0.011W Power Floor on i7-1255U: A Step Toward Microsoft’s Sustainability Vision
It is inspiring to see Microsoft leading the tech industry toward a greener future. Initiatives such as the Carbon Negative 2030 goal and the Energy Saver features in Windows 11 are important steps in environmental stewardship. These efforts show that software and hardware can work in harmony to preserve our planet. As a researcher, I have spent 18 months exploring how to further support this vision by identifying the absolute efficiency “floors” of the Intel Core i7-1255U (Alder Lake). My study focuses on a configuration that enhances Microsoft’s energy-saving protocols to achieve maximum hardware longevity. Key Technical Findings (18-Month Case Study): Dynamic Power Floors: Through precise optimization, I observed CPU power draw dropping to a floor of 0.011W at 1.8MHz during deep idle/sleep states, with the GPU reaching a 0W floor. Efficiency in Motion: During active productivity tasks, the system can reach 0.4W – 0.5W at a voltage range of 0.6V – 0.8V, demonstrating impressive scaling flexibility. Thermal Performance: The system consistently operates between 25°C – 35°C. In cooler ambient environments (15°C – 25°C), hardware can maintain 16°C – 20°C, virtually eliminating thermal stress. Battery Endurance: Using a standard 3-cell battery with 5% wear (originally marketed for 6 hours), these optimizations enabled up to 10 hours of continuous video playback. Uncompromised Stability: Over 18 months of daily usage, the system has encountered zero Blue Screen of Death (BSOD) events. This confirms that pushing efficiency boundaries can be done while maintaining the rock-solid reliability expected of the Windows platform. This study is a tribute to the versatility of Windows 11 and the engineering behind modern silicon. By maximizing the life of the devices we already own, we contribute directly to reducing global e-waste. Detailed technical logs (HWiNFO) and configuration data are available for verification here: 👉 [[Intel 12th] 0.011W Package Power Floor via Custom Optimization | Microsoft Community Hub] I look forward to discussing these efficiency milestones with the community and Microsoft engineers.Benchmarking Local AI Models
Introduction Selecting the right AI model for your application requires more than reading benchmark leaderboards. Published benchmarks measure academic capabilities, question answering, reasoning, coding, but your application has specific requirements: latency budgets, hardware constraints, quality thresholds. How do you know if Phi-4 provides acceptable quality for your document summarization use case? Will Qwen2.5-0.5B meet your 100ms response time requirement? Does your edge device have sufficient memory for Phi-3.5 Mini? The answer lies in empirical testing: running actual models on your hardware with your workload patterns. This article demonstrates building a comprehensive model benchmarking platform using FLPerformance, Node.js, React, and Microsoft Foundry Local. You'll learn how to implement scientific performance measurement, design meaningful benchmark suites, visualize multi-dimensional comparisons, and make data-driven model selection decisions. Whether you're evaluating models for production deployment, optimizing inference costs, or validating hardware specifications, this platform provides the tools for rigorous performance analysis. Why Model Benchmarking Requires Purpose-Built Tools You cannot assess model performance by running a few manual tests and noting the results. Scientific benchmarking demands controlled conditions, statistically significant sample sizes, multi-dimensional metrics, and reproducible methodology. Understand why purpose-built tooling is essential. Performance is multi-dimensional. A model might excel at throughput (tokens per second) but suffer at latency (time to first token). Another might generate high-quality outputs slowly. Your application might prioritize consistency over average performance, a model with variable response times (high p95/p99 latency) creates poor user experiences even if averages look good. Measuring all dimensions simultaneously enables informed tradeoffs. Hardware matters enormously. Benchmark results from NVIDIA A100 GPUs don't predict performance on consumer laptops. NPU acceleration changes the picture again. Memory constraints affect which models can even load. Test on your actual deployment hardware or comparable specifications to get actionable results. Concurrency reveals bottlenecks. A model handling one request excellently might struggle with ten concurrent