AI chatbots are now essential for businesses, developers, and tech enthusiasts. While OpenAI’s ChatGPT is a powerful hosted solution, it has drawbacks such as ongoing subscription costs, privacy concerns, and limited customization options. What if you could run a powerful AI chatbot on your own infrastructure while leveraging the scalability and reliability of cloud-hosted large language models (LLMs)?

This is where LibreChat comes in. LibreChat is an open-source, self-hosted chatbot interface that provides a familiar ChatGPT-style UI while allowing you to connect to various AI models, including OpenAI's API, Azure OpenAI, and even local LLMs. By integrating LibreChat with Azure OpenAI, you get the best of both worlds—a customizable, private chatbot without the need to invest in expensive local hardware.

Introduction​

Azure OpenAI enables organizations to leverage powerful AI models such as GPT-4o, o3-mini, and Whisper for a variety of use cases, from chatbots to code generation and beyond. However, deploying Azure OpenAI via Infrastructure as Code (IaC) using Bicep requires careful planning to ensure a scalable, cost-effective, and secure deployment.

In this article, we’ll set up a simple lab environment to explore the key concepts behind deploying Azure OpenAI with Bicep. This will help us gain a deeper understanding of the service’s deployment types, regional availability, quota limitations, and model options. By the end, we’ll have a working Azure OpenAI deployment with a few models to experiment with helping us plan more advanced deployments in the future.

Deploying Azure Firewalls with IP Groups and Firewall Policies using Azure Verified Modules (AVM) sounded straightforward—until I hit a wall with Bicep’s nested loop limitations. What followed was a deep dive into dependency chains, AVM quirks, and creative workarounds. Here’s how I tamed the beast, and how you can too.