Applying DevOps Principles on Lean Infrastructure. Lessons From Scaling to 102K Users.
Hi Azure Community, I'm a Microsoft Certified DevOps Engineer, and I want to share an unusual journey. I have been applying DevOps principles on traditional VPS infrastructure to scale to 102,000 users with 99.2% uptime. Why am I posting this in an Azure community? Because I'm planning migration to Azure in 2026, and I want to understand: What mistakes am I already making that will bite me during migration? THE CURRENT SETUP Platform: Social commerce (West Africa) Users: 102,000 active Monthly events: 2 million Uptime: 99.2% Infrastructure: Single VPS Stack: PHP/Laravel, MySQL, Redis Yes - one VPS. No cloud. No Kubernetes. No microservices. WHY I HAVEN'T USED AZURE YET Honest answer: Budget constraints in emerging market startup ecosystem. At our current scale, fully managed Azure services would significantly increase monthly burn before product-market expansion. The funding we raised needs to last through growth milestones. The trade: I manually optimize what Azure would auto-scale. I debug what Application Insights would catch. I do by hand what Azure Functions would automate. DEVOPS PRACTICES THAT KEPT US RUNNING Even on single-server infrastructure, core DevOps principles still apply: CI/CD Pipeline (GitHub Actions) • 3-5 deployments weekly • Zero-downtime deploys • Automated rollback on health check failures • Feature flags for gradual rollouts Monitoring & Observability • Custom monitoring (would love Application Insights) • Real-time alerting • Performance tracking and slow query detection • Resource usage monitoring Automation • Automated backups • Automated database optimization • Automated image compression • Automated security updates Infrastructure as Code • Configs in Git • Deployment scripts • Environment variables • Documented procedures Testing & Quality • Automated test suite • Pre-deployment health checks • Staging environment • Post-deployment verification KEY OPTIMIZATIONS Async Job Processing • Upload endpoint: 8 seconds → 340ms • 4x capacity increase Database Optimization • Feed loading: 6.4 seconds → 280ms • Strategic caching • Batch processing Image Compression • 3-8MB → 180KB (94% reduction) • Critical for mobile users Caching Strategy • Redis for hot data • Query result caching • Smart invalidation Progressive Enhancement • Server-rendered pages • 2-3 second loads on 4G WHAT I'M WORRIED ABOUT FOR AZURE MIGRATION This is where I need your help: Architecture Decisions • App Service vs Functions + managed services? • MySQL vs Azure SQL? • When does cost/benefit flip for managed services? Cost Management • How do startups manage Azure costs during growth? • Reserved instances vs pay-as-you-go? • Which Azure services are worth the premium? Migration Strategy • Lift-and-shift first, or re-architect immediately? • Zero-downtime migration with 102K active users? • Validation approach before full cutover? Monitoring & DevOps • Application Insights - worth it from day one? • Azure DevOps vs GitHub Actions for Azure deployments? • Operational burden reduction with managed services? Development Workflow • Local development against Azure services? • Cost-effective staging environments? • Testing Azure features without constant bills? MY PLANNED MIGRATION PATH Phase 1: Hybrid (Q1 2026) • Azure CDN for static assets • Azure Blob Storage for images • Application Insights trial • Keep compute on VPS Phase 2: Compute Migration (Q2 2026) • App Service for API • Azure Database for MySQL • Azure Cache for Redis • VPS for background jobs Phase 3: Full Azure (Q3 2026) • Azure Functions for processing • Full managed services • Retire VPS QUESTIONS FOR THIS COMMUNITY Question 1: Am I making migration harder by waiting? Should I have started with Azure at higher cost to avoid technical debt? Question 2: What will break when I migrate? What works on VPS but fails in cloud? What assumptions won't hold? Question 3: How do I validate before cutting over? Parallel infrastructure? Gradual traffic shift? Safe patterns? Question 4: Cost optimization from day one? What to optimize immediately vs later? Common cost mistakes? Question 5: DevOps practices that transfer? What stays the same? What needs rethinking for cloud-native? THE BIGGER QUESTION Have you migrated from self-hosted to Azure? What surprised you? I know my setup isn't best practice by Azure standards. But it's working, and I've learned optimization, monitoring, and DevOps fundamentals in practice. Will those lessons transfer? Or am I building habits that cloud will expose as problematic? Looking forward to insights from folks who've made similar migrations. --- About the Author: Microsoft Certified DevOps Engineer and Azure Developer. CTO at social commerce platform scaling in West Africa. Preparing for phased Azure migration in 2026. P.S. I got the Azure certifications to prepare for this migration. Now I need real-world wisdom from people who've actually done it!
