Operations | Monitoring | ITSM | DevOps | Cloud

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.

When debugging production issues, investigating security incidents, or analyzing network traffic, engineers and analysts need not only to find the right logs but to make sense of all the dense, unstructured data generated by different systems. Logs rarely ship neatly laid out in a way that facilitates filtering, faceting, or graphing for every possible scenario. As a result, teams often find themselves writing regular expressions or custom parsers on the fly, which can be error-prone and time-consuming.

AI coding assistants are quickly becoming a core part of software engineering workflows, helping developers write, refactor, and review code faster. But without effective monitoring, it can be difficult to know whether these tools are performing reliably and proving useful to engineers. As organizations scale their use of tools like Claude Code, key questions emerge.

Engineering teams rely on forms for everything from approvals to checklists, yet the process usually lives outside engineering operations. Spreadsheets, one-off surveys, and external form builders capture inputs, but they create scattered data, slow follow-ups, and manual translation into actionable work. Datadog Forms enables teams to create and share interactive forms directly within Datadog.

Teams deploying LLM applications face a critical blind spot: They can measure speed and cost, but not whether their AI is actually giving good answers. To build user trust in these applications, teams also need to measure response quality, including factual accuracy, safety, and tone. Operational metrics show how a system behaves, but not whether its responses are correct or on brand.

Datadog App Builder is a low-code tool for creating internal apps, making use of a drag-and-drop interface that allows engineering teams to troubleshoot issues, optimize operations, and enable self-service while connecting directly to their Datadog data and permissions. Now, with conversational AI, teams can go from idea to working prototype even faster.