Operations | Monitoring | ITSM | DevOps | Cloud

According to CloudZero’s Cloud Economics Pulse, databases are often among the largest and most persistent cloud cost categories. Database costs are notoriously difficult to predict and control. Unlike stateless infrastructure that scales predictably with traffic, databases run continuously and expand behind the scenes, causing costs to rise even when usage appears stable. Because databases run continuously and expand behind the scenes, costs can rise even when usage appears stable.

In the world of enterprise software, the transition from traditional DevOps to modern AI-driven delivery is less like a flip of a switch and more like a high-stakes kitchen. As Devan Shah, Chief Architect at IBM, puts it: the ingredients have changed from food to code, but the need for a precise, governed process remains the same.

AI-powered coding tools are changing how developers work. Tools like Claude Code can write functions, refactor code, and build features through natural conversation, often faster than you could type them yourself. But speed creates its own risks. AI-generated code can contain subtle bugs, reference packages that don’t exist, or misuse APIs in ways that only surface at runtime. That’s where continuous integration comes in. CI is a safety net that lets you move fast confidently.

AI coding assistants like Gemini are changing how developers write code. They can generate entire functions, debug tricky issues, and help you move faster than ever before. But with that speed comes a new challenge: how do you make sure AI-generated code actually works? AI assistants are powerful, but they’re not perfect. They can introduce subtle bugs, miss edge cases, or generate code that breaks existing functionality. That’s where CI (continuous integration) comes in.

Compare 9 incident management tools: PagerDuty, Opsgenie, Incident.io, Rootly, FireHydrant, BetterStack, Grafana OnCall, Squadcast, and Last9. Features, pricing, and which fits your team. Product Marketing Manager.

Onboarding new engineers to complex Kubernetes environments is expensive. Junior engineers need to learn cluster architecture, understand organizational conventions, navigate internal documentation, and build relationships with senior team members who can answer questions. The process takes weeks or months, and during that time, senior engineers spend significant time mentoring instead of working on complex problems.

How database partitioning works in PostgreSQL and MySQL. Range, list, and hash partitioning with SQL examples and guidance on when to partition vs shard. Prathamesh works as an evangelist at Last9, runs SRE stories - where SRE and DevOps folks share their stories, and maintains o11y.wiki - a glossary of all terms related to observability.

Cloud adoption is no longer about “moving to the cloud.” It’s about building cloud-native platforms that are scalable, observable, automated, and Kubernetes-driven. This guide provides a deep comparison of with a focus on Kubernetes, platform engineering, DevOps, and modern workloads, aligned with standards pioneered by the Cloud Native Computing Foundation.