Ask a generic LLM to “fix my broken deployment,” and you’ll get generic advice. Ask Pulumi Neo the same question, and you’ll get a fix plan grounded in your actual infrastructure state.
The difference isn’t about better prompts or newer models. It’s about what the AI actually knows. Generic LLMs have been trained on the internet. Neo has been trained on your infrastructure.
Neo just got significantly more capable. We’ve shipped three major updates: Operating Modes for flexible control, full ecosystem tool access, and Claude Sonnet 4.5 for better performance on complex infrastructure tasks.
We’re excited to announce the Pulumi Remote MCP Server—a hosted service that brings AI-powered infrastructure management to any AI assistant that supports the Model Context Protocol. Connect your favorite AI assistant to https://mcp.ai.pulumi.com/mcp and instantly access your Pulumi Cloud infrastructure, search resources across stacks, and delegate complex automation tasks to Pulumi Neo.
Since launching Pulumi Neo two weeks ago, we’ve seen platform teams discover creative ways to put their newest AI teammate to work. We have also been using Neo internally for a handful of use cases. Neo shifts the conversation from “what could AI do for infrastructure?” to “what can I actually accomplish with Neo today?”
The answer is quite a bit. Here are 10 concrete workflows that platform teams can use Neo for right now, each one designed to save hours of manual work while keeping humans in the driver seat.
Pulumi’s Infrastructure as Code has included a powerful policy engine from day one. Over the past year, we’ve been enhancing it significantly to provide stronger governance for modern cloud platforms. Until now, these capabilities were limited to our Business Critical tier. Today, we’re excited to announce that policy guardrails are now available to all Team and Enterprise customers. Alongside this, we’re launching a redesigned policy management experience and introducing out-of-the-box policy packs that make it easier than ever to secure, govern, and optimize your cloud environments—even when powered by AI agents like Pulumi Neo.
AI coding assistants have transformed the speed at which developers can write and deploy code. Pull request velocity has increased significantly. Feature delivery has accelerated beyond what we thought possible just two years ago. This should be a victory for everyone in the software organization.
Instead, it’s created significant challenges for infrastructure and platform teams.
We’re excited to announce the new AI capabilities for Pulumi CLI powered by Pulumi Copilot that translate complex infrastructure changes into clear, human-readable explanations and help resolve deployment errors with actionable guidance. Enable these preview features with the --copilot flag.
Infrastructure as Code (IaC) has revolutionized how we manage cloud resources, but navigating complex cloud provider APIs, writing boilerplate code, and iterating through deployment cycles can still be time-consuming. Pulumi offers a fantastic developer experience using familiar programming languages. But what if we could make it even faster and more intuitive by integrating powerful AI assistants directly into the development loop?
Kubernetes has transformed cloud infrastructure by enabling scalable, containerized applications. While it initially gained traction for managing web applications and microservices, its capabilities now extend to AI/ML workloads, making it the go-to platform for data scientists and machine learning engineers.
Running AI/ML workloads on Kubernetes presents unique challenges, including:
Google Cloud Kubernetes (GKE) provides a robust foundation for AI/ML workloads, but managing infrastructure manually can be cumbersome. This is where Pulumi comes in—enabling Infrastructure as Code (IaC) to automate and simplify AI/ML infrastructure on Kubernetes.
This weekend, my “for you” page on all of my social media accounts was filled with only one thing: DeepSeek. DeepSeek really managed to shake up the AI community with a series of very strong language models like DeepSeek R1.