Unlocking Client-Side Configuration at Scale with Azure App Configuration and Azure Front Door
As modern apps shift more logic to the browser, Azure App Configuration now brings dynamic configuration directly to client-side applications. Through its integration with Azure Front Door, developers can deliver configuration to thousands or millions of clients with CDN-scale performance while avoiding the need to expose secrets or maintain custom proxy layers. This capability is especially important for AI-powered and agentic client applications, where model settings and behaviors often need to adjust rapidly and safely. This post introduces the new capability, what it unlocks for developers, and how to start building dynamic, configuration-driven client experiences in Azure. App Configuration for Client Applications Centralized Settings and Feature Management App Configuration gives developers a single, consistent place to define configuration settings and feature flags. Until now, this capability was used almost exclusively by server-side applications. With Azure Front Door integration, these same settings can now power modern client experiences across: Single Page Applications (React, Vue, Angular, Next.js, and others using JavaScript) Mobile/ and desktop applications with .Net MAUI JavaScript-powered UI components or embedded widgets running in browser Any browser-based application that can run JavaScript This allows developers to update configuration without redeploying the client app. CDN-Accelerated Configuration Delivery with Azure Front Door Azure Front Door enables client applications to fetch configuration using a fast, globally distributed CDN path. Developers benefit from: High-scale configuration delivery to large client populations Edge caching for fast, low-latency configuration retrieval Reduced load on your backend configuration store through CDN offloading Dedicated endpoint that exposes only the configuration subset it is scoped for. Secure and Scalable Architecture App Configuration integrates with Azure Front Door to deliver configuration to client-side apps using a simple, secure, and CDN-accelerated flow. How it works The browser calls Azure Front Door anonymously, like any CDN asset. Front Door uses managed identity to access App Configuration securely. Only selected key-values, feature flags or snapshots are exposed through Azure Front Door. No secrets or credentials are shipped to the client. Edge caching enables high throughput and low latency configuration delivery. This provides a secure and efficient design for client applications and eliminates the need for custom gateway code or proxy services. Developer Scenarios: What You Can Build CDN-delivered configuration unlocks a range of rich client application scenarios: Client-side feature rollouts for UI components A/B testing or targeted experiences using feature flags Control AI/LLM model parameters and UI behaviors through configuration Dynamically control client-side agent behavior, safety modes, and guardrail settings through configuration Consistent behavior for clients using snapshot-based configuration references These scenarios previously required custom proxies. Now, they work out-of-the-box with Azure App Configuration + Azure Front Door. End-to-End Developer Journey The workflow for enabling client-side configuration with App Configuration is simple: Define key values or feature flags in Azure App Configuration Connect App Configuration to Azure Front Door in the portal Scope configuration exposed by Front Door endpoint with key value or snapshot filter. Use the updated AppConfig JavaScript or .NET provider to connect to Front Door anonymously. Client app fetches configuration via Front Door with CDN performance Update your configuration centrally, no redeployment required To see this workflow end-to-end, check out this demo video. The video shows how to connect an App Configuration store to Azure Front Door and use the Front Door endpoint in a client application. It also demonstrates dynamic feature flag refresh as updates are made in the store. Portal Experience to connect Front Door Once you create your App Configuration store with key values and/or feature flags, you can configure the Front Door connection directly in the Azure portal. The App Configuration portal guides you through connecting a profile, creating an endpoint, and scoping which keys, labels, or snapshots will be exposed to client applications. A detailed “How-To” guide is available in the App Configuration documentation. Using the Front Door Endpoint in Client Applications JavaScript Provider Minimum version for this feature is 2.3.0-preview, get the provider from here. Add below snippet in your code to fetch the key values and/or feature flags from App Configuration through front door. import { loadFromAzureFrontDoor } from "@azure/app-configuration-provider"; const appConfig = await loadFromAzureFrontDoor("https://<your-afd-endpoint>", { featureFlagOptions: { enabled: true }, }); const yoursetting = appConfig.get("<app.yoursetting>"); .NET Provider Minimum version supporting this feature is 8.5.0-preview, get the provider from here builder.Configuration.AddAzureAppConfiguration(options => { options.ConnectAzureFrontDoor(new Uri("https://<your-afd-endpoint>")) .UseFeatureFlags(featureFlagOptions => { featureFlagOptions.Select("<yourappprefix>"); }); }); See our GitHub samples for JavaScript and .NET MAUI for complete