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GitHub scales on demand with Azure Functions

GitHub is the home of the world’s software developers, with more than 100 million developers and 420 million total repositories across the platform. To keep everything running smoothly and securely, GitHub collects a tremendous amount of data through an in-house pipeline made up of several components. But even though it was built for fault tolerance and scalability, the ongoing growth of GitHub led the company to reevaluate the pipeline to ensure it meets both current and future demands. 

“We had a scalability problem, currently, we collect about 700 terabytes a day of data, which is heavily used for detecting malicious behavior against our infrastructure and for troubleshooting. This internal system was limiting our growth.”

—Stephan Miehe, GitHub Senior Director of Platform Security

GitHub worked with its parent company, Microsoft, to find a solution. To process the event stream at scale, the GitHub team built a function app that runs in Azure Functions Flex Consumption, a plan recently released for public preview. Flex Consumption delivers fast and large scale-out features on a serverless model and supports long function execution times, private networking, instance size selection, and concurrency control.

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In a recent test, GitHub sustained 1.6 million events per second using one Flex Consumption app triggered from a network-restricted event hub.

“What really matters to us is that the app scales up and down based on demand. Azure Functions Flex Consumption is very appealing to us because of how it dynamically scales based on the number of messages that are queued up in Azure Event Hubs.”

—Stephan Miehe, GitHub Senior Director of Platform Security

In a recent test, GitHub’s new function app processed 1.6 million messages per second in the Azure Functions Flex Consumption plan.

A look back

GitHub’s problem lay in an internal messaging app orchestrating the flow between the telemetry producers and consumers. The app was originally deployed using Java-based binaries and Azure Event Hubs. But as it began handling up to 460 gigabytes (GB) of events per day, the app was reaching its design limits, and its availability began to degrade.

For best performance, each consumer of the old platform required its own environment and time-consuming manual tuning. In addition, the Java codebase was prone to breakage and hard to troubleshoot, and those environments were getting expensive to maintain as the compute overhead grew.

“We couldn’t accept the risk and scalability challenges of the current solution,“ Miehe says. He and his team began to weigh the alternatives. “We were already using Azure Event Hubs, so it made sense to explore other Azure services. Given the simple nature of our need—HTTP POST request—we wanted something serverless that carries minimal overhead.”

Familiar with serverless code development, the team focused on similar Azure-native solutions and arrived at Azure Functions.

“Both platforms are well known for being good for simple data crunching at large scale, but we don’t want to migrate to another product in six months because we’ve reached a ceiling.”

—Stephan Miehe, GitHub Senior Director of Platform Security

A function app can automatically scale the queue based on the amount of logging traffic. The question was how much it could scale. At the time GitHub began working with the Azure Functions team, the Flex Consumption plan had just entered private preview. Based on a new underlying architecture, Flex Consumption supports up to 1,000 partitions and provides a faster target-based scaling experience. The product team built a proof of concept that scaled to more than double the legacy platform’s largest topic at the time, showing that Flex Consumption could handle the pipeline.

“Azure Functions Flex Consumption gives us a serverless solution with 100% of the capacity we need now, plus all the headroom we need as we grow.”

—Stephan Miehe, GitHub Senior Director of Platform Security

Making a good solution great

GitHub joined the private preview and worked closely with the Azure Functions product team to see what else Flex Consumption could do. The new function app is written in Python to consume events from Event Hubs. It consolidates large batches of messages into one large message and sends it on to the consumers for processing.

Finding the right number for each batch took some experimentation, as every function execution has at least a small percentage of overhead. At peak usage times, the platform will process more than 1 million events per second. Knowing this, the GitHub team needed to find the sweet spot in function execution. Too high a number and there’s not enough memory to process the batch. Too small a number and it takes too many executions to process the batch and slows performance.

The right number proved to be 5,000 messages per batch. “Our execution times are already incredibly low—in the 100–200 millisecond range,” Miehe reports.

This solution has built-in flexibility. The team can vary the number of messages per batch for different use cases and can trust that the target-based scaling capabilities will scale out to the ideal number of instances. In this scaling model, Azure Functions determines the number of unprocessed messages on the event hub and then immediately scales to an appropriate instance count based on the batch size and partition count. At the upper bound, the function app scales up to one instance per event hub partition, which can work out to be 1,000 instances for very large event hub deployments.

“If other customers want to do something similar and trigger a function app from Event Hubs, they need to be very deliberate in the number of partitions to use based on the size of their workload, if you don’t have enough, you’ll constrain consumption.”

—Stephan Miehe, GitHub Senior Director of Platform Security

Azure Functions supports several event sources in addition to Event Hubs, including Apache Kafka, Azure Cosmos DB, Azure Service Bus queues and topics, and Azure Queue Storage.

Reaching behind the virtual network

The function as a service model frees developers from the overhead of managing many infrastructure-related tasks. But even serverless code can be constrained by the limitations of the networks where it runs. Flex Consumption addresses the issue with improved virtual network (VNet) support. Function apps can be secured behind a VNet and can reach other services secured behind a VNet—without degrading performance.

As an early adopter of Flex Consumption, GitHub benefited from improvements being made behind the scenes to the Azure Functions platform. Flex Consumption runs on Legion, a newly architected, internal platform as a service (PaaS) backbone that improves network capabilities and performance for high-demand scenarios. For example, Legion is capable of injecting compute into an existing VNet in milliseconds—when a function app scales up, each new compute instance that is allocated starts up and is ready for execution, including outbound VNet connectivity, within 624 milliseconds (ms) at the 50 percentile and 1,022 ms at the 90 percentile. That’s how GitHub’s messaging processing app can reach Event Hubs secured behind a virtual network without incurring significant delays. In the past 18 months, the Azure Functions platform has reduced cold start latency by approximately 53% across all regions and for all supported languages and platforms.

Working through challenges

This project pushed the boundaries for both the GitHub and Azure Functions engineering teams. Together, they worked through several challenges to achieve this level of throughput:

In the first test run, GitHub had so many messages pending for processing that it caused an integer overflow in the Azure Functions scaling logic, which was immediately fixed.

In the second run, throughput was severely limited due to a lack of connection pooling. The team rewrote the function code to correctly reuse connections from one execution to the next.

At about 800,000 events per second, the system appeared to be throttled at the network level, but the cause was unclear. After weeks of investigation, the Azure Functions team found a bug in the receive buffer configuration in the Azure SDK Advanced Message Queuing Protocol (AMQP) transport implementation. This was promptly fixed by the Azure SDK team and allowed GitHub to push beyond 1 million events per second.

Best practices in meeting a throughput milestone

With more power comes more responsibility, and Miehe acknowledges that Flex Consumption gave his team “a lot of knobs to turn,” as he put it. “There’s a balance between flexibility and the effort you have to put in to set it up right.”

To that end, he recommends testing early and often, a familiar part of the GitHub pull request culture. The following best practices helped GitHub meet its milestones:

Batch it if you can: Receiving messages in batches boosts performance. Processing thousands of event hub messages in a single function execution significantly improves the system throughput.

Experiment with batch size: Miehe’s team tested batches as large as 100,000 events and as small as 100 before landing on 5,000 as the max batch size for fastest execution.

Automate your pipelines: GitHub uses Terraform to build the function app and the Event Hubs instances. Provisioning both components together reduces the amount of manual intervention needed to manage the ingestion pipeline. Plus, Miehe’s team could iterate incredibly quickly in response to feedback from the product team.

