Operations | Monitoring | ITSM | DevOps | Cloud

If you work in automotive, you’ve probably already heard of the CRA – the EU’s Cyber Resilience Act. It’s one of the most ambitious pieces of cybersecurity regulation in years. And while it wasn’t written specifically for cars, it’s going to impact a huge part of how software gets built, updated, and maintained across the automotive stack. So here’s the question: how do you prepare for something like the CRA?

Over the past few years at Catchpoint, we’ve benchmarked the digital performance of banks, airlines, hotels, travel aggregators, GenAI platforms, athletic footwear brands, and even ad hoc events like the Super Bowl, Olympics, and Election Day. Each time, our approach focused on the technical metrics performance professionals live and breathe: DNS resolution times, Time to First Byte, page load speeds, and six other core measurements that we'd dissect, analyze, and use to rank companies.

Why the traditional model of monitoring and manual operations is collapsing–and what enterprises must do to survive The digital universe is expanding at a pace no enterprise can keep up with through traditional methods. Dependencies pull at each other in ways even experts can’t predict. What once could be managed with dashboards and siloed monitoring tools has become too vast, too interdependent, and too fast-moving, a new operating model is needed to master such complexity.

Modern PHP applications rarely exist in isolation. They run across distributed environments, connect to MySQL or PostgreSQL databases, interact with Redis or Memcached, rely on APIs, and communicate with microservices. This interconnected web brings power but also enormous complexity. When performance issues arise, finding the root cause can feel like searching for a needle in a haystack. Is it the database? A caching layer? A failing third-party API?

Serverless architecture has changed how applications are built and run. It removes the need to manage servers, letting developers focus on writing code while automatically scaling with demand. But even with its pay-as-you-go model, serverless apps can get expensive if not monitored and optimized. In this blog, lets see how smart serverless monitoring helps developers and DevOps engineers lower serverless costs, boost performance, and keep operations running smoothly.

Your application is only as reliable as the infrastructure it runs on. Most commonly, that means Kubernetes is doing the job by managing fleets of containers, scaling services on demand, and keeping workloads distributed across nodes. Traditional dashboards weren’t built to scale with this reality. They give you snapshots of raw metrics. They don’t scale to multi-cluster environments. They don’t map relationships between resources.

If your SaaS organization is experimenting with OpenAI, your cloud bill just got a new line item. And unless you know exactly what drives it, that line item can go from manageable to margin-killer, fast. It’s also worth clarifying that OpenAI is not the same as ChatGPT. ChatGPT is the familiar end-user app with a flat monthly subscription. OpenAI, meanwhile, is the platform behind it — a mix of models, features, and usage-based pricing that shifts depending on what you build.

The most challenging question in AI today isn’t how to build with it. It’s whether you can prove it’s worth what you’re spending on it. Every week, I hear the same thing from engineering and finance leaders: “We know the AI bills are big.

A bit more behind-the-scenes work than usual this month, but I still managed to ship some public-facing features you might be interested in. Logging in, and clicking around the dashboard just got 60% faster, and we're just getting started.

APM tracing records the complete execution path of a request as it travels through your system, including database queries, external API calls, cache lookups, message queue events, and inter-service requests. Each step is captured with precise start and end timestamps, duration, and context such as service name, operation name, and relevant attributes. This lets you pinpoint where latency or errors originate without piecing together metrics and logs manually.