client application setups. Notes & Limitations Feature flag scoping requires two key prefix filters, startsWith(".appconfig.featureflag") and ALL keys. Portal Telemetry feature does not reflect client-side consumption yet. This feature is in preview, and currently not supported in Azure sovereign clouds. Conclusion By combining Azure App Configuration with Azure Front Door, developers can now power a new generation of dynamic client applications. Configuration is delivered at CDN speed, securely and at scale letting you update experiences instantly, without redeployment or secret management on client side. This integration brings App Configuration’s flexibility directly to the browser, making it easier to power AI-driven interfaces, agentic workflows, and dynamic user experiences. Try client-side configuration with App Configuration today and update your apps’ behavior in real time, without any redeployments.Search Less, Build More: Inner Sourcing with GitHub CoPilot and ADO MCP Server
Developers burn cycles context‑switching: opening five repos to find a logging example, searching a wiki for a data masking rule, scrolling chat history for the latest pipeline pattern. Organisations that I speak to are often on the path of transformational platform engineering projects but always have the fear or doubt of "what if my engineers don't use these resources". While projects like Backstage still play a pivotal role in inner sourcing and discoverability I also empathise with developers who would argue "How would I even know in the first place, which modules have or haven't been created for reuse". In this blog we explore how we can ensure organisational standards and developer satisfaction without any heavy lifting on either side, no custom model training, no rewriting or relocating of repositories and no stagnant local data. Using GitHub CoPilot + Azure DevOps MCP server (with the free `code_search` extension) we turn the IDE into an organizational knowledge interface. Instead of guessing or re‑implementing, engineers can start scaffolding projects or solving issues as they would normally (hopefully using CoPilot) and without extra prompting. GitHub CoPilot can lean into organisational standards and ensure recommendations are made with code snippets directly generated from existing examples. What Is the Azure DevOps MCP Server + code_search Extension? MCP (Model Context Protocol) is an open standard that lets agents (like GitHub Copilot) pull in structured, on-demand context from external systems. MCP servers contain natural language explanations of the tools that the agent can utilise allowing dynamic decision making of when to implement certain toolsets over others. The Azure DevOps MCP Server is the ADO Product Team's implementation of that standard. It exposes your ADO environment in a way CoPilot can consume. Out of the box it gives you access to: Projects – list and navigate across projects in your organization. Repositories – browse repos, branches, and files. Work items – surface user stories, bugs, or acceptance criteria. Wiki's – pull policies, standards, and documentation. This means CoPilot can ground its answers in live ADO content, instead of hallucinating or relying only on what’s in the current editor window. The ADO server runs locally from your own machine to ensure that all sensitive project information remains within your secure network boundary. This also means that existing permissions on ADO objects such as Projects or Repositories are respected. Wiki search tooling available out of the box with ADO MCP server is very useful however if I am honest I have seen these wiki's go unused with documentation being stored elsewhere either inside the repository or in a project management tool. This means any tool that needs to implement code requires the ability to accurately search the code stored in the repositories themself. That is where the code_search extension enablement in ADO is so important. Most organisations have this enabled already however it is worth noting that this pre-requisite is the real unlock of cross-repo search. This allows for CoPilot to: Query for symbols, snippets, or keywords across all repos. Retrieve usage examples from code, not just docs. Locate standards (like logging wrappers or retry policies) wherever they live. Back every recommendation with specific source lines. In short: MCP connects CoPilot to Azure DevOps. code_search makes that connection powerful by turning it into a discovery engine. What is the relevance of CoPilot Instructions? One of the less obvious but most powerful features of GitHub CoPilot is its ability to follow instructions files. CoPilot automatically looks for these files and uses them as a “playbook” for how it should behave. There are different types of instructions you can provide: Organisational instructions – apply across your entire workspace, regardless of which repo you’re in. Repo-specific instructions – scoped to a particular repository, useful when one project has unique standards or patterns. Personal instructions – smaller overrides layered on top of global rules when a local exception applies. (Stored in .github/copilot-instructions.md) In this solution, I’m using a single personal instructions file. It tells CoPilot: When to search (e.g., always query repos and wikis before answering a standards question). Where to look (Azure DevOps repos, wikis, and with code_search, the code itself). How