requests. Real applications experience variable load, measuring only single-threaded performance misses critical scalability constraints. Controlled concurrency testing reveals these limits. Statistical rigor prevents false conclusions. Running a prompt once and noting the response time tells you nothing about performance distribution. Was this result typical? An outlier? You need dozens or hundreds of trials to establish p50/p95/p99 percentiles, understand variance, and detect stability issues. Comparison requires controlled experiments. Different prompts, different times of day, different system loads, all introduce confounding variables. Scientific comparison runs identical workloads across models sequentially, controlling for external factors. Architecture: Three-Layer Performance Testing Platform FLPerformance implements a clean separation between orchestration, measurement, and presentation: The frontend React application provides model management, benchmark configuration, test execution, and results visualization. Users add models from the Foundry Local catalog, configure benchmark parameters (iterations, concurrency, timeout values), launch test runs, and view real-time progress. The results dashboard displays comparison tables, latency distribution charts, throughput graphs, and "best model for..." recommendations. The backend Node.js/Express server orchestrates tests and captures metrics. It manages the single Foundry Local service instance, loads/unloads models as needed, executes benchmark suites with controlled concurrency, measures comprehensive metrics (TTFT, TPOT, total latency, throughput, error rates), and persists results to JSON storage. WebSocket connections provide real-time progress updates during long benchmark runs. Foundry Local SDK integration uses the official foundry-local-sdk npm package. The SDK manages service lifecycle, starting, stopping, health checkin, and handles model operations, downloading, loading into memory, unloading. It provides OpenAI-compatible inference APIs for consistent request formatting across models. The architecture supports simultaneous testing of multiple models by loading them one at a time, running identical benchmarks, and aggregating results for comparison: User Initiates Benchmark Run ↓ Backend receives {models: [...], suite: "default", iterations: 10} ↓ For each model: 1. Load model into Foundry Local 2. Execute benchmark suite - For each prompt in suite: * Run N iterations * Measure TTFT, TPOT, total time * Track errors and timeouts * Calculate tokens/second 3. Aggregate statistics (mean, p50, p95, p99) 4. Unload model ↓ Store results with metadata ↓ Return comparison data to frontend ↓ Visualize performance metrics Implementing Scientific Measurement Infrastructure Accurate performance measurement requires instrumentation that captures multiple dimensions without introducing measurement overhead: // src/server/benchmark.js import { performance } from 'perf_hooks'; export class BenchmarkExecutor { constructor(foundryClient, options = {}) { this.client = foundryClient; this.options = { iterations: options.iterations || 10, concurrency: options.concurrency || 1, timeout_ms: options.timeout_ms || 30000, warmup_iterations: options.warmup_iterations || 2 }; } async runBenchmarkSuite(modelId, prompts) { const results = []; // Warmup phase (exclude from results) console.log(`Running ${this.options.warmup_iterations} warmup iterations...`); for (let i = 0; i < this.options.warmup_iterations; i++) { await this.executePrompt(modelId, prompts[0].text); } // Actual benchmark runs for (const prompt of prompts) { console.log(`Benchmarking prompt: ${prompt.id}`); const measurements = []; for (let i = 0; i < this.options.iterations; i++) { const measurement = await this.executeMeasuredPrompt( modelId, prompt.text ); measurements.push(measurement); // Small delay between iterations to stabilize await sleep(100); } results.push({ prompt_id: prompt.id, prompt_text: prompt.text, measurements, statistics: this.calculateStatistics(measurements) }); } return { model_id: modelId, timestamp: new Date().toISOString(), config: this.options, results }; } async executeMeasuredPrompt(modelId, promptText) { const measurement = { success: false, error: null, ttft_ms: null, // Time to first token tpot_ms: null, // Time per output token total_ms: null, tokens_generated: 0, tokens_per_second: 0 }; try { const startTime = performance.now(); let firstTokenTime = null; let tokenCount = 0; // Streaming completion to measure TTFT const stream = await this.client.chat.completions.create({ model: modelId, messages: [{ role: 'user', content: promptText }], max_tokens: 200, temperature: 0.7, stream: true }); for await (const chunk of stream) { if (chunk.choices[0]?.delta?.content) { if (firstTokenTime === null) { firstTokenTime = performance.now(); measurement.ttft_ms = firstTokenTime - startTime; } tokenCount++; } } const endTime = performance.now(); measurement.total_ms = endTime - startTime; measurement.tokens_generated = tokenCount; if (tokenCount > 1 && firstTokenTime) { // TPOT = time after first token / (tokens - 1) const timeAfterFirstToken = endTime - firstTokenTime; measurement.tpot_ms = timeAfterFirstToken / (tokenCount - 1); measurement.tokens_per_second = 1000 / measurement.tpot_ms; } measurement.success = true; } catch (error) { measurement.error = error.message; measurement.success = false; } return measurement; } calculateStatistics(measurements) { const successful = measurements.filter(m => m.success); const total = measurements.length; if (successful.length === 0) { return { success_rate: 0, error_rate: 1.0, sample_size: total }; } const ttfts = successful.map(m => m.ttft_ms).sort((a, b) => a - b); const tpots = successful.map(m => m.tpot_ms).filter(v => v !== null).sort((a, b) => a - b); const totals = successful.map(m => m.total_ms).sort((a, b) => a - b); const throughputs = successful.map(m => m.tokens_per_second).filter(v => v > 0); return { success_rate: successful.length / total, error_rate: (total - successful.length) / total, sample_size: total, ttft: { mean: mean(ttfts), median: percentile(ttfts, 50), p95: percentile(ttfts, 95), p99: percentile(ttfts, 99), min: Math.min(...ttfts), max: Math.max(...ttfts) }, tpot: tpots.length > 0 ? { mean: mean(tpots), median: percentile(tpots, 50), p95: percentile(tpots, 95) } : null, total_latency: { mean: mean(totals), median: percentile(totals, 50), p95: percentile(totals, 95), p99: percentile(totals, 99) }, throughput: { mean_tps: mean(throughputs), median_tps: percentile(throughputs, 50) } }; } } function mean(arr) { return arr.reduce((sum, val) => sum + val, 0) / arr.length; } function percentile(sortedArr, p) { const index = Math.ceil((sortedArr.length * p) / 100) - 1; return sortedArr[Math.max(0, index)]; } function sleep(ms) { return new Promise(resolve => setTimeout(resolve, ms)); } This measurement infrastructure captures: Time to First Token (TTFT): Critical for perceived responsiveness—users notice delays before output begins Time Per Output Token (TPOT): Determines generation speed after first token—affects throughput Total latency: End-to-end time—matters for batch processing and high-volume scenarios Tokens per second: Overall throughput metric—useful for capacity planning Statistical distributions: Mean alone masks variability—p95/p99 reveal tail latencies that impact user experience Success/error rates: Stability metrics—some models timeout or crash under load Designing Meaningful Benchmark Suites Benchmark quality depends on prompt selection. Generic prompts don't reflect real application behavior. Design suites that mirror actual use cases: // benchmarks/suites/default.json { "name": "default", "description": "General-purpose benchmark covering diverse scenarios", "prompts": [ { "id": "short-factual", "text": "What is the capital of France?", "category": "factual", "expected_tokens": 5 }, { "id": "medium-explanation", "text": "Explain how photosynthesis works in 3-4 sentences.", "category": "explanation", "expected_tokens": 80 }, { "id": "long-reasoning", "text": "Analyze the economic factors that led to the 2008 financial crisis. Discuss at least 5 major causes with supporting details.", "category": "reasoning", "expected_tokens": 250 }, { "id": "code-generation", "text": "Write a Python function that finds the longest palindrome in a string. Include docstring and example usage.", "category": "coding", "expected_tokens": 150 }, { "id": "creative-writing", "text": "Write a short story (3 paragraphs) about a robot learning to paint.", "category": "creative", "expected_tokens": 200 } ] } This suite covers multiple dimensions: Length variation: Short (5 tokens), medium (80), long (250)—tests models across output ranges Task diversity: Factual recall, explanation, reasoning, code, creative—reveals capability breadth Token predictability: Expected token counts enable throughput calculations For production applications, create custom suites matching your actual workload: { "name": "customer-support", "description": "Simulates actual customer support queries", "prompts": [ { "id": "product-question", "text": "How do I reset my password for the customer