The GitHub team continues to run the new platform in parallel with the legacy solution while it monitors performance and determines a cutover date. 

“We’ve been running them side by side deliberately to find where the ceiling is,” Miehe explains.

The team was delighted. As Miehe says, “We’re pleased with the results and will soon be sunsetting all the operational overhead of the old solution.“

Explore solutions with Azure Functions

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Elevate your AI deployments more efficiently with new deployment and cost management solutions for Azure OpenAI Service including self-service Provisioned

We’re excited to announce significant updates for Azure OpenAI Service, designed to help our 60,000 plus customers manage AI deployments more efficiently and cost-effectively beyond current pricing. With the introduction of self-service Provisioned deployments, we aim to help make your quota and deployment processes more agile, faster to market, and more economical. The technical value proposition remains unchanged—Provisioned deployments continue to be the best option for latency-sensitive and high-throughput applications. Today’s announcement includes self-service provisioning, visibility to service capacity and availability, and the introduction of Provisioned (PTU) hourly pricing and reservations to help with cost management and savings. 

Azure OpenAI Service deployment and cost management solutions walkthrough

What’s new? 

Self-Service Provisioning and Model Independent Quota Requests 

We are introducing self-service provisioning alongside standard tokens, allowing you to request Provisioned Throughput Units (PTUs) more flexibly and efficiently. This new feature empowers you to manage your Azure OpenAI Service quata deployments independently without relying on support from your account team. By decoupling quota requests from specific models, you can now allocate resources based on your immediate needs and adjust as your requirements evolve. This change simplifies the process and accelerates your ability to deploy and scale your applications. 

Visibility to service capacity and availability

Gain better visibility into service capacity and availability, helping you make informed decisions about your deployments. With this new feature, you can access real-time information about service capacity in different regions, ensuring that you can plan and manage your deployments more effectively. This transparency allows you to avoid potential capacity issues and optimize the distribution of your workloads across available resources, leading to improved performance and reliability for your applications. 

Provisioned hourly pricing and reservations 

We are excited to introduce two new self-service purchasing options for PTUs: 

Hourly no-commitment purchasing 

You can now create a Provisioned deployment for as little as an hour, with a flat hourly rate of $2 per unit per hour. This model-independent pricing makes it easy to deploy and tear down deployments as needed, offering maximum flexibility. This is ideal for testing scenarios or transitional periods without any long-term commitment. 

Monthly and yearly Azure reservations for Provisioned deployments

For production environments with steady request volumes, Azure OpenAI Service Provisioned Reservations offer significant cost savings. By committing to a monthly or yearly reservation, you can save up to 82% or 85%, respectively, over hourly rates. Reservations are now decoupled from specific models and deployments, providing unmatched flexibility. This approach allows enterprises to optimize costs while maintaining the ability to switch models and adjust deployments as needed. Read our technical blog on Reservations here.

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Benefits for decision makers 

These updates are designed to provide flexibility, cost efficiency, and ease of use, making it simpler for decision-makers to manage AI deployments. 

Flexibility: With self-service provisioning and hourly pricing, you can scale your deployments up or down based on immediate needs without long-term commitments. 

Cost efficiency: Azure Reservations offer substantial savings for long-term use, enabling better budget planning and cost management. 

Ease of use: Enhanced visibility and simplified provisioning processes reduce administrative burdens, allowing your team to focus on strategic initiatives rather than operational details. 

Customer success stories 

Before we made self-service available, select customers started achieving benefits of these options. 

Visier Solutions: By leveraging Provisioned Throughput Units (PTUs) with Azure OpenAI Service, Visier Solutions has significantly enhanced their AI-powered people analytics tool, Vee. With PTUs, Visier guarantees rapid, consistent response times, crucial for handling the high volume of queries from their extensive customer base. This powerful synergy between Visier’s innovative solutions and Azure’s robust infrastructure not only boosts customer satisfaction by delivering swift and accurate insights but also underscores Visier’s commitment to using cutting-edge technology to drive transformational change in workforce analytics. Read the case study on Microsoft. 

An analytics and insights company: Switched from Standard Deployments to GPT-4 Turbo PTUs and experienced a significant reduction in response times, from 10–20 seconds to just 2–3 seconds. 

A Chatbot Services company: Reported improved stability and lower latency with Azure PTUs, enhancing the performance of their services. 

A visual entertainment company: Noted a drastic latency improvement, from 12–13 seconds down to 2–3 seconds, enhancing user engagement. 

Empowering all customers to build with Azure OpenAI Service

These new updates do not alter the technical excellence of Provisioned deployments, which continue to deliver low and predictable latency. Instead, they introduce a more flexible and cost-effective procurement model, making Azure OpenAI Service more accessible than ever. With self-service Provisioned, model-independent units, and both hourly and reserved pricing options, the barriers to entry have been drastically lowered. 

To learn more about enhancing the reliability, security, and performance of your cloud and AI investments, explore the additional resources below.

Additional Resources 

Azure Pricing Provisioned Reservations

Azure OpenAI Service Pricing 

More details about Provisioned

Documentation for On-Boarding 

PTU Calculator in Azure AI Studio 

Unveiling Azure OpenAI Service Provisioned reservations blog

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Announcing mandatory multi-factor authentication for Azure sign-in

Learn how multifactor authentication (MFA) can protect your data and identity and get ready for Azure’s upcoming MFA requirement. 

As cyberattacks become increasingly frequent, sophisticated, and damaging, safeguarding your digital assets has never been more critical. As part of Microsoft’s $20 billion dollar investment in security over the next five years and our commitment to enhancing security in our services in 2024, we are introducing mandatory multifactor authentication (MFA) for all Azure sign-ins.

The need for enhanced security

One of the pillars of Microsoft’s Secure Future Initiative (SFI) is dedicated to protecting identities and secrets—we want to reduce the risk of unauthorized access by implementing and enforcing best-in-class standards across all identity and secrets infrastructure, and user and application authentication and authorization. As part of this important priority, we are taking the following actions:

Protect identity infrastructure signing and platform keys with rapid and automatic rotation with hardware storage and protection (for example, hardware security module (HSM) and confidential compute).

Strengthen identity standards and drive their adoption through use of standard SDKs across 100% of applications.

Ensure 100% of user accounts are protected with securely managed, phishing-resistant multifactor authentication.

Ensure 100% of applications are protected with system-managed credentials (for example, Managed Identity and Managed Certificates).

Ensure 100% of identity tokens are protected with stateful and durable validation.

Adopt more fine-grained partitioning of identity signing keys and platform keys.

Ensure identity and public key infrastructure (PKI) systems are ready for a post-quantum cryptography world.

Ensuring Azure accounts are protected with securely managed, phishing-resistant multifactor authentication is a key action we are taking. As recent research by Microsoft shows that multifactor authentication (MFA) can block more than 99.2% of account compromise attacks, making it one of the most effective security measures available, today’s announcement brings us all one step closer toward a more secure future.

In May 2024, we talked about implementing automatic enforcement of multifactor authentication by default across more than one million Microsoft Entra ID tenants within Microsoft, including tenants for development, testing, demos, and production. We are extending this best practice of enforcing MFA to our customers by making it required to access Azure. In doing so, we will not only reduce the risk of account compromise and data breach for our customers, but also help organizations comply with several security standards and regulations, such as Payment Card Industry Data Security Standard (PCI DSS), Health Insurance Portability and Accountability Act (HIPAA), General Data Protection Regulation (GDPR), and National Institute of Standards and Technology (NIST).