to answer (responses must cite the repo/file/line or wiki page; if no source is found, say so). How to resolve conflicts (prefer dated wiki entries over older README fragments). As a small example, a section of a CoPilot instruction file could look like this: # GitHub Copilot Instructions for Azure DevOps MCP Integration This project uses Azure DevOps with MCP server integration to provide organizational context awareness. Always check to see if the Azure DevOps MCP server has a tool relevant to the user's request. ## Core Principles ### 1. Azure DevOps Integration - **Always prioritize Azure DevOps MCP tools** when users ask about: - Work items, stories, bugs, tasks - Pull requests and code reviews - Build pipelines and deployments - Repository operations and branch management - Wiki pages and documentation - Test plans and test cases - Project and team information ### 2. Organizational Context Awareness - Before suggesting solutions, **check existing organizational patterns** by: - Searching code across repositories for similar implementations - Referencing established coding standards and frameworks - Looking for existing shared libraries and utilities - Checking architectural decision records (ADRs) in wikis ### 3. Cross-Repository Intelligence - When providing code suggestions: - **Search for existing patterns** in other repositories first - **Reference shared libraries** and common utilities - **Maintain consistency** with organizational standards - **Suggest reusable components** when appropriate ## Tool Usage Guidelines ### Work Items and Project Management When users mention bugs, features, tasks, or project planning: ``` ✅ Use: wit_my_work_items, wit_create_work_item, wit_update_work_item ✅ Use: wit_list_backlogs, wit_get_work_items_for_iteration ✅ Use: work_list_team_iterations, core_list_projects The result... To test this I created 3 ADO Projects each with between 1-2 repositories. The repositories were light with only ReadMe's inside containing descriptions of the "repo" and some code snippets examples for usage. I have then created a brand-new workspace with no context apart from a CoPilot instructions document (which could be part of a repo scaffold or organisation wide) which tells CoPilot to search code and the wikis across all ADO projects in my demo environment. It returns guidance and standards from all available repo's and starts to use it to formulate its response. In the screenshot I have highlighted some key parts with red boxes. The first being a section of the readme that CoPilot has identified in its response, that part also highlighted within CoPilot chat response. I have highlighted the rather generic prompt I used to get this response at the bottom of that window too. Above I have highlighted CoPilot using the MCP server tooling searching through projects, repo's and code. Finally the largest box highlights the instructions given to CoPilot on how to search and how easily these could be optimised or changed depending on the requirements and organisational coding standards. How did I implement this? Implementation is actually incredibly simple. As mentioned I created multiple projects and repositories within my ADO Organisation in order to test cross-project & cross-repo discovery. I then did the following: Enable code_search - in your Azure DevOps organization (Marketplace → install extension). Login to Azure - Use the AZ CLI to authenticate to Azure with "az login". Create vscode/mcp.json file - Snippet is provided below, the organisation name should be changed to your organisations name. Start and enable your MCP server - In the mcp.json file you should see a "Start" button. Using the snippet below you will be prompted to add your organisation name. Ensure your CoPilot agent has access to the server under "tools" too. View this setup guide for full setup instructions (azure-devops-mcp/docs/GETTINGSTARTED.md at main · microsoft/azure-devops-mcp) Create a CoPilot Instructions file - with a search-first directive. I have inserted the full version used in this demo at the bottom of the article. Experiment with Prompts – Start generic (“How do we secure APIs?”). Review the output and tools used and then tailor your instructions. Considerations While this is a great approach I do still have some considerations when going to production. Latency - Using MCP tooling on every request will add some latency to developer requests. We can look at optimizing usage through copilot instructions to better identify when CoPilot should or shouldn't use the ADO MCP server. Complex Projects and Repositories - While I have demonstrated cross project and cross repository retrieval my demo environment does not truly simulate an enterprise ADO environment. Performance should be tested and closely monitored as organisational complexity increases. Public Preview - The ADO MCP server is moving quickly but is currently still public preview. We have demonstrated in this article how quickly we can make our Azure DevOps content discoverable. While their are considerations moving forward this showcases a direction towards agentic inner sourcing. Feel free to comment below how you think this approach could be extended or augmented for other use cases! Resources MCP Server Config (/.vscode/mcp.json) { "inputs": [ { "id": "ado_org", "type": "promptString", "description": "Azure DevOps organization name (e.g. 