Adam Gordon Bell

In previous articles, we looked at how platform engineering fixes infrastructure chaos, enables self-service, and improves developer workflows. These pillars work together to boost both developer productivity and organizational speed.

But there’s still one critical element that can make or break all this progress: security.

Traditional security efforts — even “shift-left” initiatives — often create friction instead of clearing the way for innovation. Embedding security directly into your platform changes that. By weaving in policy-as-code, centralized secrets management, and identity-based authentication, you turn security from a blocker into an enabler. And with the right metrics, you can measure how well your platform balances protection and speed.

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In the last article in this Platform Engineering Pillars series, we explored how self-service infrastructure sets developers free from bottlenecks and dependency gates. By providing reusable infrastructure modules and intent-based configurations, platform teams dramatically reduce infrastructure friction. This sefl-service then powers faster deployments, increased autonomy, and fewer delays.

But infrastructure provisioning alone doesn’t ensure happy, productive developers. Even with efficient, streamlined infrastructure interactions, developers still battle daily hurdles: from inconsistent local dev setups and sluggish CI/CD pipelines to poor documentation and fragmented knowledge. These obstacles quietly chip away at momentum, reduce feature velocity, and increase operational overhead.

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Previous articles in this series explored platform engineering principles and how Infrastructure as Code creates a solid foundation. But there’s still an important challenge to address: the infrastructure provisioning process itself. Without proper modularity and a clear separation between intent and infrastructure details, things get messy—leading to friction, delays, and unnecessary complexity.

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Provisioning is the first pillar of platform engineering. Without consistent infrastructure provisioning – the automated creation and management of the underlying cloud resources – the rest of the platform suffers. Self-service, governance, and streamlined developer workflows all depend on it. Ultimately, a self-service layer on top of your cloud infrastructure is the goal, enabling developers to quickly and safely provision the resources they need, while adhering to organizational best practices and policies. But before self-service, the foundation of a good IDP is a robust and reliable provisioning system.

By defining cloud resources as code and automating deployments, platform engineering teams ensure every environment – development, staging, and production – stays consistent and maintainable. This cuts down on configuration drift, reduces manual work, and supports auditable, collaborative workflows for every change.

Let’s explore how platform engineering teams can achieve this by version-controlling infrastructure, automating deployments, separating environments properly, and limiting console interventions. By applying these principles, teams can create a platform where developers can move fast without breaking things, and where infrastructure supports innovation rather than slowing it down.

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Software drives innovation. Development teams face pressure to ship features faster. But speed collides with infrastructure complexity. Developers struggle with cloud setups, juggle scattered tools, and wait on operations teams for resources. The result is friction and slower innovation.

This is where Platform Engineering comes in. It helps developers move faster by creating tools that actually work. A good internal platform lets teams self-serve infrastructure, find documentation, follow best practices, and focus on what they do best: writing useful software.

Building a platform isn’t about finding one perfect tool. It’s about assembling the right pieces, or pillars. These pillars define what every successful internal developer platform needs.

This series explores these key pillars of Platform Engineering, offering a practical guide to building platforms that remove barriers to developer speed. Each pillar addresses a specific challenge organizations face when scaling developer productivity. The first challenge is overcoming infrastructure chaos.

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Most developers maintain at least one low-traffic service that still needs to be reliably available. It might be an internal reporting API that gets a few calls per hour or a side project with occasional use. While these services don’t handle much load, they need to exist and remain responsive.

This creates an interesting hosting challenge: how do you maintain high availability for services that might only handle a few thousand requests per month? Traditional hosting approaches mean paying for 24/7 server time, even when your service sits idle.

These services present a unique challenge: they need to be reliable when called but get less than 500,000 requests a month.

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Have you ever had one of those moments: Elasticsearch is crashing, logs are filling up too fast, or a deployment needs data from three different APIs. The big enterprise solutions aren’t quite right, and your team estimates six months to build something proper. But you know that with Python and a few choice libraries, you could hack something together by tomorrow.

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Building AI-powered developer tools comes with unique challenges, and now that we’ve launched our REST API, we want to share some lessons we’ve learned building Pulumi Copilot, an AI assistant for cloud infrastructure.

One of the big challenges was determining what ‘working’ really meant. So when a message landed in our feedback channel after months of rigorous testing - ‘Your tool doesn’t know anything!’ - it caused some mild panic. We’d just made some changes, so we braced for the worst. But our evals were still looking strong, so what was going on?

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If a computer can be connected to the internet, someone has tried to run a container on it. From quantum computers to smart toasters, from phones in AWS racks to CI pipelines, there’s many ways to deploy containers. While most people only know about the mainstream cloud providers at the surface, there’s actually a vast world of increasingly unusual and specialized options beneath.

This guide serves two purposes: to showcase the surprising breadth of container options available today, and to help you understand the full spectrum of choices – from practical to very experimental. Whether you’re looking for production-ready solutions or just curious about what’s out there in the dark depths, you’ll find something interesting here.

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Continuing our work to bring the best of modern Python to Infrastructure as Code, we are excited to announce built-in support for uv in Pulumi. uv is an extremely fast Python package manager that can install dependencies up to 100x faster than traditional tools, providing one of the fastest ways to manage your Python dependencies and virtual environments.

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