April 22, 2025

Azure’s evolution: From traditional IaaS to AI-ready infrastructure

Scott Gosling

National Practice Manager - Microsoft at Data^#3

Connect

As customers increasingly adopt AI, their cloud infrastructure needs to do more than just support traditional workloads. It must enable rapid innovation, seamless data management, robust security, cost efficiency, and multi-cloud compatibility. Microsoft Azure has responded to these demands by transitioning from traditional Infrastructure as a Service (IaaS) to a holistic, AI-ready infrastructure. This new model expands beyond conventional landing zones, integrating data, security, financial operations (FinOps), and multi-cloud management, all while enabling the modernisation of applications and the development of AI solutions.

Building the foundation with traditional IaaS

Traditional IaaS was foundational in bringing agility and scalability to the cloud, enabling users to:

Provision virtual machines (VMs) on demand
Scale storage and network capacity to meet demand
Increase flexibility and reduce capital costs through virtualisation

This technology transformed the way organisations operated, providing a cloud-based environment to manage and host applications, and here at Data^#3 we supported many of our customers to achieve that. However, the increasing demand for AI and data-driven solutions has exposed the limitations of traditional IaaS:

Limited data integration: Legacy landing zones often lack the data pipelines and real-time processing capabilities that AI models require.
Security gaps for AI: AI introduces new security risks, such as model poisoning or data breaches during training and deployment.
Unpredictable costs: Without advanced cost management, organisations often struggle with spiralling expenses as they adopt AI models that require intensive compute resources.
Multi-cloud challenges: Organisations that have embraced multi-cloud strategies require seamless integration across different cloud environments

AI-ready infrastructure is the next evolution, addressing these challenges head-on.

Introducing AI-ready infrastructure

The Data^#3 Azure AI-ready infrastructure model is more than an upgrade; it’s a paradigm shift designed to accommodate both traditional workloads and AI solutions. This infrastructure encompasses data, security, FinOps, and multi-cloud tooling, forming a comprehensive foundation that not only modernises applications but also facilitates the creation of AI solutions.

1. Data-frst approach

AI-ready infrastructure treats data as a core asset, ensuring that data pipelines, storage, and analytics are deeply integrated into the cloud architecture:

Unified data lakes: Azure Data Lake Storage Gen2 offers a unified repository for structured and unstructured data, making it easier to feed AI models with vast datasets for training and inferencing.
Real-time analytics: Azure Synapse Analytics, Event Hub, and Stream Analytics enable real-time data processing, crucial for AI applications that require immediate insights.
AI model integration: Azure Machine Learning natively connects to these data services, allowing seamless training, testing, and deployment of models, ensuring that data flows efficiently from ingestion to AI outputs.

2. Enhanced security for AI workloads

AI introduces specific security needs whether it’s protecting sensitive datasets during training or ensuring model integrity in deployment. AI-ready infrastructure is built to secure AI applications at every layer:

Confidential AI computing: Azure’s confidential computing capabilities, like Azure confidential VMs, protect data during computation, which is essential for handling sensitive AI data models.
Zero Trust security: Azure’s AI-ready infrastructure enforces a Zero Trust architecture, ensuring continuous verification and micro-segmentation to prevent unauthorised access to AI resources and data.
Compliance automation: Tools like Azure Policy, Purview, and Security Center ensure that organisations maintain compliance across AI and data operations, automating risk assessments and data lineage tracking.

3. Optimising AI costs with FinOps

The adoption of AI can lead to unexpected costs, especially with compute-intensive model training. Azure’s AI-ready infrastructure integrates FinOps tools to manage and optimise these costs:

AI-specific cost tracking: Azure Cost Management tracks expenses associated with AI workloads, offering clear visibility into training, deployment, and operational costs.
Predictive cost analysis: Leveraging AI, Azure’s cost management tools predict future spending patterns based on past usage, enabling organisations to plan more effectively.
Resource right-sizing: Azure Advisor now includes AI-specific insights to optimise compute and storage resources, ensuring efficient resource utilisation and reducing waste.

4. Multi-cloud management

AI-ready infrastructure acknowledges that organisations often use multiple cloud providers. Azure’s approach ensures consistent AI deployment and management across different environments:

Azure Arc: Azure Arc extends Azure’s management capabilities to other clouds and on-premises environments, making AI deployments consistent, secure, and manageable across multi-cloud environments.
Cross-cloud machine learning models: Azure Machine Learning can train models across cloud providers, supporting flexible AI development without cloud lock-in.
Unified monitoring: Tools like Azure Monitor and Log Analytics provide a single-pane view of AI applications, enabling holistic insights and troubleshooting across clouds.

AI-Ready landing zones: The next-generation

Azure’s AI-ready landing zones build upon traditional ones, adapting to support modern workloads and AI-centric applications. They offer a pre-configured environment that integrates:

AI data pipelines: Ready-made templates for building data pipelines that feed directly into AI models.
Built-in security and compliance: Advanced security protocols and compliance frameworks tailored to AI, embedded from the start.
AI operations (AIOps): Automated AI management frameworks, such as auto-scaling, predictive alerts, and performance monitoring, all essential for reliable AI operations.

These AI-ready landing zones allow organisations to modernise their applications more effectively, ensuring compatibility with AI workloads and data-driven solutions.

How AI-ready infrastructure enables application modernisation

The transition to AI-ready infrastructure isn’t only about supporting new AI models, it’s a comprehensive strategy for modernising existing applications. Here’s how Azure’s AI-ready infrastructure helps:

Easier modernisation: By integrating AI tools and data services into the infrastructure, existing applications can leverage AI capabilities, such as real-time insights, predictive analytics, and improved user personalisation.
Smarter workloads: Applications can benefit from real-time data processing, enabling smarter workflows and automation.
Integrated innovation: Developers can now build AI solutions on top of existing applications, using the same data repositories and compute resources, reducing the friction between traditional and AI-based workloads.

Steering into the future with AI-ready infrastructure

Azure’s transition from traditional IaaS to AI-ready infrastructure reflects the evolving needs of modern enterprises. By focusing on data, security, FinOps, and multi-cloud management, this new infrastructure foundation enables organisations to modernise applications and create AI solutions more effectively. Whether it’s real-time analytics, predictive AI models, or cross-cloud integrations, Azure’s AI-ready infrastructure is designed to power the next wave of innovation.

Organisations looking to gain a competitive edge should consider transitioning to Azure’s AI-ready infrastructure not merely as a technical upgrade, but as a strategic enabler for AI-driven digital transformation.

Trust Data^#3 on your journey to the cloud

Data^#3’s validated maturity journey for cloud adoption has been proven to successfully guide and support customers throughout their Azure journey. The three steps ensure readiness to adopt AI services by having an AI Ready Infrastructure, Applications are modernised using Azure services and AI Solutions built to enhance customer services and the drive efficiency.

At every stage, we draw on our deep expertise and advanced Azure certifications. Aligned to Microsoft’s Well Architected Framework and the Cloud Adoption Framework (CAF) methodologies, we are able to offer proven, consistent and repeatable services at all stages a predictable price model.

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