Making Azure the Best Place to Observe Your Apps with OpenTelemetry
Our goal is to make Azure the most observable cloud. To that end, we are refactoring Azure’s native observability platform to be based on OpenTelemetry, an industry standard for instrumenting applications and transmitting telemetry.
Announcing the Public Preview of Azure Monitor health models
Troubleshooting modern cloud-native workloads has become increasingly complex. As applications scale across distributed services and regions, pinpointing the root cause of performance degradation or outages often requires navigating a maze of disconnected signals, metrics, and alerts. This fragmented experience slows down troubleshooting and burdens engineering teams with manual correlation work. We address these challenges by introducing a unified, intelligent concept of workload health that’s enriched with application context. Health models streamline how you monitor, assess, and respond to issues affecting your workloads. Built on Azure service groups, they provide an out-of-the-box model tailored to your environment, consolidate signals to reduce alert noise, and surface actionable insights — all designed to accelerate detection, diagnosis, and resolution across your Azure landscape. Overview Azure Monitor health models enable customers to monitor the health of their applications with ease and confidence. These models use the Azure-wide workload concept of service groups to infer the scope of workloads and provide out-of-the-box health criteria based on platform metrics for Azure resources. Key Capabilities Out-of-the-Box Health Model Customers often struggle with defining and monitoring the health of their workloads due to the variability of metrics across different Azure resources. Azure Monitor health models provide a simplified out-of-the-box health experience built using Azure service group membership. Customers can define the scope of their workload using service groups and receive default health criteria based on platform metrics. This includes recommended alert rules for various Azure resources, ensuring comprehensive monitoring coverage. Improved Detection of Workload Issues Isolating the root cause of workload issues can be time-consuming and challenging, especially when dealing with multiple signals from various resources. The health model aggregates health signals across the model to generate a single health notification, helping customers isolate the type of signal that became unhealthy. This enables quick identification of whether the issue is related to backend services or user-centric signals. Quick Impact Assessment Assessing the impact of workload issues across different regions and resources can be complex and slow, leading to delayed responses and prolonged downtime. The health model provides insights into which Azure resources or components have become unhealthy, which regions are affected, and the duration of the impact based on health history. This allows customers to quickly assess the scope and severity of issues within the workload. Localize the Issue Identifying the specific signals and resources that triggered a health state change can be difficult, leading to inefficient troubleshooting and resolution processes. Health models inform customers which signals triggered the health state change, and which service group members were affected. This enables quick isolation of the trouble source and notifies the relevant team, streamlining the troubleshooting process. Customizable Health Criteria for Bespoke Workloads Many organizations operate complex, bespoke workloads that require their own specific health definitions. Relying solely on default platform metrics can lead to blind spots or false positives, making it difficult to accurately assess the true health of these custom applications. Azure Monitor health models allow customers to tailor health assessments by adding custom health signals. These signals can be sourced from Azure Monitor data such as Application Insights, Managed Prometheus, and Log Analytics. This flexibility empowers teams to tune the health model to reflect the unique characteristics and performance indicators of their workloads, ensuring more precise and actionable health insights. Getting Started Ready to simplify and accelerate how you monitor the health of your workloads? Getting started with Azure Monitor health models is easy — and during the public preview, it’s completely free to use. Pricing details will be shared ahead of general availability (GA), so you can plan with confidence. Start Monitoring in Minutes Define Your Service Group Create your service group and add the relevant resources as members to the service group. If you don’t yet have access to service groups, you can join here. Create Your Health Model In the Azure Portal navigate to Health Models and create your first model. You’ll get out-of-the-box health criteria automatically applied. Customize to Fit Your Needs In many cases the default health signals may suit your needs, but we support customization as well. Investigate and Act Use the health timeline and our alerting integration to quickly assess impact, isolate issues, and take action — all from a single pane of glass. You can access health models today in the Azure portal! For more details on how to get started with health models, please refer to our documentation. We Want to Hear From You Azure Monitor health models are built with our customers in mind — and your feedback is essential to shaping the future of this experience. Whether you're using the out-of-the-box health model or customizing it to fit your unique workloads, we want to know what’s working well and where we can improve. Share Your Feedback Use the “Give Feedback” feature directly within the Azure Monitor health models experience to send us your thoughts in context. Post your ideas in the Azure Monitor community. Prefer email? Reach out to us at azmonhealthmodels@service.microsoft.com — we’re listening. Your insights help us prioritize features, improve usability, and ensure Azure Monitor continues to meet the evolving needs of modern cloud-native operations.How to leverage Azure Monitor to meet functional and non-functional requirements - No.1 overview
Azure Monitor can be used for centralized monitoring and analysis of log data by using Kusto query, thus Azure Monitor allows you to effectively monitor and visualize Azure resources. Azure Arc also empowers Azure Monitor to expand its capability to on-premise and other public clouds. You can monitor every resources across environments, Azure, AWS, GCP, OCI, on-premise and others, with Azure Monitor and Azure Arc, then Azure Monitor minimize your effort to manage all the resources regardless locations or environments. Azure Monitor is a very powerful solution, but customers and partners sometimes have a challenge to map Azure Monitor features to their functional and non-functional requirements. These series articles describe how to use various Azure Monitor features in terms of functional and non-functional requirements. This article answers how to meet the requirements by using Azure Monitor.