portal?" }, { "id": "troubleshooting", "text": "I'm getting error code 503 when trying to upload files. What should I do?" }, { "id": "policy-inquiry", "text": "What is your refund policy for annual subscriptions?" } ] } Visualizing Multi-Dimensional Performance Comparisons Raw numbers don't reveal insights—visualization makes patterns obvious. The frontend implements several comparison views: Comparison Table shows side-by-side metrics: // frontend/src/components/ResultsTable.jsx export function ResultsTable({ results }) { return ( {results.map(result => ( ))} Model TTFT (ms) TPOT (ms) Throughput (tok/s) P95 Latency Error Rate {result.model_id} {result.stats.ttft.median.toFixed(0)} (p95: {result.stats.ttft.p95.toFixed(0)}) {result.stats.tpot?.median.toFixed(1) || 'N/A'} {result.stats.throughput.median_tps.toFixed(1)} {result.stats.total_latency.p95.toFixed(0)} ms 0.05 ? 'error' : 'success'}> {(result.stats.error_rate * 100).toFixed(1)}% ); } Latency Distribution Chart reveals performance consistency: // Using Chart.js for visualization export function LatencyChart({ results }) { const data = { labels: results.map(r => r.model_id), datasets: [ { label: 'Median (p50)', data: results.map(r => r.stats.total_latency.median), backgroundColor: 'rgba(75, 192, 192, 0.5)' }, { label: 'p95', data: results.map(r => r.stats.total_latency.p95), backgroundColor: 'rgba(255, 206, 86, 0.5)' }, { label: 'p99', data: results.map(r => r.stats.total_latency.p99), backgroundColor: 'rgba(255, 99, 132, 0.5)' } ] }; return ( ); } Recommendations Engine synthesizes multi-dimensional comparison: export function generateRecommendations(results) { const recommendations = []; // Find fastest TTFT (best perceived responsiveness) const fastestTTFT = results.reduce((best, r) => r.stats.ttft.median < best.stats.ttft.median ? r : best ); recommendations.push({ category: 'Fastest Response', model: fastestTTFT.model_id, reason: `Lowest median TTFT: ${fastestTTFT.stats.ttft.median.toFixed(0)}ms` }); // Find highest throughput const highestThroughput = results.reduce((best, r) => r.stats.throughput.median_tps > best.stats.throughput.median_tps ? r : best ); recommendations.push({ category: 'Best Throughput', model: highestThroughput.model_id, reason: `Highest tok/s: ${highestThroughput.stats.throughput.median_tps.toFixed(1)}` }); // Find most consistent (lowest p95-p50 spread) const mostConsistent = results.reduce((best, r) => { const spread = r.stats.total_latency.p95 - r.stats.total_latency.median; const bestSpread = best.stats.total_latency.p95 - best.stats.total_latency.median; return spread < bestSpread ? r : best; }); recommendations.push({ category: 'Most Consistent', model: mostConsistent.model_id, reason: 'Lowest latency variance (p95-p50 spread)' }); return recommendations; } Key Takeaways and Benchmarking Best Practices Effective model benchmarking requires scientific methodology, comprehensive metrics, and application-specific testing. FLPerformance demonstrates that rigorous performance measurement is accessible to any development team. Critical principles for model evaluation: Test on target hardware: Results from cloud GPUs don't predict laptop performance Measure multiple dimensions: TTFT, TPOT, throughput, consistency all matter Use statistical rigor: Single runs mislead—capture distributions with adequate sample sizes Design realistic workloads: Generic benchmarks don't predict your application's behavior Include warmup iterations: Model loading and JIT compilation affect early measurements Control concurrency: Real applications handle multiple requests—test at realistic loads Document methodology: Reproducible results require documented procedures and configurations The complete benchmarking platform with model management, measurement infrastructure, visualization dashboards, and comprehensive documentation is available at github.com/leestott/FLPerformance. Clone the repository and run the startup script to begin evaluating models on your hardware. Resources and Further Reading FLPerformance Repository - Complete benchmarking platform Quick Start Guide - Setup and first benchmark run Microsoft Foundry Local Documentation - SDK reference and model catalog Architecture Guide - System design and SDK integration Benchmarking Best Practices - Methodology and troubleshootingIgnite 2022: Announcing new features in Azure Database for MySQL – Flexible Server
Today, we're pleased to announce a set of new and exciting features in Azure Database for MySQL - Flexible Server that further improve the service's availability, performance, security, management, and developer experiences!
