August 2 2026 • Infrastructure Update
When I started AI Data Lab, everything ran directly on my laptop. It was the fastest way to prototype ideas, experiment with AI, and iterate quickly. As the project evolved into a real platform with multiple services, web interfaces, and cloud AI integration, it became clear that the next step was moving to Microsoft Azure.
This migration was not only about hosting the application online—it was about building a scalable foundation for future AI agents and enterprise-grade features.
Why move to Azure?
Running everything on a local machine worked well during early development, but it also introduced several limitations:
- The application was only available while my laptop was powered on.
- Local networking complicated remote access and testing.
- Database backups depended on manual processes.
- Scaling the application for multiple users wasn’t practical.
- Integrating additional cloud services required extra infrastructure.
Moving to Azure solved these problems while preparing the project for future growth.
New Cloud Architecture
The current platform is built around Azure services that separate application logic, storage, databases, and AI capabilities.
Core components
- Azure App Service
- Hosts the FastAPI backend
- Automatically manages the web server
- Supports continuous deployment from GitHub
- Azure Database for MySQL
- Replaces the local MySQL instance
- Automatic backups
- Built-in high availability
- Managed updates and maintenance
- Azure OpenAI
- Natural language processing
- SQL generation
- AI reasoning
- Future AI agents
- Azure Blob Storage
- File uploads
- Generated reports
- Future document processing
- GitHub Actions
- Automatic deployment
- CI/CD pipeline
- Version-controlled releases
- Custom Domain & HTTPS
- Secure access through vykhodtsev.dev
- SSL certificate managed by Azure