Public Preview: Smarter Troubleshooting in Azure Monitor with AI-powered Investigation
Investigate smarter – click, analyze, and easily mitigate with Azure Monitor investigations! We are excited to introduce the public preview of Azure Monitor issue and investigation. These new capabilities are designed to enhance your troubleshooting experience and streamline the process of resolving health degradations in your application and infrastructure.Identify and solve performance issues faster with App Insights Code Optimizations
The integration of Code Optimizations with Microsoft Copilot for Azure and GitHub Copilot enables seamless integration between operations teams identifying performance bottlenecks in running .NET applications on Azure, and developers remediating them faster on code level in Visual Studio Code.Automate Your Log Analytics Workflows with AI and Logic Apps
In this post, we’ll demonstrate how to build a simple yet powerful workflow using Azure Logic Apps, Log Analytics queries, and LLMs to automate log analysis, save time, and spot issues faster. While we focus here on an example using Application Insights data with Azure OpenAI, the same approach can be applied to any Log Analytics source - whether raw logs, security events, or custom logs. By customizing your queries and AI prompts to match your data and the model’s capabilities, you can easily adapt this workflow to meet your specific needs. Note: This blog post offers guidance for automating workflows with Log Analytics data and LLMs using existing Azure Monitor products. It’s intended as a flexible approach based on user needs and preferences, providing an additional option alongside other Microsoft experiences, such as Azure Monitor issues and investigations (preview). Application Insights as a Use Case Imagine you’re an Application Insights user relying on the AppTraces table - detailed logs of events, errors, and critical traces. You need to spot hour-over-hour spikes or drops, identify operations causing the most issues, and detect recurring patterns or keywords that reveal deeper problems. These insights help turn raw data into actionable information. Running queries and analyzing logs regularly is essential, and automation offers a way to make this process more efficient. This saves time and helps you focus on the most impactful insights - so you can quickly move on to what matters next. With Azure Logic Apps, you can create a recurring workflow that automatically runs your Log Analytics queries, sends the summarized results to Azure OpenAI for analysis, and delivers a clear, actionable report straight to your inbox on your preferred schedule. From Logs to Insights: Step-by-Step AI Workflow 1. Create a Logic App Go to the Azure Portal and create a new Logic App. Open the Logic App Designer to start building your workflow. Helpful resource: Overview - Azure Logic Apps | Microsoft Learn 2. Set a Trigger Add a trigger to start your flow - for this scenario, we recommend using the Recurrence trigger to schedule it on a weekly basis (or any frequency you prefer). Of course, you can choose other triggers depending on your specific needs. 3. Query Your Log Analytics Data Add the Azure Monitor Logs - “Run query and list results” connector to your Logic App. Connect it to your Log Analytics workspace (or another relevant resource). Write a Kusto Query Language (KQL) query to pull data from Log Analytics Tables. In our example, the query retrieves aggregated error-level (SeverityLevel = 3) and critical-level (SeverityLevel = 4) traces from the last week, grouped by hour and operation name, with three sample messages for context. This not only shows the number of errors, when they occurred, and which operations were most affected, but also gives the LLM in the next step a solid foundation for uncovering deeper insights and trends. The query: AppTraces | where TimeGenerated > startofday(ago(7d)) | where SeverityLevel in (3, 4) // Error = 3, Critical = 4 | summarize TracesCount = count(), SampleMessages = make_list(Message, 3) by bin(TimeGenerated, 