Preparing for mandatory Azure MFA

Required MFA for all Azure users will be rolled out in phases starting in the 2nd half of calendar year 2024 to provide our customers time to plan their implementation: 

Phase 1: Starting in October, MFA will be required to sign-in to Azure portal, Microsoft Entra admin center, and Intune admin center. The enforcement will gradually roll out to all tenants worldwide. This phase will not impact other Azure clients such as Azure Command Line Interface, Azure PowerShell, Azure mobile app and Infrastructure as Code (IaC) tools. 

Phase 2: Beginning in early 2025, gradual enforcement for MFA at sign-in for Azure CLI, Azure PowerShell, Azure mobile app, and Infrastructure as Code (IaC) tools will commence.

Beginning today, Microsoft will send a 60-day advance notice to all Entra global admins by email and through Azure Service Health Notifications to notify the start date of enforcement and actions required. Additional notifications will be sent through the Azure portal, Entra admin center, and the M365 message center.

For customers who need additional time to prepare for mandatory Azure MFA, Microsoft will review extended timeframes for customers with complex environments or technical barriers.

How to use Microsoft Entra for flexible MFA

Organizations have multiple ways to enable their users to utilize MFA through Microsoft Entra:

Microsoft Authenticator allows users to approve sign-ins from a mobile app using push notifications, biometrics, or one-time passcodes. Augment or replace passwords with two-step verification and boost the security of your accounts from your mobile device.

FIDO2 security keys provide access by signing in without a username or password using an external USB, near-field communication (NFC), or other external security key that supports Fast Identity Online (FIDO) standards in place of a password.

Certificate-based authentication enforces phishing-resistant MFA using personal identity verification (PIV) and common access card (CAC). Authenticate using X.509 certificates on smart cards or devices directly against Microsoft Entra ID for browser and application sign-in.

Passkeys allow for phishing-resistant authentication using Microsoft Authenticator.

Finally, and this is the least secure version of MFA, you can also use a SMS or voice approval as described in this documentation.

External multifactor authentication solutions and federated identity providers will continue to be supported and will meet the MFA requirement if they are configured to send an MFA claim.

Moving forward

At Microsoft, your security is our top priority. By enforcing MFA for Azure sign-ins, we aim to provide you with the best protection against cyber threats. We appreciate your cooperation and commitment to enhancing the security of your Azure resources.

Our goal is to deliver a low-friction experience for legitimate customers while ensuring robust security measures are in place. We encourage all customers to begin planning for compliance as soon as possible to avoid any business interruptions. 

Start today! For additional details on implementation, impacted accounts, and next steps for you, please refer to this documentation.
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Microsoft Cost Management updates—July 2024

Whether you’re a new student, a thriving startup, or the largest enterprise, you have financial constraints, and you need to know what you’re spending, where it’s being spent, and how to plan for the future. Nobody wants a surprise when it comes to the bill, and this is where Microsoft Cost Management comes in.

We’re always looking for ways to learn more about your challenges and how Microsoft Cost Management can help you better understand where you’re accruing costs in the cloud, identify and prevent bad spending patterns, and optimize costs to empower you to do more with less. Here are a few of the latest improvements and updates based on your feedback:

Exports enhancements: Parquet format support, file compression, and Fabric ingestion

Pricing updates on Azure.com

Your feedback matters: Take our quick survey! 

New ways to save money with Microsoft Cloud

Documentation updates

Let’s dig into the details.

Exports enhancements: Parquet format support, file compression, and Fabric ingestion 

In our last blog, I spoke about the support for FOCUS 1.0 (FinOps Cost Usage and Specification) datasets in Exports. We continue to make enhancements to the Exports functionality bringing support for the parquet format and file compression which can potentially help you achieve 40 to 70% file size reduction. These new cost saving features are initially available for the following datasets: Cost and usage details (Actual, Amortized, FOCUS) and Price Sheet. They aim to streamline your cost management processes, improve data handling efficiency, and reduce storage and network costs, all while providing comprehensive insights into your Azure spending.

Parquet is an open-source, columnar storage file format designed for efficient data processing and analytics. It offers several benefits over traditional formats like Comma-Separated Values (CSV), some of which are included below:

Efficient storage and reduced network cost: Parquet’s columnar format allows for better compression and encoding schemes, resulting in smaller file sizes. Compressed datasets occupy less space, translating to lower storage expenses and file transfer network cost.

Improved data transfer speed: Smaller file sizes mean faster data transfer rates, enhancing the efficiency of data operations.

Faster query performance: By storing data by column, parquet enables faster data retrieval and query performance, especially for large datasets.

Optimized analytics: Parquet format is optimized for big data tools and can be easily integrated with various analytics platforms.

To further reduce the size of your datasets, you can now compress your CSV files using GNU ZIP (GZIP) and parquet files using Snappy.

Here is the screenshot showing the new configuration options:

Please refer to this article to get started.

Microsoft Fabric ingestion 

Microsoft Fabric, as we know, is a great tool for data reporting and analytics where you can reference datasets from multiple sources without copying the data. We have now added new documentation to make it easy for you to ingest your exported costs datasets into new or existing Fabric workspaces. Just follow the steps included in this article. 

Pricing updates on Azure.com

We’ve been working hard to make some changes to our Azure pricing experiences, and we’re excited to share them with you. These changes will help make it easier for you to estimate the costs of your solutions.

We’re thrilled to announce the launch of new pricing pages for Azure AI Health (now generally available) and the innovative Phi-3 service (now in preview), ensuring you have the latest information at your fingertips.

Our Azure AI suite has seen significant enhancements, with updated calculators for Azure AI Vision and Azure AI Language, ensuring you have access to the most current offers and SKUs. The Azure AI Speech service now proudly offers generally available pricing for the cutting-edge Text to Speech add-on feature “Avatar”, and Azure AI Document Intelligence has added pricing for new training and custom generative stock-keeping units (SKUs).

To maintain the accuracy and relevance of our offers, we’ve deprecated the Azure HPC Cache and SQL Server Stretch pricing pages and calculators. This step ensures that you’re only presented with the most up-to-date and valid options.

The pricing calculator has been updated with the latest offers and SKUs for Azure Container Storage, Azure AI Vision, Azure Monitor, and PostgreSQL, reflecting our commitment to providing you with the most accurate cost estimates.

We’ve introduced new prices and SKUs across various services, including pricing for the new Intel Dv6/Ev6 series (preview) and ND Mi300X v5 series for Virtual Machines, auxiliary logs offer for Azure Monitor, and audio streaming and closed caption SKUs for Azure Communication Services. The Azure Databricks service now features pricing for Automated Serverless Compute, and the Azure Container Storage service pricing page now reflects generally available pricing.

Our dedication to enhancing your pricing experience is reflected in the continuous improvements made to several pages, including Azure Synapse Analytics, Azure SQL Database, Azure Migrate, Azure Cosmos DB (autoscale-provisioned), Microsoft Purview, Microsoft Fabric, Linux Virtual Machines, Azure VMware Solution, Azure Web PubSub, Azure Content Delivery Network, and Azure SignalR Service.

We’re constantly working to improve our pricing tools and make them more accessible and user-friendly. We hope you find these changes helpful in estimating the costs for your Azure Solutions. If you have any feedback or suggestions for future improvements, please let us know!

Your Feedback Matters: Take our quick survey!