Scaling Smart with Azure: Architecture That Works
Hi Tech Community! I’m Zainab, currently based in Abu Dhabi and serving as Vice President of Finance & HR at Hoddz Trends LLC a global tech solutions company headquartered in Arkansas, USA. While I lead on strategy, people, and financials, I also roll up my sleeves when it comes to tech innovation. In this discussion, I want to explore the real-world challenges of scaling systems with Microsoft Azure. From choosing the right architecture to optimizing performance and cost, I’ll be sharing insights drawn from experience and I’d love to hear yours too. Whether you're building from scratch, migrating legacy systems, or refining deployments, let’s talk about what actually works.Comparision on Azure Cloud Sync and Traditional Entra connect Sync.
Introduction In the evolving landscape of identity management, organizations face a critical decision when integrating their on-premises Active Directory (AD) with Microsoft Entra ID (formerly Azure AD). Two primary tools are available for this synchronization: Traditional Entra Connect Sync (formerly Azure AD Connect) Azure Cloud Sync While both serve the same fundamental purpose, bridging on-prem AD with cloud identity, they differ significantly in architecture, capabilities, and ideal use cases. Architecture & Setup Entra Connect Sync is a heavyweight solution. It installs a full synchronization engine on a Windows Server, often backed by SQL Server. This setup gives administrators deep control over sync rules, attribute flows, and filtering. Azure Cloud Sync, on the other hand, is lightweight. It uses a cloud-managed agent installed on-premises, removing the need for SQL Server or complex infrastructure. The agent communicates with Microsoft Entra ID, and most configurations are handled in the cloud portal. For organizations with complex hybrid setups (e.g., Exchange hybrid, device management), is Cloud Sync too limited?
Azure NSG Challenge : When NIC and Subnet Rules Collide
Imagine this real-world scenario: 🔹 A VM needs to connect outbound via RDP (TCP 3389) to an external server for management. 🔹 The NIC-level NSG allows outbound RDP, ensuring the VM can initiate connections. 🔹 However, the Subnet-level NSG has an inbound deny rule specifically for RDP. 💭 Question for IT Pros: 👉 Would the outbound RDP session succeed or be blocked due to the subnet-level NSG? 👉 How do you design NSG rules to prevent misconfigurations while maintaining security? ####################################################### Great challenge! Let's break it down: 🚦 Would the outbound RDP session succeed or be blocked? The outbound RDP session would succeed because the subnet-level NSG applies to inbound traffic coming into the subnet, not traffic leaving the VM. Since outbound RDP is explicitly allowed at the NIC level, the VM can initiate connections without issue. However, if the external server tries to respond back, the inbound deny rule at the subnet level would block the return traffic. This effectively disrupts the session, making it seem like the connection failed. 🔒 How to design NSG rules effectively? To prevent misconfigurations while maintaining security: 1- Understand NSG processing – Rules are evaluated independently at the NIC and Subnet levels, but both must allow the required traffic. 2- Use least privilege principles – Only allow necessary traffic and explicitly deny everything else. 3- Be careful with inbound rules at the subnet level – Blocking inbound traffic here can unintentionally interfere with legitimate outbound sessions. 4- Log traffic flows with NSG Flow Logs – Use diagnostic settings to capture insights for troubleshooting. 5- Consider Application Security Groups (ASGs) – These simplify NSG management by grouping resources dynamically.Azure VM Networking Components Real Case Scenario
