The Pulumi Registry now supports browsing documentation for previous versions of first-party Pulumi providers. If you’ve ever needed to look up the API docs for an older provider version, you no longer have to dig through Git history or guess at changes — the docs are right there in the Registry. These docs also help Pulumi Neo and other agents more accurately assist you with your Pulumi code and operations.
Many developers and platform engineers already use Google accounts daily for email, cloud console access, and collaboration. Until now, signing in to Pulumi Cloud required a GitHub, GitLab, or Atlassian account, or an email/password combination. Today, we’re adding Google as a first-class identity provider, so you can sign in to Pulumi Cloud with the same Google account you already use for everything else.
Your version control provider shouldn’t limit your infrastructure workflows. Pulumi Cloud now works with GitHub, GitHub Enterprise Server, Azure DevOps, and GitLab. Every team gets the same deployment pipelines, PR previews, and AI-powered change summaries regardless of where their code lives.
Pulumi has a lot of engineers. It has marketers, solution architects, developer advocates. Everyone has something to contribute to docs and blog posts — domain expertise, hard-won lessons, real-world examples. What they don’t all have is familiarity with our Hugo setup, our style guide, our metadata conventions, or where a new document is supposed to live in the navigation tree. I joined Pulumi in July 2025 as a Senior Technical Content Engineer. A few weeks in, my sole teammate departed. The docs practice was now, functionally, me.
The problem was clear enough: how do you take one docs engineer’s accumulated knowledge and make it available to everyone who needs it, without that engineer becoming a bottleneck?
I started packaging it. Here’s what that looked like in practice.
In January, we introduced a major performance enhancement for Pulumi Cloud through a fundamental change to how Pulumi manages state that speeds up operations by up to 20x. After a staged rollout across many organizations, it is now enabled by default for every Pulumi Cloud operation. No opt-in required—just use Pulumi CLI v3.225.0+ with Pulumi Cloud. The improvement applies to pulumi up, pulumi destroy, and pulumi refresh; pulumi preview does not modify state, so it is unchanged.
Platform engineering teams managing infrastructure across Terraform and Pulumi now have a way to unify state management without rewriting a single line of HCL. Starting today, Pulumi Cloud can serve as a Terraform state backend, letting you store and manage Terraform state alongside your Pulumi stacks. Your team continues using the Terraform or OpenTofu CLI for day-to-day operations while gaining the benefits of Pulumi Cloud: AI-powered infrastructure management with Pulumi Neo — our infrastructure agent — encrypted state storage, update history, state locking, role-based access control, audit policies, and unified resource visibility through Insights.
When an AI agent writes infrastructure code, two things matter: how compact the output is (token efficiency) and how well the model actually reasons about what it’s writing (cognitive efficiency). HCL produces fewer tokens for the same resource. But does that make it the better choice when agents need to refactor, debug, and iterate? We ran a benchmark across Claude Opus 4.6 and GPT-5.2-Codex to find out.
Pulumi Insights gives you visibility and governance across your entire cloud footprint: discovery scans catalog every resource in your cloud accounts, and policy evaluations continuously enforce compliance against those resources. Until now, Insights workflows ran exclusively on Pulumi-hosted infrastructure. That works well for many teams, but enterprises with strict data residency requirements, private network constraints, or regulatory obligations need to run this work in their own environments. Today, Pulumi Insights supports customer-managed workflow runners for both SaaS Pulumi Cloud and self-hosted Pulumi Cloud installations.
Pulumi Cloud orchestrates a growing number of workflow types: Deployments, Insights discovery scans, and policy evaluations. Some of that work runs on Pulumi’s infrastructure, and some of it runs on yours via customer-managed workflow runners. We needed a scheduling system that could handle all of these workflow types reliably across both environments. In this post, we’ll take a look at the system we built.
When you manage dozens of data-loading pipelines, copying and pasting IaC configurations between them is a recipe for mishap. IAM policies can drift, naming conventions diverge, and every new source is a new opportunity to make a mistake — not to mention compound the problem of duplication. In this post, we’ll show you how you can identify and encapsulate common patterns into composable components and walk through the production lessons we’ve learned running 25+ pipelines for over three years.
