Operations | Monitoring | ITSM | DevOps | Cloud

Launch Week Day 1: Introducing Discover Services Picture this: It's 2 AM, alerts are firing, and you're staring at a dashboard trying to figure out which service is causing the cascade of failures. Your service map is a six-month-old Miro board, and you have no idea what's actually talking to what in production right now. If you've been there, you're not alone. In fast-moving teams, new services get deployed faster than you can track them.

One of the biggest benefits of the cloud is that it gives engineering teams the freedom to deploy and iterate applications quickly. Unlike traditional IT environments where engineers require a series of approvals before embarking on projects, in the cloud, engineers can choose from several managed services and deploy them at the click of a button. This means your team can innovate faster and respond quickly to market demands.

AI is surging into products. And the invoices are exploding with it. The key question is no longer, “How much did we spend?” It’s now: “Was it worth it?” That shift, from totals to value, is at the heart of FinOps. The FinOps community defines the practice as bringing financial accountability to the cloud, so teams make tradeoffs with clear business context. In plain English, measure value per dollar, then optimize the system and not just the bill.

Amazon SageMaker makes it easy to prepare data for machine learning (ML) and then train, deploy, and modify ML models. SageMaker is a fully managed service that automates much of the ML lifecycle. So, if you want a single partner to help you through all stages of your Artificial Intelligence (AI) lifecycle, SageMaker might be the answer. Perhaps more important for this post is the promise that Amazon SageMaker can reduce your machine learning model costs. But does SageMaker pricing reflect this?

AI coding assistants are already transforming how developers work, helping them write code faster, answer tough questions, and automate repetitive tasks. It’s exciting, it’s powerful… and it’s just the beginning. Cortex MCP connects your AI assistant directly to your live service data, ownership, and organizational standards so it can give accurate, context-rich answers right in your IDE.

Our CLI team has been cooking up some awesome new AI functionality that we think you will love. The latest release includes several new commands such as changelog, commit, pr, and a command to check the remaining tokens on your subscription. They are all nested under a new top level command, gk ai.

AI adoption isn’t just accelerating, it’s compounding. From GPT-5 to Claude to Llama and beyond, engineering teams are integrating diverse LLMs across products, experiments, and services. And finance teams are now grappling with a new kind of cloud complexity: token-based economics and volatile inference costs, often spread across multi-model, multi-cloud, and multi-region architectures. The modern FinOps stack needs to keep up. CloudZero was built for this moment.

When a service slows down, metrics will tell you that it’s happening but logs tell you why. For MySQL, slow queries can be a silent performance killer, gradually chewing through resources until users start complaining. By enabling MySQL’s slow query log and forwarding it to Loki (via Promtail), you can visualize query-level details right alongside your metrics on Grafana dashboards. This makes it easy to correlate what is slow (metrics) with what is causing the slowdown (logs).