📌 Public IP 📌 🔹 Public IPs allow internet-based services to reach Azure resources, such as web applications hosted on VMs or Azure App Services. 🔹 Azure resources can use Public IPs to communicate with external services, ensuring connectivity for APIs, databases, and other cloud-based applications. 🔹 Public IPs can be assigned as static (fixed address) or dynamic (changes over time). Static IPs are ideal for services requiring a consistent address, while dynamic IPs are useful for temporary workloads. 📌 Azure Load Balancer (External / Internal) 📌 🔹 Distributes Internet Traffic – Balances incoming requests from the internet across multiple backend resources. 🔹 Balances Private Network Traffic – Distributes requests within an Azure Virtual Network (VNet). 🔹 Supports Multi-Tier Architectures – Ideal for backend services like databases and application layers. 🔹 Enhances Availability – Ensures high availability by routing traffic to healthy instances. 🔹 Provides Outbound Connectivity – Enables Azure VMs to communicate with external services using NAT. 📌 VNET Subnets Segmentation 📌 🔹 Web Subnet – Contains two VMs, each with a Network Interface Card (NIC) and is protected by a Network Security Group (NSG) to filter traffic based on rules. 🔹 App Subnet – Similar to the Web Subnet, hosting two VMs with NICs and NSGs, but uses an internal load balancer to balance traffic within the subnet. 🔹 Data Subnet – Also includes two VMs with NICs and NSGs, leveraging an internal load balancer for optimized traffic management. 🔹 Gateway Subnet – Hosts the VPN Gateway, ensuring connectivity between on-premises networks and Azure. 📌 Azure Network Security Groups (NSGs)📌 🔹 Traffic Filtering – NSGs allow or deny inbound and outbound traffic based on defined security rules. 🔹 Granular Control – Rules can be applied at the subnet or network interface level for precise traffic management. 🔹 Default Security Rules – Azure provides built-in rules to ensure basic security, which can be overridden with custom rules. 🔹 Priority-Based Processing – Rules are evaluated in order of priority (100-4096), with lower numbers processed first. 🔹 Supports Service Tags – Simplifies rule management by using predefined tags like Internet, VirtualNetwork, and AzureLoadBalancer. 📌 Azure VPN Gateway 📌 🔹 Secure Connectivity – Establishes encrypted connections between Azure Virtual Networks (VNets) and on-premises networks. 🔹 Site-to-Site VPN – Enables secure communication between an on-premises network and Azure using IPsec/IKE VPN tunnels. 🔹 Point-to-Site VPN – Allows individual devices to securely connect to Azure from remote locations using OpenVPN, IKEv2, or SSTP. 🔹 VNet-to-VNet Connectivity – Facilitates secure communication between multiple Azure VNets. 🔹 ExpressRoute Failover – Provides a backup connection for ExpressRoute in case of failure. 🔹 High Availability – Supports active-active configurations for redundancy and reliability. If you found this valuable, consider sharing so more professionals can benefit. Let's keep the conversation growing! 🚀🔥The Power of Azure’s Security Arsenal 🔥
◆ Using a Public IP without securing your Azure applications and resources exposes you to security threats. Today, we’ll explore the most powerful security solutions from Azure’s arsenal. ◆ Azure provides a multi-layered approach (more than one layer of protection) to secure your resources when using a Public IP. Organizations can now transform this open gateway into a fortified checkpoint. Here’s how these tools work together to mitigate risks: 🚀 Azure DDoS Protection 🚀 ■ Protects your resources and services from being overwhelmed by malicious traffic. This excellent service is available for Network & IP Protection SKUs. ■ Uses Machine Learning to distinguish between normal traffic patterns and malicious flooding attempts (such as SYN floods or UDP amplification attacks) before they impact your applications and services ensuring availability. 🚀 Azure Web Application Firewall (WAF) 🚀 ■ Adds application-layer protection, intercepting HTTP/HTTPS traffic for inspection. ■ Blocks suspicious attacks like SQL injection or XSS by applying OWASP core rule sets, which define how attacks occur and how to defend against them, with continuous updates. ■ Enhances security for customer-facing services, ensuring trust and protection for your website and users. 🚀 Network Security Groups (NSGs) 🚀 ■ Acts as a virtual firewall at the subnet or network interface level, filtering traffic based on predefined rules. ■ Can allow only trusted HTTPS (port 443) connections while blocking unsolicited RDP or SSH attempts. ■ Implements the critical security principle of reducing attack surface, ensuring only authorized traffic reaches your target resources. 