1h), SeverityLevel, OperationName | order by TimeGenerated asc Tip: Log datasets can be huge - use the summarize operator to aggregate results and reduce the volume for the AI model. Helpful resource: Connect to Log Analytics or Application Insights - Azure Logic Apps | Microsoft Learn 4. Prerequisite - Azure OpenAI Resource Configuration Make sure you have an Azure OpenAI resource set up and an AI model (e.g., GPT-4) deployed before continuing with your workflow. Helpful resource: What is Azure OpenAI in Azure AI Foundry Models? | Microsoft Learn 5. Analyze and Summarize with Azure OpenAI In Logic Apps, add an HTTP action and set all the parameters to call the OpenAI API endpoint. Pass the query results from the previous step (step 3) as input and instruct the OpenAI model to: Summarize key findings - for example, the total number of errors and critical events, and identify the top operations generating the most issues. Highlight anomalies or trends - such as trends and spikes in errors over time (hour-by-hour), and detection of recurring error patterns or keywords. Provide recommendations prioritized by urgency to guide the next steps. Format the output in HTML for easy email rendering. Tip: The body structure sent to the AI includes both System and User rules, formatted together as one string (see below). Helpful resource: How to use Assistants with Logic apps - Azure OpenAI | Microsoft Learn Here’s the prompt example: "messages": [ { "role": "system", "content": "You are an Azure AI tool that creates a weekly report based solely on this prompt and input JSON data from Log Analytics. The input is a list of records, each with these fields: TimeGenerated (ISO 8601 timestamp string), SeverityLevel (integer, where 3=Error, 4=Critical), OperationName (string), TracesCount (integer), SampleMessages (JSON string representing a list of up to 3 messages). Your tasks: 1) Sum the TracesCount values accurately to provide total counts for the entire week and broken down by day and SeverityLevel. 2) Present TracesCount counts per OperationName, grouped by hour and day with severity-level breakdowns. 3) Identify and list the top 10 OperationNames by combined Error and Critical TracesCount for the week, including up to 3 unique sample messages per OperationName, removing duplicates. 4) Compare TracesCount hour-by-hour and day-by-day, calculating percentage changes and highlighting spikes (>100% increase) and significant drops. 5) Detect any new OperationNames appearing during the week that did not appear before. 6) Highlight recurring Errors and Critical issues based on keywords: timeout, exception, outofmemory, connection refused. 7) Assign urgency levels based on frequency, impact, and trends. 8) Provide clear, prioritized recommendations for resolving the main issues. Format your output as valid inline-styled HTML using only these tags: <h2>, <h3>, <p>, <ul>, <li>, and <hr>. Include these report sections in this order: Executive Summary, Weekly Totals and Daily Breakdown, Hourly and Daily Trend Comparison, New & Emerging OperationNames, Detailed Operation Errors, Data Quality & Confidence, Recommendations. Include an opening title with the report’s time period." }, { "role": "user", "content": "string(outputs('Run_query_and_list_results'))" } ] } 6. Send the Report via Email Use the Send an email (V2) connector, or another endpoint connector, such as Teams. Send the AI-generated report to your team, stakeholders, or yourself. Customize the email subject, body, and importance level as needed. Section of the final email report: Important reminder: Once your flow is ready, enable it in Logic Apps to ensure it starts running according to the schedule. Key Takeaways By combining Azure Logic Apps, Log Analytics, and Azure OpenAI, you can turn raw, complex logs into clear, actionable insights - automatically. This workflow helps reduce manual analysis time and enables faster responses to critical issues. Ready to try? Build your own automated log insights workflow today and empower your team with AI-driven clarity.
