Operations | Monitoring | ITSM | DevOps | Cloud

We built Bits AI SRE to help engineers investigate and solve production incidents, one of the most difficult aspects of operating distributed systems today. As environments grow more dynamic and complex, resolving issues becomes more challenging. Failures now span more services, involve noisier signals, and encompass larger volumes of telemetry data, making it hard for on-call engineers to find root causes quickly. Today, Bits AI SRE is already helping teams decrease time to resolution by up to 95%.

When a pod fails during a TensorFlow training job, the investigation usually starts with the obvious questions. The answers rarely come quickly, especially when the failure involves GPU hardware that most engineers don’t troubleshoot regularly. This scenario walks through an actual GPU hardware failure and shows how AI-augmented investigation changes both the time to resolution and the expertise required to handle it.

There’s a moment every engineer knows. An AI suggests a fix, it looks reasonable,maybe even obvious, but production is on the line and you hesitate before clicking execute. There’s a big difference between an AI that can recommend an action and one you’re willing to let take that action. All it takes is one bad call, one kubectl command that makes things worse, and suddenly every automated suggestion is a potential liability instead of a help.

We’ve written before about the advantages of training an AI SRE on real telemetry data rather than generic Kubernetes documentation. We’ve explained why RAG augmentation based on actual high-scale workload patterns produces better results than LLMs trained on generic scenarios or forum threads. The theory makes sense, the architecture is sound, and the approach is defensible.

When it comes to containerization technologies, Podman and Docker are the two giants that often come up in conversation. Both have revolutionized how we build, deploy, and manage containers, but what sets them apart? In this blog, we'll dive deep into a side-by-side comparison of Podman and Docker. We'll cover everything from architecture to security, performance, and compatibility.

When it comes to monitoring and observability tools, Datadog is often one of the first names that comes to mind. But while Datadog’s features are widely discussed, its pricing often remains a topic of confusion. How much does Datadog cost, and what factors influence your bill? This guide breaks down Datadog pricing to help you better understand its structure, hidden nuances, and whether it’s the right fit for your needs.

High-cardinality metrics are one of those ideas that sound obviously right - until you try to use them in production. In theory, they promise precision. Instead of averages and rollups, you get specificity: per-request, per-userid, per-container, per-feature insights. The kind of detail we all immediately want when something is on fire. And then things start breaking. Not immediately. Not loudly.But quietly.

As AI workloads shift from training to massive-scale inference, SRE teams are about to feel even more pressure. GPU-heavy computing is breaking the assumptions today’s clusters were built on, while enterprises are beginning to trust autonomous operations and cost pressure is pushing consolidation across the cloud-infrastructure stack.

When systems fail, the instinct to find someone to blame runs deep. But what if assigning fault actually makes your systems less reliable? A blameless postmortem culture transforms how teams learn from incidents, creating stronger systems and more effective incident response processes.