🚀 Azure Private Link 🚀 ■ In some scenarios, avoiding Public IPs altogether is the best security approach. This powerful service allows secure access to Azure SQL Database or Storage via Private Endpoints inside your virtual network. ■ Helps organizations minimize external exposure while maintaining secure, private connections to necessary services. 🚀 Azure Bastion 🚀 ■ Provides secure access to Azure VMs without Public IPs, using RDP/SSH over encrypted TLS 1.2 traffic. ■ Uses a browser-based HTML5 web client to establish RDP/SSH sessions over TLS on port 443, fully compatible with any firewall. ■ Connects to VMs via Private IPs while enforcing NSG rules to allow access only through Azure Bastion. If you found this valuable, consider sharing so more professionals can benefit. Let's keep the conversation growing! 🚀🚀 Mastering Azure Management with Global Admin Elevation 🌐
◆ Microsoft Entra ID and Azure resources are secured independently from one another. ◆ Microsoft Entra role assignments do not grant access to Azure resources. ◆ Azure role assignments do not grant access to Microsoft Entra ID. ◆ As a Global Administrator in Microsoft Entra ID, you can assign yourself access to all Azure subscriptions and management groups in your tenant. ◆ Use this capability if you don't have access to Azure subscription resources, such as virtual machines or storage accounts, and you want to use your Global Administrator privilege to gain access to those resources. ◆ When you elevate your access, you are assigned the User Access Administrator role in Azure at root scope (/). This allows you to view all resources and assign access in any subscription or management group in the tenant. ◆ User Access Administrator role assignments can be removed using Azure PowerShell, Azure CLI, or the REST API. 🚀 Why would you need to elevate your access? If you are a Global Administrator, there might be times when you want to do the following actions: ■ Regain access to an Azure subscription or management group when a user has lost access ■ Grant another user or yourself access to an Azure subscription or management group ■ See all Azure subscriptions or management groups in an organization ■ Allow an automation app (such as an invoicing or auditing app) to access all Azure subscriptions or management groups # Perform steps at root scope # Follow these steps to elevate access for a Global Administrator using the Azure portal. (1) Sign in to the Azure portal as a Global Administrator. Note : If you are using Microsoft Entra Privileged Identity Management, activate your Global Administrator role assignment !! (2) Browse to Microsoft Entra ID > Manage > Properties. (3) Under Access management for Azure resources, set the toggle to Yes. (4) Select Save to save your setting. If you found this valuable, consider sharing so more professionals can benefit. Let's keep the conversation growing! 🚀Building an AI-Powered ESG Consultant Using Azure AI Services: A Case Study
In today's corporate landscape, Environmental, Social, and Governance (ESG) compliance has become increasingly important for stakeholders. To address the challenges of analyzing vast amounts of ESG data efficiently, a comprehensive AI-powered solution called ESGai has been developed. This blog explores how Azure AI services were leveraged to create a sophisticated ESG consultant for publicly listed companies. https://youtu.be/5-oBdge6Q78?si=Vb9aHx79xk3VGYAh The Challenge: Making Sense of Complex ESG Data Organizations face significant challenges when analyzing ESG compliance data. Manual analysis is time-consuming, prone to errors, and difficult to scale. ESGai was designed to address these pain points by creating an AI-powered virtual consultant that provides detailed insights based on publicly available ESG data. Solution Architecture: The Three-Agent System ESGai implements a sophisticated three-agent architecture, all powered by Azure's AI capabilities: Manager Agent: Breaks down complex user queries into manageable sub-questions containing specific keywords that facilitate vector search retrieval. The system prompt includes generalized document headers from the vector database for context. Worker Agent: Processes the sub-questions generated by the Manager, connects to