If you use Azure in your day-to-day work from deploying resources to managing costs and billing, we would love to hear from you. (All experience levels welcome!) Please take a few moments to complete this short, 5 to 10-minute survey to help us understand your roles, responsibilities, and the challenges you face in managing the cloud. Your feedback will help us improve our services to better meet your personal needs. 

New ways to save money in the Microsoft Cloud

Here are new and updated offerings which can potentially help with your cost optimization needs:

Generally Available: Azure Virtual Network Manager mesh and direct connectivity

Generally Available: Announcing kube-egress-gateway for Kubernetes

Generally Available: Run your Databricks Jobs with Serverless compute for workflows

Generally Available: Azure Elastic SAN Feature Updates

Generally Available: Azure Virtual Network Manager mesh and direct connectivity

Public Preview: Summary rules in Azure Monitor Log Analytics, for optimal consumption experiences and cost

Public Preview: Continuous Performance Diagnostics for Windows VMs to enhance VM Troubleshooting

Public Preview: Azure cross-subscription Load Balancer

Public Preview: Advanced Network Observability for your Azure Kubernetes Service clusters through Azure Monitor

New Azure Advisor recommendations for Azure Database for PostgreSQL—Flexible Server

Want a more guided experience? Start with Control Azure spending and manage bills with Microsoft Cost Management.

Documentation updates 

Here are a few costs related documentation updates you might be interested in:

Update: Centrally managed Azure Hybrid Benefit FAQ

Update: Pay for your Azure subscription by wire transfer

Update: Tutorial: Create and manage budgets

Update: Understand cost details fields

Update: Quickstart: Start using Cost analysis

Update: Tutorial: Improved exports experience—Preview

Update: Transfer Azure Enterprise enrollment accounts and subscriptions

Update:  Migrate from Consumption Usage Details API

Update: Change contact information for an Azure billing account

New: Avoid unused subscriptions

Want to keep an eye on all documentation updates? Check out the Cost Management and Billing documentation change history in the azure-docs repository on GitHub. If you see something missing, select Edit at the top of the document and submit a quick pull request. You can also submit a GitHub issue. We welcome and appreciate all contributions!

What’s next?

These are just a few of the big updates from last month. Don’t forget to check out the previous Microsoft Cost Management updates. We’re always listening and making constant improvements based on your feedback, so please keep the feedback coming.

Follow the Microsoft Cost Management YouTube channel to stay in the loop with new videos as they’re released and let us know what you’d like to see next.
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New Azure Data Box capabilities to accelerate your offline data migration

Azure Data Box offline data transfer solution allows you send petabytes of data into Azure Storage in a quick, inexpensive, and reliable manner. The secure data transfer is accelerated by hardware transfer devices that enable offline data ingestion to Azure.

We’re excited to announce several new service capabilities including:

General availability of self-encrypted drives Azure Data Box Disk SKU that enables fast transfers on Linux systems.

Support for data ingestion to multiple blob access tiers in a single order.

Preview of cross-region data transfers for seamless data ingest from source country or region to select Azure destinations in a different country or region.

Support in Azure Storage Mover for online catch-up data copy of any changes active workloads may have generated post offline migrations with Azure Data Box.

Additionally, we’re happy to share the Azure Data Box cloud service is HIPAA/BAA, PCI 3DS and PCI DSS certified. More details on each of these new capabilities can be found below.

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Azure Data Box Disk: self-encrypted drives

Azure Data Box Disk is now generally available in a hardware-encrypted option in the European Union, United States, and Japan. These self-encrypting drives (SEDs) use the dedicated/native/specialized hardware for data encryption, without any software dependency from the host machine. These SEDs use the specialized native hardware present on the disk for data encryption, without any software dependencies on the host machine. With this offering, we now support comparable data transfer rates on Linux as that of our BitLocker-encrypted Data Box Disk drives on Windows.

Azure Data Box Disk SED is popular with some of our Automotive customers as it connects directly to the in-car Linux-based data loggers through a SATA interface, thereby eliminating the need for a secondary data copy from another in-car storage and saving time. Here is how Xylon, manufacturer of automotive data loggers uses Azure Data Box Disk: self encrypted drives to migrate Advanced driver-assistance systems (ADAS) sensor data to Azure: 

Through the cooperation with the Microsoft Azure team, we have enabled direct data logging to the hardware-encrypted Data Box Disks plugged into our logiRECORDER Automotive HIL Video Logger. It enables our common customers to transfer precious data from the test fleet to the cloud in the simplest and fastest possible way, without wasting time on unnecessary data copying and reformatting along the way.” 
—Jura Ivanovic, Product Director, Automotive HIL Video Logger, Xylon 

Learn more about Data Box Disk: self encrypted drives and get started migrating your on-premises data to Azure. 

Multi-access tier ingestion support

You can now transfer data to different blob access tiers including Cold Tier in a single Azure Data Box order. Previously, Azure Data Box only supported transferring data to the default access tiers of Azure Storage Accounts. For example, if you wanted to move data to the Cool tier in an Azure Storage Account that has the default set to hot, you would have had to first move the data to hot tier via Azure Data Box and then leverage life cycle management to move the data to the Cool tier after it’s uploaded to Azure. 

We have now introduced new “access tier” folders in the folder hierarchy on the device. All data that you copy to the “Cool” folder will have it’s access tier set as cool, irrespective of the default access tier of the destination Storage account, and similarly for data copied to other folders representing the various access tiers. Learn more about multi-access tier ingestion support. 

Cross-region data transfer to select Azure regions 

We’re excited to share that Azure Data Box cross-region data transfer capabilities, now in preview, supports seamless ingest of on-premises data from a source country or region to select Azure destinations in a different country or region. For example, with this capability you can now copy on-premises data from Singapore or India to the West United States Azure destination region. Note that the Azure Data Box device isn’t shipped across commerce boundaries. Instead, it’s transported from and to an Azure data Center within the originating country or region where the on-premises data resides. Data transfer to the destination Azure region takes place across the Azure network without incurring additional fees. 

Learn more about this capability and the supported country or region combinations for Azure Data Box, Azure Data Box Disk, and Azure Data Box Heavy respectively. 

Support for online catch-up copy with Azure Storage Mover Integration 

If your data source has any active workloads, it will likely make changes while your Azure Data Box is in transit to Azure. Consequently, you’ll also need to bring those changes to your cloud storage, before a workload can be cut over to it. We’re happy to announce that you can now combine the Azure Storage Mover and Data Box services to form an effective file and folder migration solution to minimize downtime for your workloads. Storage Mover jobs can detect differences between your on-site and cloud storage to effectively transfer any updates and new files not previously captured by your Data Box transfer. For example, if only a file’s metadata (such as permissions) has changed, Azure Storage Mover will upload only the new metadata instead of the entire file content. 

Learn more about how catch-up copies with Azure Storage Mover’s merge and mirror copy mode can help transfer only the delta data to Azure.

Certifications

The Azure Data Box cloud service has achieved HIPAA/BAA, PCI 3DS & PCI DSS certifications. These certifications have been key requests from many of our customers across the healthcare and financial sectors respectively, and we’re happy to have achieved the compliance status to enable our customers’ data transfer needs.

Additional product updates

Support for up to 4 TB Azure files across the product family. 

Support for data transfer to “Poland Central” and “Italy North” Azure regions. 

Transfers to Premium Azure Files and Blob Archive tiers now supported with Data Box Disk. 

The data copy service, which significantly improves the ingestion and upload time for small files, is now generally available.

