Operations | Monitoring | ITSM | DevOps | Cloud

Shipping application code, especially for LLM-based applications, can be a stressful and complex task. These applications demand intricate model management, careful resource allocation, and manual handling of dependency conflicts. Traditionally, preparing such applications for deployment involves integration tests, containerization, and updating image registries: all time-consuming manual steps. This is where an automated CI pipeline becomes invaluable.

With the ACME protocol, to issue a certificate you have to prove you control the domain. The CA gives you a challenge, you complete it, and they issue your cert. The trouble is that every validation method has tradeoffs. And as certificate lifetimes get shorter, those tradeoffs will get more painful. DNS-PERSIST-01 is a new approach coming in 2026 that trades proof-of-freshness for easier operations.

Developers and SREs who rely on GitLab to develop their services often face significant friction when troubleshooting errors or fixing issues that degrade code quality. To understand the context of a problem, they resort to tab-hopping between observability tools and GitLab, connecting stack traces, spans, and profiles back to the right files and commits.

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.

“Cut spending!” “Slash costs!” “Stick to the budget!” Poke your head into almost any finance meeting in a SaaS company and you’re likely to hear one or more of the above phrases played on repeat. At first glance it makes sense: Costs are increasing, so we should reduce them. I’d like to challenge that narrative. Mature companies don’t care about cloud costs.