the vector database to retrieve relevant text chunks, and provides answers to the sub-questions. Results are stored in Cosmos DB for later use. Director Agent: Consolidates the answers from the Worker agent into a comprehensive final response tailored specifically to the user's original query. It's important to note that while conceptually there are three agents, the Worker is actually a single agent that gets called multiple times - once for each sub-question generated by the Manager. Current Implementation State The current MVP implementation has several limitations that are planned for expansion: Limited Company Coverage: The vector database currently stores data for only 2 companies, with 3 documents per company (Sustainability Report, XBRL, and BRSR). Single Model Deployment: Only one GPT-4o model is currently deployed to handle all agent functions. Basic Storage Structure: The Blob container has a simple structure with a single directory. While Azure Blob storage doesn't natively support hierarchical folders, the team plans to implement virtual folders in the future. Free Tier Limitations: Due to funding constraints, the AI Search service is using the free tier, which limits vector data storage to 50MB. Simplified Vector Database: The current index stores all 6 files (3 documents × 2 companies) in a single vector database without filtering capabilities or schema definition. Azure Services Powering ESGai The implementation of ESGai leverages multiple Azure services for a robust and scalable architecture: Azure AI Services: Provides pre-built APIs, SDKs, and services that incorporate AI capabilities without requiring extensive machine learning expertise. This includes access to 62 pre-trained models for chat completions through the AI Foundry portal. Azure OpenAI: Hosts the GPT-4o model for generating responses and the Ada embedding model for vectorization. The service combines OpenAI's advanced language models with Azure's security and enterprise features. Azure AI Foundry: Serves as an integrated platform for developing, deploying, and governing generative AI applications. It offers a centralized management centre that consolidates subscription information, connected resources, access privileges, and usage quotas. Azure AI Search (formerly Cognitive Search): Provides both full-text and vector search capabilities using the OpenAI ada-002 embedding model for vectorization. It's configured with hybrid search algorithms (BM25 RRF) for optimal chunk ranking. Azure Storage Services: Utilizes Blob Storage for storing PDFs, Business Responsibility Sustainability Reports (BRSRs), and other essential documents. It integrates seamlessly with AI Search using indexers to track database changes. Cosmos DB: Employs MongoDB APIs within Cosmos DB as a NoSQL database for storing chat history between agents and users. Azure App Services: Hosts the web application using a B3-tier plan optimized for cost efficiency, with GitHub Actions integrated for continuous deployment. Project Evolution: From Concept to Deployment The development of ESGai followed a structured approach through several phases: Phase 1: Data Cleaning Extracted specific KPIs from XML/XBRL datasets and BRSR reports containing ESG data for 1,000 listed companies Cleaned and standardized data to ensure consistency and accuracy Phase 2: RAG Framework Development Implemented Retrieval-Augmented Generation (RAG) to enhance responses by dynamically fetching relevant information Created a workflow that includes query processing, data retrieval, and response generation Phase 3: Initial Deployment Deployed models locally using Docker and n8n automation tools for testing Identified the need for more scalable web services Phase 4: Transition to Azure Services Migrated automation workflows from n8n to Azure AI Foundry services Leveraged Azure's comprehensive suite of AI services, storage solutions, and app hosting capabilities Technical Implementation Details Model Configurations: The GPT model is configured with: Model version: 2024-11-20 Temperature: 0.7 Max Response Token: 800 Past Messages: 10 Top-p: 0.95 Frequency/Presence Penalties: 0 The embedding model uses OpenAI-text-embedding-Ada-002 with 1536 dimensions and hybrid semantic search (BM25 RRF) algorithms. Cost Analysis