Our goal is to continually enhance the simplicity of your offline data transfers, and your input is invaluable. Should you have any suggestions or feedback regarding Azure Data Box, feel free to reach out via email at DataBox@microsoft.com. We look forward to you reviewing your feedback and comments.
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Announcing a new OpenAI feature for developers on Azure 

We are thrilled to announce the launch of OpenAI’s latest model on Azure. This new model, officially named GPT-4o-2024-08-06, brings innovative features designed to elevate developer experiences on Azure. Specifically, the new model focuses on enhancing productivity through Structured Outputs, like JSON Schemas, for the new GPT-4o and GPT-4o mini models. 

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A focus on Structured Outputs 

GPT-4o was first announced in May 2024, as OpenAI’s new multimodal model, followed by GPT-4o mini in July 2024. Today’s version is designed with a specific use case in mind: simplifying the process of generating well-defined, structured outputs from AI models. This feature is particularly valuable for developers who need to validate and format AI outputs into structures like JSON Schemas. Developers often face challenges validating and formatting AI outputs into well-defined structures like JSON Schemas.  

Structured Outputs addresses this by allowing developers to specify the desired output format directly from the AI model. This feature enables developers to define a JSON Schema for text outputs, simplifying the process of generating data payloads that can seamlessly integrate with other systems or enhance user experiences. 

Use cases for JSON 

JSON Schema is essential for defining the structure and constraints of JSON documents, ensuring they follow specific formats with mandatory properties and value types. It enhances data understandability through semantic annotation and serves as a domain-specific language for optimized application requirements. Development teams use JSON Schema to maintain consistency across platforms, drive model-driven UI constraints, and automatically generate user interfaces. It aids in data serialization, security testing, and partial validation in technical scenarios. JSON Schema also supports automated testing, Schema inference, and machine-readable web profiles, improving data interoperability. It standardizes validation interfaces and reporting, handles external validation, and ensures data consistency within and across documents. It can also help with customer support and how to communicate in a timely manner. 

Two flavors of Structured Outputs 

Structured Outputs is available in two forms: 

User-defined JSON Schema: This option allows developers to specify the exact JSON Schema they want the AI to follow, supported by both GPT-4o-2024-08-06 and GPT-4o-mini-2024-07-18.

More Accurate Tool Output (“Strict Mode”): This limited version lets developers define specific function signatures for tool use, supported by all models that support function calling, including GPT-3.5 Turbo, GPT-4, GPT-4 Turbo, and GPT-4o models from June 2023 onwards. 

Technical guidance on using Structured Outputs 

To help you get started with Structured Outputs, we recommend the following approach. 

Getting started with Structured Outputs 

Define Your JSON Schema: Determine the structure you want your AI outputs to follow. This can include required fields, data types, and other constraints. 

Configure the AI model: Use the Structured Outputs feature to specify your JSON Schema within the API call. This ensures that the AI output adheres to your defined structure. 

Integration and testing: Integrate the output into your application or system, and test thoroughly to ensure compliance with your JSON Schema. 

Example use case: Customer support automation 

Imagine you’re developing a customer support chatbot that needs to generate responses in a specific format for logging and analytics. By using Structured Outputs, you can define a JSON Schema that includes fields like responseText, intent, confidenceScore, and timestamp. This ensures that every response generated by the chatbot is formatted correctly, making it easier to log, analyze, and act upon. 

Example API call 

Here’s an example API call to illustrate how to use Structured Outputs:

{
"model": "gpt-4o-2024-08-06",
"prompt": "Generate a customer support response",
"structured_output": {
"schema": {
"type": "object",
"properties": {
"responseText": { "type": "string" },
"intent": { "type": "string" },
"confidenceScore": { "type": "number" },
"timestamp": { "type": "string", "format": "date-time" }
},
"required": ["responseText", "intent", "confidenceScore", "timestamp"]
}
}
}

Pricing 

We will make pricing for this feature available soon. Please bookmark the Azure OpenAI Service pricing page. 

Learn more about the future of AI

We’ve been rolling out several new models recently, and we understand it can be a lot to keep up with. This flurry of activity is all about empowering developer innovation. Each new model brings unique capabilities and enhancements, helping you build even more powerful and versatile applications. 

The launch of this new model feature for GPT-4o and GPT-4o mini marks a significant milestone in our ongoing efforts to push the boundaries of AI capabilities. We’re excited to see how developers will leverage these new features to create innovative and impactful applications. 

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Craft AI solutions your way

Stay tuned for more updates and get ready to experience the future of AI with these new developer features for GPT-4o and mini. Start experimenting in the Azure OpenAI Playground. 

Explore Azure OpenAI Service

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Build AI-enabled applications with Azure AI and NVIDIA

Learn how Azure AI, combined with NVIDIA AI, can help you create innovative and intelligent solutions using your preferred tools and workflows.

An explosion of interest in generative AI across many industries has sparked, a direct result of the collaboration of Microsoft and NVIDIA and the breakthrough technology behind OpenAI’s ChatGPT. As a result, artificial intelligence (AI) is transforming the way we interact with digital products and services, from chatbots and voice assistants to smart cameras and recommendation systems. It’s now almost a daily demand to leverage the power of AI to create applications that can understand, interact, and learn from their users and environments.

However, building these AI applications presents significant challenges in terms of time, resources, access to AI infrastructure, and costs—which can be prohibitive for many developers and organizations.

To alleviate these challenges, developers can benefit from the combined benefits of Azure AI—a set of cloud-based AI and machine learning services that can help you build, train, and deploy AI-enabled applications with ease—and the NVIDIA AI platform to maximize application performance throughout every stage of development and deployment. To make access and entry even easier to build the best AI-enabled applications, Microsoft and NVIDIA have launched a collaborative resource for developers and organizations to experience the better together benefits.

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In this blog, we’ll discuss how combining the power of both Azure AI and the NVIDIA AI Platform can help you create your most impactful AI-enabled applications, providing you with flexibility, productivity, efficiency, and innovation.

Better together: Microsoft and NVIDIA

Recognizing the barriers developers face, NVIDIA and Microsoft have worked closely to democratize access to the same core technology that powers ChatGPT to accelerate adoption. The partnership focuses on optimizing every layer of the generative AI stack—from highly performant and scalable AI infrastructure to developer-friendly tools and services—to reduce complexity and cost, making advanced AI capabilities more accessible and feasible for a broader range of applications and industries.

Used by more than 60,000 organizations, Azure AI integrates with popular developer environments Visual Studio Code and GitHub, allowing you to use your preferred tools and workflows to develop, test, and deploy your AI solutions. Whether you want to use pre-built models and APIs, or build and train your own custom models, Azure AI can support various AI scenarios including building your own copilot with enterprise chat, speech analytics, document processing automation, and more.

Azure’s leading cloud AI supercomputing infrastructure, leveraging both state of the art NVIDIA GPUs and NVIDIA InfiniBand networking, provides the best performance, scalability, and built-in security needed to build, train, and deploy the most demanding AI workloads with confidence, at any scale. This combination accelerates time to solution, lowers deployment costs by supporting more users with fewer compute resources, and enhances user experience through optimized performance and faster data throughput.

Benefits for developers in GitHub and Visual Studio Code

Whether a developer that uses Visual Studio Code or GitHub, Azure AI integrates with your existing development environment, allowing you to use the same tools and workflows that you’re already familiar with.

