Operations | Monitoring | ITSM | DevOps | Cloud

Artificial intelligence is changing the way we execute our everyday operations. AI assistants are incredibly intelligent; they can write code, explain complex concepts, and answer any question you throw at them. However, they can't execute actions on their own. If you ask your AI assistant to “create a backup of my database,” it may provide you with clear instructions, run the CLI commands directly or in some cases, even trigger actions through connected agent workflows.

The AI landscape today feels a lot like the early days of the cloud: exciting, fast-moving, and completely fragmented. Every week, engineering teams are experimenting with dozens of large language models (LLMs) from providers like OpenAI, Anthropic, Google, Mistral, Meta, and beyond. They’re tweaking prompts, testing model performance, swapping context windows, and even running multiple models in parallel to figure out which one works best for each unique use case.

Happy Holidays, and happy almost new year from us at CloudZero! November is always a busy time! In case you missed the headlines, here’s what happened last month in relation to cloud and AI costs: Get the details below!

Today, we’re excited to announce our expanded partnership with Amazon, bringing together the power of Amazon Kiro, Amazon Q Developer, and Harness SaaS on AWS to revolutionize how your team builds, troubleshoots, secures, and deploys software. This collaboration is designed to deliver a seamless, intelligent, and scalable software delivery experience for all AWS customers.

In 2026, digital performance is more critical than ever. Users expect web applications to load instantly, respond flawlessly, and support complex interactions without delay. For DevOps teams, this means browser monitoring is no longer optional—it’s a foundational capability for ensuring availability, speed, and reliability across modern web experiences.

Looking for reliable Heroku monitoring in 2026? Start with the metrics that matter, pair Heroku’s built-ins with the right add-ons, and add alerts that catch issues before users do.

Running Kubernetes at scale almost always means paying for more compute than you need. To protect reliability, platform and application teams typically overprovision nodes early in development and keep scaling up as they add features and workloads. They are often reluctant to move to smaller or different instance types without a clear picture of how those changes will affect performance or availability. The result is a fleet of underutilized nodes that silently inflate your cloud bill.