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.
When asked about his research process, Anthony Bourdain would describe how he’d blend his formal culinary training with deep dives into local food culture - from market stalls to family recipes. Modern AI code generation follows a similar path: it can’t just rely on what it knows - it must tap into continuously evolving, domain-specific knowledge bases. Just as Bourdain would combine his classical French training with techniques learned from local kitchens, AI code generators blend their built-in knowledge with retrieved code snippets and type definitions to generate code that accurately represents the user’s intent.
We built Pulumi Copilot to automate a broad spectrum of cloud management activities using the power of LLMs. Since its initial release earlier this year, hundreds of customers have used Pulumi Copilot to understand and manage cloud infrastructure more effectively and securely, and it is only getting better by the day.
Today, we’re excited to announce the availability of the Pulumi Copilot REST API. This new API exposes the full power of Pulumi Copilot, enabling you to integrate infrastructure AI into your own tools, applications, and platforms. While currently in preview, we are eager to get your feedback to ensure it works for anything you can dream up.