Some of the benefits of using these AI tools and services for developers in GitHub and Visual Studio Code include:

Flexibility and choice: Choose the AI solution that best suits you, whether using pre-built models and APIs or building and training your own custom models. Choose the framework and language that you prefer, such as LangChain, Semantic Kernel, TensorFlow, PyTorch, Scikit-learn, Python, or R. You can even use the Azure OpenAI Service to access the latest GPT models from OpenAI. Additionally, folks can use the new Prompty format to work with prompts in their preferred environments (like Visual Studio Code and GitHub) all while using the trusted platform.

Productivity and efficiency: Simplify and accelerate the AI development process by using Visual Studio Code extensions and GitHub Actions. For example, use prompt flow to manage various versions of your flow assets like prompts, code, configurations, and environments via code repo, with tracked changes and rollback to previous versions, promoting a collaborative LLMOps ethos. For machine learning workloads, use GitHub Actions for Azure Machine Learning to automate model training, testing, and deployment.

Performance and scalability: Harness NVIDIA-optimized software from NVIDIA AI Enterprise, available in the Azure marketplace, to streamline workflows and embrace powerful AI capabilities. With support for remote development using Visual Studio Code extensions, you can write, debug, and optimize GPU-accelerated applications—including AI models—while using NVIDIA GPU-powered Azure Virtual Machines.

Innovation and creativity: Build applications that understand, interact, and learn from their users and environments, and that deliver personalized and engaging experiences. Use Azure AI to build a comprehensive generative AI stack and enrich your applications with retrieval-augmented generation, natural language processing, machine learning, and more.

Start building your most innovative applications

The strategic partnership between Microsoft and NVIDIA has significantly enhanced the Azure AI ecosystem. The integration of the NVIDIA AI Enterprise software platform combined with Azure’s AI toolsets and libraries ensures a robust and efficient environment for advancing your AI projects. Accelerate your time to deployment with the optimized NVIDIA Nemotron models, NVIDIA NIM inference microservices, Langchain and Hugging Face integrations, and APIs, inside of your Azure AI environment.

By building AI-enabled applications within the Microsoft ecosystem, developers can benefit from the productivity and efficiency gains that come from using a single, integrated set of tools and services. This can help reduce development time, support costs, and enhance collaboration and communication among team members. You can also benefit from the innovation and creativity that Azure AI enables, allowing you to create applications that understand, interact, and learn from users and environments, and deliver personalized and engaging experiences.

Learn more about how you can streamline development and build AI-enabled applications faster and easier with the combined power of Microsoft and NVIDIA.
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Accelerating AI app development with Azure AI and GitHub

Microsoft is empowering developers to become AI developers, bringing Azure AI industry leading models to the global GitHub community of more than 100 million

More than 60,000 organizations use Microsoft Azure AI today to explore the power of custom AI applications. However, the market is quickly moving from experimentation to scale, and we see more developers around the world becoming AI developers. With this shift, the needs of developers and their requirements to access and build with AI models and tools are evolving.

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The latest models with unique capabilities, performance metrics, and cost efficiencies for developers.

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To support this shift to scale, we are excited to partner with GitHub to empower their more than 100 million developers to build AI applications directly from Github.com with seamless integrations with Codespaces and Microsoft Visual Studio Code. Our collaboration starts today as we bring Azure AI’s leading model selection to developers through GitHub Models, along with simple APIs to empower responsible, production-ready AI applications.

For more insights into how GitHub Models can help you increase experimentation and accelerate your development cycles, all in GitHub, please read the blog from GitHub CEO Thomas Dohmke.

Simplifying AI development 

As AI model innovation accelerates, Azure remains committed to delivering the leading model selection and greatest model diversity to meet the unique cost, latency, design, and safety needs of AI developers. Today, we offer the largest and most complete model library in the market, including the latest models from OpenAI, Meta, Mistral and Cohere, introduced in July of this year, and updates to our own Phi-3 family of small language models. With GitHub Models, developers can now explore and utilize the latest models along with AI innovations and next-generation frontier models. This offering gives every developer the flexibility to choose the best combination of unique capabilities, performance metrics, and cost efficiencies.

While continuous model innovation brings more choice, it also brings complexity when selecting the right model for the right scenario. Today, developers have a range of options for cloud vs. edge, general-purpose vs. task specific, and more. On top of that, organizations often need multiple models to enable better quality, lower cost of goods sold, and to address complex use cases for each industry. GitHub Models opens the door for developers to experiment with multiple models, simplifying model experimentation and selection across the best of the Azure AI catalog, quickly comparing models, parameters, and prompts.

By making Azure AI an open, modular platform, we aim to help our customers rapidly go from idea to code to cloud. With Azure AI on GitHub, developers can do just that by utilizing Codespaces to set up a prototype or use the Prompty extension to generate code with GitHub Models directly in Microsoft Visual Studio Code.

In the coming months, we will expand our integration even further, bringing Azure AI’s language, vision, and multi-modal services to GitHub, along with additional Azure AI toolchain elements, further streamlining the AI application development process.

Integrating safety by default 

Developers building with AI want to be confident their AI applications are trustworthy, safe, and secure. GitHub Models gives developers a strong foundation from the start with built-in safety and security controls from Azure AI.

Azure AI works with model providers and other partners such as HiddenLayer to reduce emerging threats, from cybersecurity vulnerabilities, to malware, and other signs of tampering. And we have taken this further in GitHub Models by integrating Azure AI Content Safety for top foundation models including Azure OpenAI Service, Llama, and Mistral. Azure AI Content Safety enables built-in, real time protection for risks such as the generation of harmful content, copyright materials, hallucination, and new AI specific attacks such as jailbreaks and prompt injection attacks.

If developers want to go deeper, they can customize these controls in Azure AI, using evaluations to test and monitor their applications for ongoing quality and safety.

AI simplicity with a single API

Increased model selection gives developers the broadest range of options for the individual applications they are building. But each model naturally brings with it increased complexity. To counteract this, we’re making it incredibly easy for every developer to experiment with a range of models through the Azure AI model inference API. Using this single API, GitHub developers can now access a common set of capabilities to compare performance across a diverse set of foundational models in a uniform and consistent way, easily switching between models to compare performance without changing the underlying code.

The Azure AI Inference SDK provides client libraries in Python and JavaScript with support for C# and .NET coming soon. This SDK makes it easy to integrate AI into your applications by simplifying common tasks related to authentication, security and retries in your programming language of choice. You can get started today with Python and JavaScript samples.

Streamlining GitHub Enterprise access through Microsoft Azure 

Beyond these new integrations, we are also making it easier than ever for organizations to access GitHub Enterprise through Azure, combining GitHub’s cloud-native platform with Azure’s robust enterprise-grade security and scalability.

Organizations with an existing Azure subscription can purchase GitHub products via self-service, directly through Microsoft Sales or via Microsoft Cloud Solution Providers and can adjust the number of GitHub seats as needed to ensure efficient usage. Additionally, eligible organizations may take advantage of the Microsoft Azure Consumption Commitment (MACC) and Azure Commitment Discount (ACD). 

Companies can now spin-up a GitHub instance directly from the Azure Portal and connect their Microsoft Entra ID with GitHub to facilitate user management and access control. With an Azure subscription, you have all the necessary tools for creating an intelligent AI application, including access to GitHub’s complete range of services like repositories, Actions, Advanced Security, and Copilot. This makes it incredibly simple and efficient to give developers everything they need to build and deploy AI applications at scale.

We invite you to experience the power of this integrated end-to-end development experience. New customers can explore these capabilities with a free 30-day trial of GitHub Enterprise. 

We can’t wait to see what you will build with GitHub and Azure. 

