Building production-ready AI applications shouldn't feel like navigating a minefield of preview APIs and missing Terraform resources.

In the latest Stay In Sync post, we tackle the reality of automating AI Foundry deployments when half the infrastructure doesn't have proper Terraform support yet. From Microsoft's confusing dual architecture for "AI Foundry Projects" (seriously, why are there two completely different things with the same name?) to wrangling OpenAI agents through REST API calls because native resources don't exist.

Every week brings another LinkedIn post about another company's "AI transformation." If you're the one tasked with making this actually happen at your firm, your team is probably asking when you'll have your own AI Platform (or whether you can finally get SecOps to unblock ChatGPT). You know you need to act, but where do you start?

Accidentally deleting a storage account is easier than you think — recovering from it shouldn’t be hard. In this post, we introduce a Terraform module that automatically provisions backups for your Azure Storage Account using a Recovery Services Vault. Infrastructure-as-code meets disaster recovery, the right way.

We built a working multi-cloud ETL pipeline in just a few hours to back up our RFP response—connecting Azure SQL to BigQuery using Cloud Run, GCS, Terraform, and GitHub Actions. This post breaks down how we did it, what we learned, and how this pattern can scale for real-world data workflows.