'contoso')" } ], "servers": { "ado": { "type": "stdio", "command": "npx", "args": ["-y", "@azure-devops/mcp", "${input:ado_org}"] } } } CoPilot Instructions (/.github/copilot-instructions.md) # GitHub Copilot Instructions for Azure DevOps MCP Integration This project uses Azure DevOps with MCP server integration to provide organizational context awareness. Always check to see if the Azure DevOps MCP server has a tool relevant to the user's request. ## Core Principles ### 1. Azure DevOps Integration - **Always prioritize Azure DevOps MCP tools** when users ask about: - Work items, stories, bugs, tasks - Pull requests and code reviews - Build pipelines and deployments - Repository operations and branch management - Wiki pages and documentation - Test plans and test cases - Project and team information ### 2. Organizational Context Awareness - Before suggesting solutions, **check existing organizational patterns** by: - Searching code across repositories for similar implementations - Referencing established coding standards and frameworks - Looking for existing shared libraries and utilities - Checking architectural decision records (ADRs) in wikis ### 3. Cross-Repository Intelligence - When providing code suggestions: - **Search for existing patterns** in other repositories first - **Reference shared libraries** and common utilities - **Maintain consistency** with organizational standards - **Suggest reusable components** when appropriate ## Tool Usage Guidelines ### Work Items and Project Management When users mention bugs, features, tasks, or project planning: ``` ✅ Use: wit_my_work_items, wit_create_work_item, wit_update_work_item ✅ Use: wit_list_backlogs, wit_get_work_items_for_iteration ✅ Use: work_list_team_iterations, core_list_projects ``` ### Code and Repository Operations When users ask about code, branches, or pull requests: ``` ✅ Use: repo_list_repos_by_project, repo_list_pull_requests_by_repo ✅ Use: repo_list_branches_by_repo, repo_search_commits ✅ Use: search_code for finding patterns across repositories ``` ### Documentation and Knowledge Sharing When users need documentation or want to create/update docs: ``` ✅ Use: wiki_list_wikis, wiki_get_page_content, wiki_create_or_update_page ✅ Use: search_wiki for finding existing documentation ``` ### Build and Deployment When users ask about builds, deployments, or CI/CD: ``` ✅ Use: pipelines_get_builds, pipelines_get_build_definitions ✅ Use: pipelines_run_pipeline, pipelines_get_build_status ``` ## Response Patterns ### 1. Discovery First Before providing solutions, always discover organizational context: ``` "Let me first check what patterns exist in your organization..." → Search code, check repositories, review existing work items ``` ### 2. Reference Organizational Standards When suggesting code or approaches: ``` "Based on patterns I found in your [RepositoryName] repository..." "Following your organization's standard approach seen in..." "This aligns with the pattern established in [TeamName]'s implementation..." ``` ### 3. Actionable Integration Always offer to create or update Azure DevOps artifacts: ``` "I can create a work item for this enhancement..." "Should I update the wiki page with this new pattern?" "Let me link this to the current iteration..." ``` ## Specific Scenarios ### New Feature Development 1. **Search existing repositories** for similar features 2. **Check architectural patterns** and shared libraries 3. **Review related work items** and planning documents 4. **Suggest implementation** based on organizational standards 5. **Offer to create work items** and documentation ### Bug Investigation 1. **Search for similar issues** across repositories and work items 2. **Check related builds** and recent changes 3. **Review test results** and failure patterns 4. **Provide solution** based on organizational practices 5. **Offer to create/update** bug work items and documentation ### Code Review and Standards 1. **Compare against organizational patterns** found in other repositories 2. **Reference coding standards** from wiki documentation 3. **Suggest improvements** based on established practices 4. **Check for reusable components** that could be leveraged ### Documentation Requests 1. **Search existing wikis** for related content 2. **Check for ADRs** and technical documentation 3. **Reference patterns** from similar projects 4. **Offer to create/update** wiki pages with findings ## Error Handling If Azure DevOps MCP tools are not available or fail: 1. **Inform the user** about the limitation 2. **Provide alternative approaches** using available information 3. **Suggest manual steps** for Azure DevOps integration 4. **Offer to help** with configuration if needed ## Best Practices ### Always Do: - ✅ Search organizational context before suggesting solutions - ✅ Reference existing patterns and standards - ✅ Offer to create/update Azure DevOps artifacts - ✅ Maintain consistency with organizational practices - ✅ Provide actionable next steps ### Never Do: - ❌ Suggest solutions without checking organizational context - ❌ Ignore existing patterns and implementations - ❌ Provide generic advice when specific organizational context is available - ❌ Forget to offer Azure DevOps integration opportunities --- **Remember: The goal is to provide intelligent, context-aware assistance that leverages the full organizational knowledge base available through Azure DevOps while maintaining development efficiency and consistency.**Unlock New AI and Cloud Potential with .NET 9 & Azure: Faster, Smarter, and Built for the Future
