Systems | Development | Analytics | API | Testing

A couple of weeks ago, our team returned from API World. We’ve officially had a few weeks to decompress and get back into the swing of things after an incredible time at API World 2025. Looking back, the experience was even more rewarding than I had imagined in my Pre-API World blog. This year was especially memorable for me, as I had the opportunity to attend my first tech conference and travel across the country for work. I’m still buzzing from everything I learned and the people I met.

Cloud-native development, and the resultant rise of DevOps, has transformed how software is built, deployed, and maintained. By embracing containerization, microservices, and continuous delivery, organizations have been able to deliver features faster, scale with demand, and recover from failures more gracefully than ever before. Many organizations are adopting these practices to keep up with industry demands and improve efficiency and security.

Modern application development demands speed, reliability, and reproducibility. But between third-party APIs, unstable backend services, and inconsistent test data, API development often struggles to achieve dependable environments for testing and iteration.

When I started at Speedscale, I looked like this: And after one year of learning, growing, and keeping pace with innovation well, let’s just say the journey has left its mark: Of course, I’m joking (sort of). The truth is, this past year has been intense, energizing, and filled with new challenges. If anything, it’s made me feel younger in spirit, even if the mirror might disagree some mornings.

API gateways are often viewed as the centralized entry point for client HTTP requests in a distributed system. They act as intermediaries between clients and backend services, managing API request routing, load balancing, rate limiting, access control, and traffic shaping across multiple backend services. This API management is vital for many services and products, but many organizations can put too much stock in it.

The industry is rapidly moving towards deeper AI integration than ever before. What was once simply focused on chatbots or recommendation engines has pivoted significantly to AI systems communicating with other AI systems. These AI tools are leveraging multi-agent workflows to accomplish complex tasks that traditional systems have struggled with. Innovation without validation is a liability. Any developer worth their salt will know that these systems require ample testability and validation.

Kubernetes has become the backbone of many modern application deployment pipelines, and for good reason as a container orchestration platform, Kubernetes automates the scaling, deployment, and management of workloads, allowing developers to make their applications easier to manage and deploy at scale without worrying about their service’s dependencies, their user’s operating system, or the intricacies of their data center or infrastructure provider.

If you’ve spent any time building cloud-native systems, you’ve probably tripped over the tricky beast that is gRPC streaming. It’s powerful, flexible, and feels like magic when it works. But the minute you need to test it? Suddenly, you’re in “hold my coffee, I need a week” territory. One of the most common places we see gRPC streams in the wild is when clients connect to asynchronous message buses like Google Pub/Sub.

Traditional performance testing often comes late in the delivery cycle, typically just before release. By then, performance issues are usually quite expensive to fix, can delay deployments, and frustrate development velocity. A Shift Left testing approach addresses this by integrating performance testing early in the development cycle so issues surface while they’re still easy and cheap to fix.