Operations | Monitoring | ITSM | DevOps | Cloud

It is 12PM and you just start eating lunch when your phone starts buzzing. A storm of different monitoring and system-level alerts start stacking up on your phone and slack. The incident response "war room" opens and downtime communications are being drafted to customers. Your team is under pressure to find the root cause, but you are immediately hit with roadblocks.

Dynatrace is a powerhouse for application performance monitoring and business analytics. But for many organizations, its power comes with a significant challenge: as applications scale across complex hybrid environments and diverse tech stacks, the sheer volume and variety of logs, metrics, and traces sent to the platform can explode, leading to staggering and unpredictable costs.

You’ve built something amazing. Your services are scaling, your users are happy, and your team is shipping code like never before. Then the cloud bill arrives, and one line item makes your eyes water: observability. That Datadog invoice feels less like a utility bill and more like a ransom note. It’s a modern engineering paradox. The tools that give you sight into your complex systems are the same ones that can blind you with runaway costs.

Platform teams are struggling with observability noise, bloated storage costs, and lack of clarity during incidents. Most teams capture everything all the time, leading to expensive, overwhelming, and often unnecessary data volumes. In Telemetry for Modern Apps, Mezmo teamed up with Checkly to demonstrate how synthetic monitoring triggers and responsive telemetry pipelines can help reduce costs while maintaining the context needed during incidents.

Following the popularity of our existing GitHub integration, we’ve extended similar capabilities to GitLab users. You can now ingest GitLab events directly into Mezmo Telemetry Pipelines and route them to any destination. This provides a powerful new way to monitor, alert, and react to activity within your GitLab repositories.

Let's be honest: most conversations about AI ethics sound like they're happening in a boardroom, not an ops room. But here's the thing, when you're using AI to make sense of your telemetry data, ethics isn't some abstract concept. It's the difference between insights you can trust and algorithmic noise that leads you down the wrong path. The uncomfortable reality? Your AI is only as ethical as the messiest, most biased piece of telemetry data you feed it. And if you think your data is clean, well...

‍ We're not witnessing the end of observability, we're witnessing its evolution into something far more powerful. The observability industry is having its Moneyball moment. Just like Billy Beane revolutionized baseball by using data analytics to compete with teams that had vastly larger budgets, observability is undergoing a fundamental transformation.