.NET 9, now available to developers, marks a significant milestone in the evolution of the .NET platform, pushing the boundaries of performance, cloud-native development, and AI integration. This release, shaped by contributions from over 9,000 community members worldwide, introduces thousands of improvements that set the stage for the future of application development. With seamless integration with Azure and a focus on cloud-native development and AI capabilities, .NET 9 empowers developers to build scalable, intelligent applications with unprecedented ease. Expanding Azure PaaS Support for .NET 9 With the release of .NET 9, a comprehensive range of Azure Platform as a Service (PaaS) offerings now fully support the platform’s new capabilities, including the latest .NET SDK for any Azure developer. This extensive support allows developers to build, deploy, and scale .NET 9 applications with optimal performance and adaptability on Azure. Additionally, developers can access a wealth of architecture references and sample solutions to guide them in creating high-performance .NET 9 applications on Azure’s powerful cloud services: Azure App Service: Run, manage, and scale .NET 9 web applications efficiently. Check out this blog to learn more about what's new in Azure App Service. Azure Functions: Leverage serverless computing to build event-driven .NET 9 applications with improved runtime capabilities. Azure Container Apps: Deploy microservices and containerized .NET 9 workloads with integrated observability. Azure Kubernetes Service (AKS): Run .NET 9 applications in a managed Kubernetes environment with expanded ARM64 support. Azure AI Services and Azure OpenAI Services: Integrate advanced AI and OpenAI capabilities directly into your .NET 9 applications. Azure API Management, Azure Logic Apps, Azure Cognitive Services, and Azure SignalR Service: Ensure seamless integration and scaling for .NET 9 solutions. These services provide developers with a robust platform to build high-performance, scalable, and cloud-native applications while leveraging Azure’s optimized environment for .NET. Streamlined Cloud-Native Development with .NET Aspire .NET Aspire is a game-changer for cloud-native applications, enabling developers to build distributed, production-ready solutions efficiently. Available in preview with .NET 9, Aspire streamlines app development, with cloud efficiency and observability at its core. The latest updates in Aspire include secure defaults, Azure Functions support, and enhanced container management. Key capabilities include: Optimized Azure Integrations: Aspire works seamlessly with Azure, enabling fast deployments, automated scaling, and consistent management of cloud-native applications. Easier Deployments to Azure Container Apps: Designed for containerized environments, .NET Aspire integrates with Azure Container Apps (ACA) to simplify the deployment process. Using the Azure Developer CLI (azd), developers can quickly provision and deploy .NET Aspire projects to ACA, with built-in support for Redis caching, application logging, and scalability. Built-In Observability: A real-time dashboard provides insights into logs, distributed traces, and metrics, enabling local and production monitoring with Azure Monitor. With these capabilities, .NET Aspire allows developers to deploy microservices and containerized applications effortlessly on ACA, streamlining the path from development to production in a fully managed, serverless environment. Integrating AI into .NET: A Seamless Experience In our ongoing effort to empower developers, we’ve made integrating AI into .NET applications simpler than ever. Our strategic partnerships, including collaborations with OpenAI, LlamaIndex, and Qdrant, have enriched the AI ecosystem and strengthened .NET’s capabilities. This year alone, usage of Azure OpenAI services has surged to nearly a billion API calls per month, illustrating the growing impact of AI-powered .NET applications. Real-World AI Solutions with .NET: .NET has been pivotal in driving AI innovations. From internal teams like Microsoft Copilot creating AI experiences with .NET Aspire to tools like GitHub Copilot, developed with .NET to enhance productivity in Visual Studio and VS Code, the platform showcases AI at its best. KPMG Clara is a prime example, developed to enhance audit quality and efficiency for 95,000 auditors worldwide. By leveraging .NET and scaling securely on Azure, KPMG implemented robust AI features aligned with strict industry standards, underscoring .NET and Azure as the backbone for high-performing, scalable AI solutions. Performance Enhancements in .NET 9: Raising the Bar for Azure Workloads .NET 9 introduces substantial performance upgrades with over 7,500 merged pull requests focused on speed and efficiency, ensuring .NET 9 applications run optimally on Azure. These improvements contribute to reduced cloud costs and provide a high-performance experience across Windows, Linux, and macOS. To see how significant these performance gains can be for cloud services, take a look at what past .NET upgrades achieved for Microsoft’s high-scale internal services: Bing achieved a major reduction in startup times, enhanced efficiency, and decreased latency across its high-performance search workflows. Microsoft Teams improved efficiency by 50%, reduced latency by 30–45%, and achieved up to 100% gains in CPU