Azure Update - 9th January 2026

Azure Update - January 9th, 2026

Key Concepts: Azure Kubernetes Service (AKS), Premium SSD v2, Service Bus Geo-Replication, Osmos Acquisition & Data Integration, Custom Resource Providers (CRP), Dragon HD Omni (Text-to-Speech), GPT-4 Model Versions, Prompt Engineering, Large Language Models (LLMs), Vector Embeddings.

Compute Updates – Azure Kubernetes Service (AKS) & Pricing

A new scenario has been added to the Azure pricing calculator specifically for creating cloud-native applications using AKS. This simplifies cost estimation by including all necessary components – AKS itself, Azure Container Registry, Azure Monitoring, a Load Balancer, and Defender for Cloud – in a single estimate. The calculator also visually displays the architecture of these integrated components. This feature aims to provide a more accurate and transparent understanding of the total cost of deploying a cloud-native application on Kubernetes.

Storage Updates – Premium SSD v2 Region Expansion

Premium SSD v2 is now available in Austria East and Japan West. This storage offering allows independent scaling of IOPS (Input/Output Operations Per Second) and throughput from storage capacity, with dynamic adjustment capabilities. It delivers sub-millisecond latencies, second only to Ultra Disk, making it suitable for I/O intensive workloads like databases, big data analytics, and gaming databases.

Networking Updates – Service Bus Premium Geo-Replication

Service Bus Premium now supports geo-replication for disaster recovery. This feature replicates both configuration metadata and data from a primary region to one or more secondary regions. Replication can occur synchronously or asynchronously, with trade-offs between latency and potential data loss. Asynchronous replication is often preferred for cross-region scenarios, despite the risk of minor data loss, to minimize acknowledgement times for transactions. Service Bus facilitates decoupled application architectures through publish-subscribe messaging, reducing tight coupling between modules and applications.

Data & AI – Osmos Acquisition & Microsoft Fabric Integration

Microsoft’s acquisition of Osmos is focused on improving data integration into Microsoft Fabric. The core problem Osmos addresses is the challenge of understanding and transforming data from diverse sources, schemas, and formats into a unified format within Fabric’s OneLake. Osmos utilizes Large Language Models (LLMs) and specialized fine-tuning to analyze data schemas (e.g., schema A to schema B) and automatically determine the necessary transformations, maintaining data lineage as schemas evolve. This aims to provide a more intelligent and automated data engineering process.

Deprecation – Custom Resource Providers (CRP)

Custom Resource Providers (CRPs) are being deprecated, with end-of-life scheduled for October 31st, 2026. CRPs enable extending the Azure Resource Manager (ARM) control plane by integrating external APIs and custom resources. This allowed managing legacy systems or third-party APIs through standard ARM operations (create, update, delete). Users currently utilizing CRPs are advised to migrate to alternative solutions such as deployment scripts or Bicep extensions.

New in Preview – Dragon HD Omni Text-to-Speech

Dragon HD Omni, a new text-to-speech model, is now available in preview. It offers over 700 high-quality voices with expressive capabilities and multilingual fluency. Users can specify desired speaking styles (e.g., curious, angry, embarrassed, “chill surfer”), allowing for highly customized audio output. Access is available through the Speech Playground and APIs.

Model Updates – GPT-4 Version Retirement

Specific versions of GPT-4 (20240513 and 20240806) are being retired on March 31st, 2026. Users with auto-upgrade enabled will automatically transition to GPT-51. Those without auto-upgrade should manually update and thoroughly retest their AI applications and agents after the upgrade. The speaker emphasized the non-deterministic nature of LLMs – meaning the same input can produce different outputs – and the importance of using evaluations to ensure consistent and expected behavior.

Notable Quote:

“Prompt engineering, that context engineering is so important. You need to be specific to get the right type of output.” – Speaker, referencing a lesson learned while using AI to generate a thumbnail.

Technical Terms:

• IOPS (Input/Output Operations Per Second): A measure of disk performance, indicating the number of read/write operations a storage device can perform per second.
• Throughput: The rate at which data can be transferred, typically measured in megabytes per second (MB/s).
• ARM (Azure Resource Manager): The deployment and management service for Azure.
• Bicep: A declarative language for deploying Azure resources.
• LLM (Large Language Model): A type of artificial intelligence model trained on a massive amount of text data, capable of generating human-like text.
• Vector Embeddings: Numerical representations of data (e.g., text, images) that capture semantic meaning, used for similarity searches and AI applications.
• Geo-Replication: The process of replicating data to multiple geographic locations for disaster recovery and high availability.
• Synchronous Replication: Data is written to both primary and secondary locations simultaneously, ensuring data consistency but potentially increasing latency.
• Asynchronous Replication: Data is written to the primary location first, then replicated to secondary locations, offering lower latency but with a potential for data loss in case of a primary region failure.

Logical Connections:

The update follows a logical flow, starting with compute, then storage, networking, data/AI, deprecations, new previews, and finally model updates. Each section builds upon the previous, showcasing a broad range of Azure advancements. The discussion of Osmos directly relates to the broader theme of AI-powered data integration within the Microsoft ecosystem. The deprecation of CRPs highlights the ongoing evolution of Azure’s infrastructure and the need for users to adapt to new technologies.

Data & Research Findings:

• Premium SSD v2 offers sub-millisecond latencies, second only to Ultra Disk.
• Dragon HD Omni provides over 700 high-quality voices.

Synthesis/Conclusion:

This Azure update highlights a continued focus on enhancing cost management, improving data resilience, and integrating AI capabilities across various services. The addition of the AKS pricing scenario, the expansion of Premium SSD v2, and the geo-replication features for Service Bus all contribute to a more robust and cost-effective Azure platform. The Osmos acquisition signals a commitment to simplifying data integration within Microsoft Fabric, while the deprecation of CRPs and the GPT-4 version retirement underscore the importance of staying current with Azure’s evolving ecosystem. The introduction of Dragon HD Omni demonstrates ongoing innovation in AI-powered services. The overall message is one of continuous improvement and a commitment to providing developers and IT professionals with the tools they need to build and deploy modern cloud applications.