Learn more about GitHub Models Launch. 

Explore and experiment with Azure AI models in GitHub. 

Get deeper technical details on GitHub Models. 

Use GitHub Codespaces to setup a prototype from your repo. 

Learn more about the free 30-day trial of GitHub Enterprise. 

Activate your GitHub free trial today

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Embrace the future of container native storage with Azure Container Storage

We are thrilled to announce the general availability of Microsoft Azure Container Storage, the industry’s first platform-managed container native storage service in the public cloud. With Kubernetes driving cloud evolution, we are witnessing a transformative shift as enterprises move from virtual machines (VMs) to containers, optimizing for scalability, flexibility, and cost efficiency. We introduce Azure Container Storage to meet these demands, providing best in class price performance for hosting stateful containers on cloud-based storage and delivering lowest latency on locally attached storage.

Azure Container Storage joins our suite of container services, tightly integrating with Kubernetes and simplifying stateful workload management across Azure’s set of comprehensive storage offerings. Previously, customers needed to retrofit stateful workloads to VM-centric storage options with scalability constraints or deploy self-managed open-source container storage solutions. Since Azure Container Storage is built purposefully for Azure Kubernetes Service (AKS), it simplifies the process, allowing developers to focus on innovating and running applications without worrying about managing storage. With the ability to perform all storage operations directly through Kubernetes APIs—such as creating persistent volumes and scaling up capacity on demand, it eliminates the need to interact with control plane APIs of the underlying infrastructure.

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Azure Container Storage also streamlines storage management across multiple backing storage options. With its general availability, Azure Container Storage supports Ephemeral Disks (local NVMe and temp SSD) and Azure Disks, just the start in our journey to transform the container storage landscape. Ephemeral Disks support marks a pivotal moment for container users, providing the most comprehensive volume management support for containers on local storage in the cloud. Beyond basic persistent volume (PV) provisioning, Azure Container Storage offers built-in capabilities such as snapshots and autoscaling, capabilities that cannot be found outside of Azure.  

During preview, customers have already begun taking advantage of Azure Container Storage to evolve their business-critical, next-generation solutions. Whether it be optimizing Redpanda cluster performance on Ephemeral Disks or scaling past existing persistent volume limits for Postgres workloads on Azure Disks, Azure Container Storage supports a wide range of workloads. For building stateful applications operating containers, this is just the beginning. Shortly after general availability, we will expand our offerings to include Elastic SAN and later, options like Azure Blobs and Azure Files for shared storage use cases.  

A cloud native solution for all use cases

Azure Container Storage ensures essential resiliency and security for every workload through built-in resiliency design and security enforcements. 

Built-in resiliency: Easily run highly available stateful applications on Azure Container Storage and protect against zonal failures on all levels of the resource hierarchy. You can choose between zone-redundant storage (ZRS) options or multi-zone storage pools on local-redundant storage (LRS) to deliver a highly available solution across zones. For local storage, we optimally place a pod’s persistent volumes on ephemeral disks that exist on the same node as the AKS pod, reducing the number of failure points that could impact your application’s runtime. Moreover, we offer the best balance for availability, cost, and performance—providing the most cost-efficient block storage offering on the cloud with multi-zonal high availability support and sub millisecond read latency. 

Security by default: Security is our top priority. We offer server-side encryption (SSE) with platform-managed keys by default and enforce network security per respective backing storage options. Customers can further enhance security through extensive options, such as SSE with customer-managed keys, per their security standards.

Modernizing existing applications

For any enterprise looking to modernize its applications, Azure Container Storage consolidates management across familiar block storage offerings, simplifies the movement of workloads, and provides continuity in backup and disaster recovery.  

We streamline and consolidate the management experience across our comprehensive portfolio of familiar Azure block storage offerings. Rather than needing to certify and manage multiple container orchestration solutions for each storage resource you deploy, Azure Container Storage efficiently coordinates volume provisioning within a storage pool, a concept we introduce to group storage resources into a unified resource for your AKS cluster. This storage pool can be backed by your preferred storage option, empowering you to choose the most cost-efficient resource tailored to your specific workload performance requirements. For example, Ephemeral Disk, newly introduced as a supported block storage offering for containers, is well-suited for latency-sensitive workloads that benefit from local NVMe or temp SSD storage. KPN, a Dutch telecommunications company, shared their positive experience using Azure Container Storage with local NVMe to host a mail solution on AKS: 

“With Azure Container Storage, we have been able to achieve improved performance in our KPN consumer mail workload by leveraging ephemeral disks and taking advantage of the pooling of resources that Azure Container Storage enables. Instead of the manual configuration of storage, we can focus on running our workloads, and Azure Container Storage will take care of auto-discovering and formatting the NVMe disks, making it simple to use and in line with the Kubernetes way.” 
—Peter Teeninga, Cloud Architect, Personal Cloud by KPN

To make your journey to the cloud as painless as possible we partnered with CloudCasa, a key player in Kubernetes data mobility, to simplify mission-critical migration to the cloud. To continue supporting your cloud estate, we partnered with Kasten, the leading service for data protection for Kubernetes, offering robust backup and disaster recovery capabilities. For more details on our data migration and backup experience provided through our partners, please refer to the later section. 

Building cloud native applications

For application developers building solutions in the cloud, Azure Container Storage offers seamless integration with Kubernetes, providing a container-native experience designed for scalability from the ground up. This ensures that your applications can grow easily and cost-efficiently over time. By supporting industry-standard protocols, such as NVMe-of and iSCSI, we simplify interoperability, providing additional performance options. For instance, you can take advantage of the lower persistent volume attach and detach latencies these protocols offer and achieve rapid scale-out and fast failover. Azure Container Storage allows customers to attach more storage resources to a single VM, increasing the limit to 75 volumes for any VM sizes. The added flexibility increases customers’ ability to optimize Azure resources to meet their cost and performance goals. Sesam, a Norwegian data synchronization and management company, has effectively leveraged this capability to reduce costs by scaling up their persistent volumes more efficiently: 

“Azure Container Storage (ACS) has enabled us to achieve lower total cost of ownership in our workload. We have a large number of pods that need their own persistent volumes, and through the use of Azure Disks and storage pool resource pooling, we are able to fulfill this in a more cost-effective manner, without hitting the limits on the number of disks that can be attached to a node. In practice this makes us able to allocate capacity more easily and more efficiently.”
—Geir Ove Grønmo, Product Manager, Sesam.io 

Data migration and backup support through our trusted partners

Highly efficient and operational storage management is the baseline experience Azure Container Storage strives for. Azure Container Storage tightly integrates with two key third-party solutions—CloudCasa and Kasten—to offer you an integrated migrate, backup, and disaster recovery experience for workloads hosted on stateful containers.  

With the ability to automatically recreate an entire cluster, CloudCasa centralizes the management of cluster recovery and migration, making it easy to move your existing Kubernetes workloads to and within AKS. To modernize your existing workloads on Azure, simply do a full backup of the existing storage resources then set up a restore, indicating Azure Container Storage as the new storage resource for your cluster. 

“With Azure Container Storage, Microsoft has removed much of the management burden from Kubernetes storage, allowing development and DevOps teams to focus on their data and applications. This approach enables organizations to more easily operate stateful production applications at scale. We are pleased to have worked with the Azure Container Storage team to certify CloudCasa for backup and recovery of stateful applications running on it, and to provide a jointly tested solution for easy migration to it.”
—Bob Adair, Head of Product Management, CloudCasa By Catalogic

Kasten automates the end-to-end workflow of backup and disaster recovery, protecting your Kubernetes clusters and application operations. When you deploy your storage pool in Azure Container Storage, you can enable Kasten during snapshot setup. Using dynamic policies, Kasten helps you manage backups at scale in a crash-consistent manner. 