Microsoft Azure innovation powers leading price-performance for MySQL database in the cloud
As part of our commitment to ensuring that Microsoft Azure is the best place to run MySQL workloads, Microsoft is excited to announce that Azure Database for MySQL - Flexible Server just achieved a new, faster performance benchmark.
Azure Database for MySQL - Flexible Server accelerated logs (Preview)
The new Accelerated Logs (preview) feature significantly enhances the performance of Azure Database for MySQL flexible servers, offering a dynamic solution that is designed for high throughput needs that also reduces latency and optimizes cost efficiency. This innovative feature is available with the Business-Critical service tier at no additional cost.
The return of the performance leak - this time with subreports
Anyone remember the v2405 bug that caused loading and closing forms to become slower over time? We found something similar in the current release (tested with semi-annual 2508 (19127.20484)), just with sub reports instead of forms. Here are the repro instructions: Repro instructions Table 1. Create a "numbers" table with a PK field "nr" (Long Integer). 2. Fill it with the numbers from 1 to 100: Dim i For i = 1 To 100 CurrentDb.Execute "INSERT INTO numbers (nr) VALUES (" & i & ")" Next i Sub report 3. Create a new empty report in design view, using the "numbers" table as the record source. 4. In the detail section, add two text boxes "Text1" and "Text2" next to each other, both with the "nr" field as their control source. 5. Reduce the size of the detail section to one "row", containing just the two text boxes. Remove the page header/footer. 6. Add an Format event handler to the detail section with the following content: Me.Text2.Visible = False 7. Save the report as "sub". Main report 8. Create another report "main", which contains nothing but the "sub" report as a subreport in its detail section. Set the record source of "main" to "SELECT * FROM numbers WHERE nr <= 10" Reproduce problem 9. Create the following method in a module: Sub report_loop() Dim i As Long Dim start_time As Single Dim report_name As String Dim path As String report_name = "main" For i = 1 To 40 path = Environ("Temp") & "\testreport.pdf" start_time = Timer DoCmd.OpenReport report_name, acViewPreview DoCmd.OutputTo acOutputReport, report_name, acFormatPDF, path DoCmd.Close acReport, report_name Debug.Print Timer - start_time Next End Sub 10. Run report_loop. Observe that each report export gets slower and slower. Analysis Compare the first and last number in the immediate window: on my machine it's 0.4s for the first report and 2.3s for the last. Note that if you run report_loop again, it will start slow! In other words, each report export causes MS Access to "leak performance" that is only regained after restarting Access. We were able to reproduce this issue with Microsoft 365 semi-annual channel, Version 2508 (19127.20484). I'll do more tests tomorrow and try different older versions to find out when this bug was introduced. Workarounds If you move the visibility code from the Format event to the Print event, the problem still occurs, but slower (0.4s -> 0.72s instead of 0.4s -> 2.3s). The workaround that worked for us was to use =IIf(...) in the control source instead of modifying the visibility. Obviously, that only works for text boxes, not for lines or other controls.Unlocking AI-Driven Data Access: Azure Database for MySQL Support via the Azure MCP Server
Step into a new era of data-driven intelligence with the fusion of Azure MCP Server and Azure Database for MySQL, where your MySQL data is no longer just stored, but instantly conversational, intelligent and action-ready. By harnessing the open-standard Model Context Protocol (MCP), your AI agents can now query, analyze and automate in natural language, accessing tables, surfacing insights and acting on your MySQL-driven business logic as easily as chatting with a colleague. It’s like giving your data a voice and your applications a brain, all within Azure’s trusted cloud platform. We are excited to announce that we have added support for Azure Database for MySQL in Azure MCP Server. The Azure MCP Server leverages the Model Context Protocol (MCP) to allow AI agents to seamlessly interact with various Azure services to perform context-aware operations such as querying databases and managing cloud resources. Building on this foundation, the Azure MCP Server now offers a set of tools that AI agents and apps can invoke to interact with Azure Database for MySQL - enabling them to list and query databases, retrieve schema details of tables, and access server configurations and parameters. These capabilities are delivered through the same standardized interface used for other Azure services, making it easier to the adopt the MCP standard for leveraging AI to work with your