build custom copilots and generative AI solutions. One customer reported 150% improvement in productivity and work output after integrating Azure AI Services with their data platform.

Real-world innovation examples

• American Airlines migrated 10 TB of data and a load of roughly 32,000 transactions per second to Azure SQL Managed Instance in phases. This gave them a more responsive, scalable platform for customer data services without disrupting operations.
• Cube RM reimagined tender management for pharmaceutical and medical suppliers using Azure Cognitive Services, Azure OpenAI and Power BI. This combination helped them significantly increase accuracy and granularity for clients in more than 40 countries.
• The World Bank used Azure Arc and SQL Server to centralise management and security. By 2023, they had connected 25% of their SQL Server estate to Azure Arc, improving control and uncovering new efficiencies.

In practice, Azure SQL helps you:

• Unlock your full data estate so AI models can access consistent, governed data.
• Build and deploy new applications faster using fully managed database services.
• Integrate AI directly into apps and workflows, from copilots to advanced search and analytics.

The result is a data platform that not only runs your existing workloads, but also gives you the flexibility to rethink customer experiences, internal processes and new digital products.