3 posts tagged with "bicep"

There has been a lot of talk about OpenClaw lately.

As an Azure Platform Engineer, I wanted to deploy it myself and understand what OpenClaw is about, why there is so much hype, and what the real security considerations look like in practice.

This walkthrough covers a GitOps-style deployment of an MVP-level architecture for a lab environment, using Azure Verified Modules (AVM) and GitHub Actions.

⚠️ Important: this is a lab/MVP deployment for learning and experimentation, not a production reference architecture. In my lab setup, I restricted inbound access to a single trusted source IP using Container Apps ingress access restrictions (firewall behavior).

This post captures a clean, repeatable way to provision and deploy an Azure Static Web App (SWA) with GitHub Actions. The goal was simple: make the pipeline idempotent and easy to re-run in a new subscription without hand edits or stale secrets.

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.