business data and operations across the Azure ecosystem. Before we delve into these new tools and explore how to get started with them, let’s take a moment to refresh our understanding of MCP and the Azure MCP Server - what they are, how they work, and why they matter. MCP architecture and key components The Model Context Protocol (MCP) is an emerging open protocol designed to integrate AI models with external data sources and services in a scalable, standardized, and secure manner. MCP dictates a client-server architecture with four key components: MCP Host, MCP Client, MCP Server and external data sources, services and APIs that provide the data context required to enhance AI models. To explain briefly, an MCP Host (AI apps and agents) includes an MCP client component that connects to one or more MCP Servers. These servers are lightweight programs that securely interface with external data sources, services and APIs and exposes them to MCP clients in the form of standardized capabilities called tools, resources and prompts. Learn more: MCP Documentation What is Azure MCP Server? Azure offers a multitude of cloud services that help developers build robust applications and AI solutions to address business needs. The Azure MCP Server aims to expose these powerful services for agentic usage, allowing AI systems to perform operations that are context-aware of your Azure resources and your business data within them, while ensuring adherence to the Model Context Protocol. It supports a wide range of Azure services and tools including Azure AI Search, Azure Cosmos DB, Azure Storage, Azure Monitor, Azure CLI and Developer CLI extensions. This means that you can empower AI agents, apps and tools to: Explore your Azure resources, such as listing and retrieving details on your Azure subscriptions, resource groups, services, databases, and tables. Search, query and analyze your data and logs. Execute CLI and Azure Developer CLI commands directly, and more! Learn more: Azure MCP Server GitHub Repository Introducing new Azure MCP Server tools to interact with Azure Database for MySQL The Azure MCP Server now includes the following tools that allow AI agents to interact with Azure Database for MySQL and your valuable business data residing in these servers, in accordance with the MCP standard: Tool Description Example Prompts azmcp_mysql_server_list List all MySQL servers in a subscription & resource group "List MySQL servers in resource group 'prod-rg'." "Show MySQL servers in region 'eastus'." azmcp_mysql_server_config_get Retrieve the configuration of a MySQL server "What is the backup retention period for server 'my-mysql-server'?" "Show storage allocation for server 'my-mysql-server'." azmcp_mysql_server_param_get Retrieve a specific parameter of a MySQL server "Is slow_query_log enabled on server my-mysql-server?" "Get innodb_buffer_pool_size for server my-mysql-server." azmcp_mysql_server_param_set Set a specific parameter of a MySQL server to a specific value "Set max_connections to 500 on server my-mysql-server." "Set wait_timeout to 300 on server my-mysql-server." azmcp_mysql_table_list List all tables in a MySQL database "List tables starting with 'tmp_' in database 'appdb'." "How many tables are in database 'analytics'?" azmcp_mysql_table_schema_get Get the schema of a specific table in a MySQL database "Show indexes for table 'transactions' in database 'billing'." "What is the primary key for table 'users' in database 'auth'?" azmcp_mysql_database_query Executes a SELECT query on a MySQL Database. The query must start with SELECT and cannot contain any destructive SQL operations for security reasons. “How many orders were placed in the last 30 days in the salesdb.orders table?” “Show the number of new users signed up in the last week in appdb.users grouped by day.” These interactions are secured using Microsoft Entra authentication, which enables seamless, identity-based access to Azure Database for MySQL - eliminating the need for password storage and enhancing overall security. How are these new tools in the Azure MCP Server different from the standalone MCP Server for Azure Database for MySQL? We have integrated the key capabilities of the Azure Database for MySQL MCP server into the Azure MCP Server, making it easier to connect your agentic apps not only to Azure Database for MySQL but also to other Azure services through one unified and secure interface! How to get started Installing and running the Azure MCP Server is quick and easy! Use GitHub Copilot in Visual Studio Code to gain meaningful insights from your business data in Azure Database for MySQL. Pre-requisites Install Visual Studio Code. Install GitHub Copilot and GitHub Copilot Chat extensions. An Azure Database for MySQL with Microsoft Entra authentication enabled. Ensure that the MCP Server is installed on a system with