Pulumi Cloud helps teams manage and operate their cloud infrastructure in one place, from state and secrets to deployments, visibility, and policy enforcement.
For a long time, one request has consistently come up from the Pulumi community: dark mode. Today, we’re announcing that this request is now available in Pulumi Cloud.
Managing credentials in CI/CD pipelines has always involved tradeoffs. Long-lived access tokens are convenient but create security risks when they leak or fall into the wrong hands. Short-lived credentials are more secure but require additional tooling to obtain and manage. Today, we’re eliminating this tradeoff with native OIDC token exchange support in the Pulumi CLI.
Every developer building AI agents knows the gap between a working prototype and production deployment. Your fraud detection agent works perfectly on your laptop, but how do you deploy it with proper authentication, memory persistence, observability, and guardrails? This post walks through a complete journey from local development to production-ready AI agents using Amazon Bedrock AgentCore, the Strands SDK, and Pulumi.
The IDE is dying, and so is tool calling. OpenAI is not going to win. And next year, you’re going to be shipping code that you’ve never reviewed before, even as an experienced engineer.
These are bold claims, but the way we use AI in 2026 for coding and agents is going to look completely different. In this post, I want to cover my predictions and why they matter right now for DevOps engineers. Some of these are definitely hot takes, but that’s what makes this conversation worth having.
We’ve been in the infrastructure business for nearly a decade, and we’ve never been more excited about, or in awe of, the scale we are seeing as the industry pursues superintelligence. We are now hitting a tipping point that requires entirely different approaches to managing and scaling infrastructure in this new era.
What do we mean by superintelligence? Superintelligence means AI systems that operate with genuine autonomy—planning, reasoning, executing, adapting—at scale, on the path toward human-level and eventually superhuman intelligence. The infrastructure needed to accomplish this is greater than anything we’ve ever seen. Jensen Huang projects $600 billion in AI infrastructure spending this year, scaling to $3-4 trillion by decade’s end. Stargate committed $500 billion to AI infrastructure in the U.S. Microsoft, Meta, and Google are each spending $70-90 billion annually on datacenters. AWS just activated Project Rainier, a data center scaling to one million custom Trainium chips for Anthropic’s frontier models.
Superintelligence is driving the biggest, fastest infrastructure scaling period in the history of computing. This is exciting but comes with challenges: all of that infrastructure has to be managed, secured, scaled, made compliant, and cost effective. Legacy infrastructure tools weren’t built for this reality—they add friction that slows progress or breaks it altogether.
This reveals an important insight:
AWS re:Invent 2025 delivered a myriad of announcements across AI, silicon, and cloud infrastructure. AWS unveiled the expanded Nova model family, introduced Nova Forge for custom model training, launched Trainium3 UltraServers, and added major production features to AgentCore. It was a lot, and taken at face value, it looks like another scattershot year of big releases.
But if you look past the firehose, a pattern emerges. These announcements fit together into a single bet about how enterprise AI will be built.
Every organization builds up knowledge over time: naming standards, compliance requirements, patterns your team has settled on, and proven approaches to common tasks. Until now, bringing this knowledge into Neo meant repeating it manually each time - specifying preferences, describing how your team works, and recreating prompts that someone already perfected.
Two new features change this. Custom Instructions teach Neo your standards so it applies them automatically. Slash Commands capture proven prompts so anyone on your team can use them with a keystroke.
The magic of Pulumi is that we rarely have to worry about the fine details of how our deployment and infrastructure management works, allowing us to focus instead on what we want. If our program declares an S3 bucket, Pulumi handles creation, updates, and deletion automatically.
Most of the time, this is exactly what we want. However, some use cases require finer-grained control over resource dependencies. Today, we’re introducing the replaceWith resource option, a new feature that gives you explicit control over replacement dependencies between resources.
One of Pulumi’s foundational benefits is that it allows you to manage your infrastructure as software with rich programming languages, robust testing, and CI/CD patterns that you’d use with your application code. This post will cover applying another classic software development technique to your infrastructure: feature flagging. You can use feature flags to control change rollout, reduce the risk of new releases, and speed up the development of your infrastructure, the same way you do with your applications.
The examples in this post range from simply creating a flag and using it in a Lambda function to fully integrating with LaunchDarkly to build a comprehensive flagging system for your infrastructure.
In 2026, several trends will dominate cloud computing, driving innovation, efficiency, and scalability. From Infrastructure as Code (IaC) to AI/ML, platform engineering to multi-cloud and hybrid strategies, and security practices, let’s explore the 10 biggest emerging trends.
