Today we’re excited to announce Azure Native Provider v3.8, featuring several enhancements that simplify authentication and extend support to private Azure environments. These updates make it easier than ever to manage Azure infrastructure using credentials provided by the hosting environment, such as in Azure Kubernetes Service (AKS), Azure VM, and Azure Cloud Shell.
Azure Resource Manager (ARM) templates are powerful, but painful. If you’ve ever tried to provision cloud infrastructure using ARM, you know the challenges:
ARM wasn’t built for the complexity of modern Azure workloads. If you’re already familiar with general-purpose languages, there’s a better path: Pulumi.
Infrastructure as Code (IaC) has evolved beyond simple automation into a fundamental shift toward applying software engineering practices to infrastructure management. In 2025, leading organizations aren’t just provisioning infrastructure—they’re treating it as software, complete with testing, version control, code reviews, and continuous integration.
Pulumi Azure Native V3 is the most comprehensive infrastructure as code (IaC) solution for Microsoft Azure, combining full resource coverage with dramatic improvements in performance and developer experience.
With V3, the Pulumi Azure Native SDK is 75% smaller while maintaining 100% coverage of the Azure Resource Manager (ARM) API. This makes it the most efficient and complete way to define and manage Azure infrastructure with real programming languages.
In this post, we’re introducing a powerful new capability in Pulumi Insights that extends policy as code (PaC) beyond infrastructure as code to automatically govern all cloud resources in your environment. By unifying policy enforcement across both IaC and discovered resources, you can now write policies once and apply them universally - dramatically simplifying how organizations maintain security and compliance standards at scale.
When should you use Cosmos DB on Azure? It’s a hard question to answer.
Azure Cosmos DB tries to do it all - which makes it both powerful and, honestly, kind of confusing. The marketing doesn’t help either. Yeah, it’s a flexible database, but before you jump in, you need to understand how it actually works under the hood, what it’ll cost you, and what trade-offs you’re making by choosing it.
I’m going to break all that down. Here’s a roadmap:
The latest major release of the Azure Classic Pulumi Provider is available now! Our 6.0 release contains the latest upstream changes to keep you up-to-date along with the latest features and improvements from Pulumi.
The Azure Classic provider is derived from the upstream Terraform AzureRM provider and can be used to provision most resources on Azure. The Azure Classic provider can be used to complement the Pulumi Azure Native provider, our recommended provider, which has amazing resource coverage due to being derived directly from the official specifications. The provider is open source and available on GitHub so you can always follow along with current issues and developments, or even open your first pull request.
We are excited to announce the support for authoring Azure Deployment Environments (ADE) environment definitions in Pulumi Infrastructure as Code (IaC) empowering developers to self-serve app infrastructure required to deploy and test cloud-based applications. With Pulumi support, you can now manage your Azure resources in these environments using the same familiar programming model and the full power of our IaC platform.
Bicep is a DSL developed by Microsoft to simplify the authoring of ARM templates and deploy resources to Azure. Today I will be sharing with you a new Pulumi converter plugin that I have been working on that converts Bicep code to any of the supported Pulumi languages.
The AI industry is stealing the show as tech’s goldrush of the ’20s. Just looking at ChatGPT’s record setting user growth, and rapid 3rd party integration by top brands, it is not surprising the hype suggests this is the beginning of a major digital transformation.
However, using AI/ML in your own products has some major challenges and obstacles. Below is a diagram of the end to end workflow of building and using an AI model: preparing the data, training a model, fine-tuning a model, hosting and running a model, building a backend service to serve the model, and building the user interface that interacts with the model. Most AI engineers are only involved in a few steps of the process. However, there is one challenge that is common across the entire workflow: creating and managing the cloud infrastructure is hard.