network connectivity and credentials to connect to Azure Database for MySQL. Installation and Testing Please use this guide for installation: Azure MCP Server Installation Guide Try the following prompts with your Azure Database for MySQL: Azure Database for MySQL tools for Azure MCP Server Try it out and share your feedback! Start using Azure MCP Server with the MySQL tools today and let our cloud services become your AI agent’s most powerful ally. We’re counting on your feedback - every comment, suggestion, or bug-report helps us build better tools together. Stay tuned: more features and capabilities are on the horizon! Feel free to comment below or write to us with your feedback and queries at AskAzureDBforMySQL@service.microsoft.com.Ignite 2025: Advancing Azure Database for MySQL with Powerful New Capabilities
At Ignite 2025, we’re introducing a wave of powerful new capabilities for Azure Database for MySQL, designed to help organizations modernize, scale, and innovate faster than ever before. From enhanced high availability and seamless serverless integrations to AI-powered insights and greater flexibility for developers, these advancements reflect our commitment to delivering a resilient, intelligent data platform. Join us as we unveil what’s next for MySQL on Azure - and discover how industry leaders are already building the future with confidence. Enhanced Failover Performance with Dedicated SLB for High-Availability Servers We’re excited to announce the General Availability of Dedicated Standard Load Balancer (SLB) for HA-enabled servers in Azure Database for MySQL. This enhancement introduces a dedicated SLB to High Availability configurations for servers created with public access or private link. By managing the MySQL data traffic path, SLB eliminates the need for DNS updates during failover, significantly reducing failover time. Previously, failover relied on DNS changes, which caused delays due to DNS TTL (30 seconds) and client-side DNS caching. What’s new with GA: The FQDN consistently resolves to the SLB IP address before and after failover. Load-balancing rules automatically route traffic to the active node. Removes DNS cache dependency, delivering faster failovers. Note: This feature is not supported for servers using private access with VNet integration. Learn more Build serverless, event-driven apps at scale – now GA with Trigger Bindings for Azure Functions We’re excited to announce the General Availability of Azure Database for MySQL Trigger bindings for Azure Functions, completing the full suite of Input, Output, and Trigger capabilities. This feature lets you build real-time, event-driven applications by automatically invoking Azure Functions when MySQL table rows are created or updated - eliminating custom polling and boilerplate code. With native support across multiple languages, developers can now deliver responsive, serverless solutions that scale effortlessly and accelerate innovation. Learn more Enable AI agents to query Azure Database for MySQL using Azure MCP Server We’re excited to announce that Azure MCP Server now supports Azure Database for MySQL, enabling AI agents to query and manage MySQL data using natural language through the open Model Context Protocol (MCP). Instead of writing SQL, you can simply ask questions like “Show the number of new users signed up in the last week in appdb.users grouped by day.”, all secured with Microsoft Entra authentication for enterprise-grade security. This integration delivers a unified, secure interface for building intelligent, context-aware workflows across Azure services - accelerating insights and automation. Learn more Greater networking flexibility with Custom Port Support Custom port support for Azure Database for MySQL is now generally available, giving organizations the flexibility to configure a custom port (between 25001 and 26000) during new server creation. This enhancement streamlines integration with legacy applications, supports strict network security policies, and helps avoid port conflicts in complex environments. Supported across all network configurations - including public access, private access, and Private Link - custom port provisioning ensures every new MySQL server can be tailored to your needs. The managed experience remains seamless, with all administrative capabilities and integrations working as before. Learn more Streamline migrations and compatibility with Lower Case Table Names support Azure Database for MySQL now supports configuring lower_case_table_names server parameter during initial server creation for MySQL 8.0 and above, ensuring seamless alignment with your organization’s naming conventions. This setting is automatically inherited for restores and replicas, and cannot be modified. Key Benefits: Simplifies migrations by aligning naming conventions and reducing complexity. Enhances