“With Azure Container Storage and Kasten by Veeam, organizations can maximize performance, flexibility and resiliency, while protecting their cloud native workloads from ransomware attacks. Kasten by Veeam collaborated with Microsoft Product and Engineering teams to validate provisioning, volume snapshot and restore capabilities on Azure Container Storage to ensure joint Microsoft and Kasten by Veeam customers can backup, protect, and migrate their stateful workloads to Azure Kubernetes Service (AKS). Through our strategic partnership, we simplify organizations’ cloud journeys without sacrificing performance, scalability or resiliency.” 
—Matt Slotten, Principal Solution Architect, Cloud Native Partnerships Kasten by Veeam  

What is new with general availability?

Our announcement builds on the updates we’ve shared throughout our preview, highlighting several differentiated capabilities. We’ve enhanced the resiliency of stateful containers with multi-zone storage pools and volume replication for local NVMe storage pools to protect against availability loss during single node failures. We’ve also added snapshot support across all storage options for backup and disaster recovery. Additionally, we’ve expanded the Ephemeral Disk portfolio from local NVMe to include temp SSD support, enhancing cost efficiency for use cases that can leverage directly attached local storage. With this announcement, we are excited to introduce three new capabilities that will further increase the resiliency and performance of running stateful workloads: 

Enhance the resiliency of your persistent volumes hosted on local NVMe storage (L-series ephemeral disks) with replication support.

Improved persistent volume recovery after a restart of an Azure Kubernetes Service (AKS) cluster.

Customize the performance of your local NVMe storage with new performance tier options. 

Learn more about Azure Container Storage

Get started with installing Azure Container Storage to your AKS cluster! For a comprehensive guide, watch our step-by-step walkthrough video. You can also explore workload samples from our newly launched community repository to create your first stateful application. To learn more, refer to our AKS engineering blog. We encourage everyone to contribute and share your insights as you explore our newest storage offering.  

If you have any questions, please reach out to AskContainerStorage@microsoft.com. Embrace the future of stateful containers with Azure and unlock new possibilities! 

Explore Azure Container Storage capabilities

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OpenAI’s fastest model, GPT-4o mini is now available on Azure AI

We are also announcing safety features by default for GPT-4o mini, expanded data residency and service availability, plus performance upgrades to Microsoft Azure OpenAI Service.

GPT-4o mini allows customers to deliver stunning applications at a lower cost with blazing speed. GPT-4o mini is significantly smarter than GPT-3.5 Turbo—scoring 82% on Measuring Massive Multitask Language Understanding (MMLU) compared to 70%—and is more than 60% cheaper.1 The model delivers an expanded 128K context window and integrates the improved multilingual capabilities of GPT-4o, bringing greater quality to languages from around the world.

GPT-4o mini, announced by OpenAI today, is available simultaneously on Azure AI, supporting text processing capabilities with excellent speed and with image, audio, and video coming later. Try it at no cost in the Azure OpenAI Studio Playground.

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We’re most excited about the new customer experiences that can be enhanced with GPT-4o mini, particularly streaming scenarios such as assistants, code interpreter, and retrieval which will benefit from this model’s capabilities. For instance, we observed the incredible speed while testing GPT-4o mini on GitHub Copilot, an AI pair programmer that assists you by delivering code completion suggestions in the tiny pauses between keystrokes, rapidly updating recommendations with each new character typed.

We are also announcing updates to Azure OpenAI Service, including extending safety by default for GPT-4o mini, expanded data residency, and worldwide pay-as-you-go availability, plus performance upgrades. 

Azure AI brings safety by default to GPT-4o mini

Safety continues to be paramount to the productive use and trust that we and our customers expect.

We’re pleased to confirm that our Azure AI Content Safety features—including prompt shields and protected material detection— are now ‘on by default’ for you to use with GPT-4o mini on Azure OpenAI Service.

We have invested in improving the throughput and speed of the Azure AI Content Safety capabilities—including the introduction of an asynchronous filter—so you can maximize the advancements in model speed while not compromising safety. Azure AI Content Safety is already supporting developers across industries to safeguard their generative AI applications, including game development (Unity), tax filing (H&R Block), and education (South Australia Department for Education).

In addition, our Customer Copyright Commitment will apply to GPT-4o mini, giving peace of mind that Microsoft will defend customers against third-party intellectual property claims for output content.

Azure AI now offers data residency for all 27 regions

From day one, Azure OpenAI Service has been covered by Azure’s data residency commitments.

Azure AI gives customers both flexibility and control over where their data is stored and where their data is processed, offering a complete data residency solution that helps customers meet their unique compliance requirements. We also provide choice over the hosting structure that meets business, application, and compliance requirements. Regional pay-as-you-go and Provisioned Throughput Units (PTUs) offer control over both data processing and data storage.

We’re excited to share that Azure OpenAI Service is now available in 27 regions including Spain, which launched earlier this month as our ninth region in Europe.

Azure AI announces global pay-as-you-go with the highest throughput limits for GPT-4o mini

GPT-4o mini is now available using our global pay-as-you-go deployment at 15 cents per million input tokens and 60 cents per million output tokens, which is significantly cheaper than previous frontier models.

We are pleased to announce that the global pay-as-you-go deployment option is generally available this month, allowing customers to pay for the resources they consume, making it flexible for variable workloads, while traffic is routed globally to provide higher throughput, and still offering control over where data resides at rest.

Additionally, we recognize that one of the challenges customers face with new models is not being able to upgrade between model versions in the same region as their existing deployments. Now, with global pay-as-you-go deployments, customers will be able to upgrade from existing models to the latest models.

Global pay-as-you-go offers customers the highest possible scale, offering 15M tokens per minute (TPM) throughput for GPT-4o mini and 30M TPM throughput for GPT-4o. Azure OpenAI Service offers GPT-4o mini with 99.99% availability and the same industry leading speed as our partner OpenAI.

Azure AI offers leading performance and flexibility for GPT-4o mini

Azure AI is continuing to invest in driving efficiencies for AI workloads across Azure OpenAI Service.

GPT-4o mini comes to Azure AI with availability on our Batch service this month. Batch delivers high throughput jobs with a 24-hour turnaround at a 50% discount rate by using off-peak capacity. This is only possible because Microsoft runs on Azure AI, which allows us to make off-peak capacity available to customers.

We are also releasing fine-tuning for GPT-4o mini this month which allows customers to further customize the model for your specific use case and scenario to deliver exceptional value and quality at unprecedented speeds. Following our update last month to switch to token based billing for training, we’ve reduced the hosting charges by up to 43%. Paired with our low price for inferencing, this makes Azure OpenAI Service fine-tuned deployments the most cost-effective offering for customers with production workloads.

With more than 53,000 customers turning to Azure AI to deliver breakthrough experiences at impressive scale, we’re excited to see the innovation from companies like Vodafone (customer agent solution), the University of Sydney (AI assistants), and GigXR (AI virtual patients). More than 50% of the Fortune 500 are building their applications with Azure OpenAI Service.

We can’t wait to see what our customers do with GPT-4o mini on Azure AI!

1GPT-4o mini: advancing cost-efficient intelligence | OpenAI
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