utilization for key services, resulting in faster user interactions. Microsoft Copilot and other AI-powered applications benefited from optimized runtime performance, enabling scalable, high-quality experiences for users. Upgrading to the latest .NET version offers similar benefits for cloud apps, optimizing both performance and cost-efficiency. For more information on updating your applications, check out the .NET Upgrade Assistant. For additional details on ASP.NET Core, .NET MAUI, NuGet, and more enhancements across the .NET platform, check out the full Announcing .NET 9 blog post. Conclusion: Your Path to the Future with .NET 9 and Azure .NET 9 isn’t just an upgrade—it’s a leap forward, combining cutting-edge AI integration, cloud-native development, and unparalleled performance. Paired with Azure’s scalability, these advancements provide a trusted, high-performance foundation for modern applications. Get started by downloading .NET 9 and exploring its features. Leverage .NET Aspire for streamlined cloud-native development, deploy scalable apps with Azure, and embrace new productivity enhancements to build for the future. For additional insights on ASP.NET, .NET MAUI, NuGet, and more, check out the full Announcing .NET 9 blog post. Explore the future of cloud-native and AI development with .NET 9 and Azure—your toolkit for creating the next generation of intelligent applications.Discover How App Modernization on Azure Enables Intelligent App Innovation
Legacy applications, built on outdated technologies, are increasingly becoming a roadblock for businesses in the fast-paced digital world. They struggle to manage growing data volumes and user traffic, posing scalability challenges that can lead to performance bottlenecks and system failures.
Managed Identity for Azure App Services
Azure App Service supports an interesting feature called Managed Identity from Azure Active Directory. This allows your App Service to easily connect to Azure Resources such as Azure KeyVault, Azure Storage, Azure SQL etc. You could refer to our official documentation for more details on this feature here. MSI-Validator helps you troubleshoot issues with Managed Identity on App Services.
A look into App Service: Backup and Restore over Azure Virtual Network
There is a new preview feature that provides a secure way backups are handled for web applications. This feature “App Service: Backup and Restore over Azure Virtual Network”, provides an additional layer of security by allowing backups to be stored in a firewall-protected storage account. The primary advantage I see of this feature is the enhanced security it offers. By storing backups in a firewall-protected storage account, it ensures that your data is safe from unauthorized access. Additionally, this feature allows for custom backups giving more control over what data is backed up and when. There are a few prerequisites needed: The app must be integrated with a virtual network or be in a v3 App Service environment. The storage account must have granted access from the virtual network that the app is integrated with. I am sharing couple of scenarios where this feature could be beneficial: A healthcare company web app that handles sensitive patient data. If there is a requirement to ensure that data is securely backed up and protected from unauthorized access they can back up their web app’s files and configuration data to a firewall-protected storage account, ensuring that their data is secure. In software development projects following DTAP deployment strategy, they have multiple environments such as Development, Testing, Staging, and Production. Each environment is isolated and has its own set of resources. With this feature, the pipelines can back up the web app’s files and configuration data to a firewall-protected storage account in the same virtual network. This aligns with their backup policy and adds an extra layer of security as the backups are not exposed to the public internet. This new preview feature offers enhanced security and flexibility for backing up web app data. It should be part of backup and disaster recovery strategy. It’s worth checking out if you’re looking for a secure and customizable backup solution for your web apps.It's time to 🍂 #FallForIntelligentApps 🍂
Today, we kick off the Fall season with content and activities to skill you up on all things Intelligent Apps or AI Apps on Azure with content, events, and community interactions. It is time to combine the power of AI, cloud-native application development, and, cloud-scale data to create highly differentiated digital experiences by building and modernizing intelligent applications with Azure for your users. Check out the blog below for some learning resources: https://techcommunity.microsoft.com/t5/apps-on-azure-blog/it-s-time-to-fallforintelligentapps/ba-p/3931266 What new skills or technologies are you focused on learning this fall?Azure App Service Limits blog series - performance issue tips/tricks?
Have you checked out the recent series of blogs about Azure App Service Limits? Azure App Service Limit (1) - Remote Storage (Windows) Azure App Service Limit (2) - Temp File Usage (Windows) Azure App Service Limit (3) - Connection Limit (TCP Connection, SNAT and TLS Version) Azure App Service Limit (4) - CPU (Windows) Azure App Service Limit (5) - Memory (Windows) What's your favorite tip or trick for resolving performance issues within Azure App Services?