compatibility with legacy systems that depend on case-insensitive table names. Minimizes support dependency, enabling faster and smoother onboarding. Learn more Unlock New Capabilities with Private Preview Features at Ignite 2025 We’re excited to announce that you can now explore two powerful capabilities in early access - Reader Endpoint for seamless read scaling and Server Rename for greater flexibility in server management. Scale reads effortlessly with Reader Endpoint (Private Preview) We’re excited to announce that the Reader Endpoint feature for Azure Database for MySQL is now ready for private preview. Reader Endpoint provides a dedicated read-only endpoint for read replicas, enabling automatic connection-based load balancing of read-only traffic across multiple replicas. This simplifies application architecture by offering a single endpoint for read operations, improving scalability and fault tolerance. Azure Database for MySQL supports up to 10 read replicas per primary server. By routing read-only traffic through the reader endpoint, application teams can efficiently manage connections and optimize performance without handling individual replica endpoints. Reader endpoints continuously monitor the health of replicas and automatically exclude any replica that exceeds the configured replication lag threshold or becomes unavailable. To enroll in the preview, please submit your details using this form. Limitations During Private Preview: Only performance-based routing is supported in this preview. Certain settings such as routing method and the option to attach new replicas to the reader endpoint can only be configured at creation time. Only one reader endpoint can be created per replica group. Including the primary server as a fallback for read traffic when no replicas are available is not supported in this preview. Get flexibility in server management with Server Rename (Private Preview) We’re excited to announce the Private Preview of Server Rename for Azure Database for MySQL. This feature lets you update the name of an existing MySQL server without recreating it, migrating data, or disrupting applications - making it easier to adopt clear, consistent naming. It provides a near zero-downtime path to a new hostname of the server. To enroll in the preview, please submit your details using this form. Limitations During Private Preview: Primary server with read replicas: Renaming a primary server that has read replicas keeps replication healthy. However, the SHOW SLAVE STATUS output on the replicas will still display the old primary server's name. This is a display inconsistency only and does not affect replication. Renaming is currently unsupported for servers using Customer Managed Key (CMK) encryption or Microsoft Entra Authentication (Entra Id). Real-World Success: Azure Database for MySQL Powers Resilient Applications at Scale Factorial Factorial, a leading HR software provider, uses Azure Database for MySQL alongside Azure Kubernetes Service to deliver secure, scalable HR solutions for thousands of businesses worldwide. By leveraging Azure Database for MySQL’s reliability and seamless integration with cloud-native technologies, Factorial ensures high availability and rapid innovation for its customers. Learn more YES (Youth Employment Service) South Africa’s largest youth employment initiative, YES, operates at national scale by leveraging Azure Database for MySQL to deliver a resilient, centralized platform for real-time job matching, learning management, and career services - connecting thousands of young people and employers, and helping nearly 45 percent of participants secure permanent roles within six months. Learn more Nasdaq At Ignite 2025, Nasdaq will showcase how it uses Azure Database for MySQL - alongside Azure Database for PostgreSQL and other Azure products - to power a secure, resilient architecture that safeguards confidential data while unlocking new agentic AI capabilities. Learn more These examples demonstrate that Azure Database for MySQL is trusted by industry leaders to build resilient, scalable applications - empowering organizations to innovate and grow with confidence. We Value Your Feedback Azure Database for MySQL is built for scale, resilience, and performance - ready to support your most demanding workloads. With every update, we’re focused on simplifying development, migration, and management so you can build with confidence. Explore the latest features and enhancements to see how Azure Database for MySQL meets your data needs today and in the future. We welcome your feedback and invite you to share your experiences or suggestions at AskAzureDBforMySQL@service.microsoft.com Stay up to date by visiting What's new in Azure Database for MySQL, and follow us on YouTube | LinkedIn | X for ongoing updates. Thank you for choosing Azure Database for MySQL!
