Operations | Monitoring | ITSM | DevOps | Cloud

Learn exactly how Amazon Comprehend pricing works, NLP, PII, custom models, endpoints, and how to control costs before they spiral.

Application security & engineering teams are under pressure to move faster, cover more, and reduce the operational drag that often comes with security testing. But in practice, two problems keep slowing teams down and adding friction.

GPU infrastructure is expensive and increasingly central to production workloads. Whether you’re running ML training jobs, inference serving, video transcoding, or HPC workloads, understanding what your GPUs are actually doing, and what’s going wrong when performance degrades, is not optional.

Merge conflicts waste hours of development time every week. The Git branching strategy you pick directly shapes how often these conflicts appear and how painful they are to fix. GitKraken simplifies conflict resolution with visual tools that help you spot problems before they become blockers. This guide walks you through a step-by-step decision process for selecting between GitFlow and trunk-based development.

Picking the right VS Code Git extension can shape how you move through your codebase every day. GitLens and Git Graph both add visual Git tools to your editor, but they take different paths to get there. GitLens gives you deep context about every line of code – who wrote it, when, and why. Git Graph focuses on visualizing your commit history in a branching timeline. This article breaks down each extension so you can decide which one fits your workflow.

Merge conflicts can bring a small team’s momentum to a grinding halt. You’re working on a feature, ready to push your changes, and suddenly Git throws up conflict markers that demand your attention. For smaller teams where everyone touches the same codebase, these interruptions stack up fast. This guide walks you through the root causes of frequent merge conflicts and gives you actionable tactics to prevent them.

When you launch a new service in production, you’re working with a lot of unknowns. You don’t yet know how it behaves under real traffic or which incidents are worth waking someone up for. That makes alerting for a new service a little different from what you’re used to with an established one. The goal in the early days isn’t to get everything perfectly configured. It’s to learn enough about the service to get your alerting right.

A two-person startup signs up for the obvious hyperscaler because their last company used it, because Stripe runs on it, because the documentation is exhaustive, and because the free tier looks generous. Eighteen months later, with a small team and a healthy seed round, they discover they're spending $18,000 a month, and they don't quite know where most of it is going. Three engineers can describe the architecture in detail. Nobody can describe the bill.

Why ECS containers collapse under service.name = aws_ecs and how to fix it for both EC2 launch type and Fargate, including the resource-vs-log-record pitfall that quietly breaks log filtering. 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.

What actually works for Kubernetes monitoring at scale — not what looks good in a vendor demo with a five-pod cluster.