and Efficiency A detailed cost breakdown per user query reveals: App Server: $390-400 AI Search: $5 per query RAG Query Processing: $4.76 per query Agent-specific costs: Manager: $0.05 (30 input tokens, 210 output tokens) Worker: $3.71 (1500 input tokens, 1500 output tokens) Director: $1.00 (600 input tokens, 600 output tokens) Challenges and Solutions The team faced several challenges during implementation: Quota Limitations: Initial deployments encountered token quota restrictions, which were resolved through Azure support requests (typically granted within 24 hours). Cost Optimization: High costs associated with vectorization required careful monitoring. The team addressed this by shutting down unused services and deploying on services with free tiers. Integration Issues: GitHub Actions raised errors during deployment, which were resolved using GitHub's App Service Build Service. Azure UI Complexity: The team noted that Azure AI service naming conventions were sometimes confusing, as the same name is used for both parent and child resources. Free Tier Constraints: The AI Search service's free tier limitation of 50MB for vector data storage restricts the amount of company information that can be included in the current implementation. Future Roadmap The current implementation is an MVP with several areas for expansion: Expand the database to include more publicly available sustainability reports beyond the current two companies Optimize token usage by refining query handling processes Research alternative embedding models to reduce costs while maintaining accuracy Implement a more structured storage system with virtual folders in Blob storage Upgrade from the free tier of AI Search to support larger data volumes Develop a proper schema for the vector database to enable filtering and more targeted searches Scale to multiple GPT model deployments for improved performance and redundancy Conclusion ESGai demonstrates how advanced AI techniques like Retrieval-Augmented Generation can transform data-intensive domains such as ESG consulting. By leveraging Azure's comprehensive suite of AI services alongside a robust agent-based architecture, this solution provides users with actionable insights while maintaining scalability and cost efficiency. https://youtu.be/5-oBdge6Q78?si=Vb9aHx79xk3VGYAhTroubleshooting Azure Function App Proxy with Private Blob Container Access for Static Web App
Recently, I shared a problem I’m facing in my testing environment with a friend. I’ve decided to bring this issue to an open forum discussion to gather additional insights. I hope you can help me figure out what might be missing in my configuration. **Context:** I’m trying to replicate a solution in my test environment but encountering difficulties in a specific scenario. **Scenario:** I have a Function App acting as a proxy for a Static Web App hosted in a Blob Container. This Blob Container is set to private access, meaning public access is disabled. **The Problem:** The goal is for my Function App to authorize users and direct them correctly to the Static Web App. However, it’s not working as expected. **What I’ve tried so far:** 1. Configured Managed Identity for the Function App and granted the necessary permissions to the Blob Container. 2. Properly set up authentication and created the App Registration, which works flawlessly. 3. Verified that the proxy functions correctly when the Blob Container’s public access is enabled. **Current behavior:** - When public access to the Blob Container is enabled, everything works fine. - When public access is disabled, even with the proxy configured, access fails, and an error message "resource not found" is returned. **My questions are:** 1. Do I need to configure something additional in the proxy definition file? 2. Is there a specific setting, like a private endpoint or something similar, that I should implement to resolve this issue? **Additional considerations:** I haven’t configured a private endpoint yet, but I’m considering whether this would be the most appropriate solution for my case. My initial expectation was that granting the necessary permissions to the Function App via Managed Identity would solve the issue, but it hasn’t. I appreciate any guidance or suggestions you can provide!
