Operations | Monitoring | ITSM | DevOps | Cloud

Datadog Real User Monitoring (RUM) SDK settings live in your application code, so changing how the SDK collects RUM data has traditionally required shipping a new application version. These configuration changes can include adjusting sampling rates, enabling Session Replay, or changing which events the SDK collects. For mobile teams, this means that updates often sit in app store review for days or weeks before users start adopting the new version. Full user adoption can take weeks or months longer.

Logs are easiest to investigate when the values that matter are already captured as attributes. When those values are buried in a log message, even a straightforward question such as filtering on a status code, graphing the duration of a request, or following a unique transaction across a set of logs requires writing complex regular expressions or Grok patterns.

Observability investigations rarely follow a straight line. A latency question might cause an AI agent to start with a metric, pivot into traces, compare a deployment window, and finish by reducing thousands of logs to a few patterns. Each individual query is easy, but propagating context throughout an entire investigation can be tricky and expensive.

Collecting high-quality user feedback on agents, like from thumbs-up or thumbs-down buttons, is an important part of agent development. User feedback is needed for everything from basic gut checks on whether your agents are behaving well to planning and creating robust eval sets. It’s a critical part of Datadog’s Agent Observability, which provides explicit end-user feedback features for collecting and analyzing it.

A funnel can tell you that 40% of users dropped off between checkout and payment. What it can’t tell you is what those users did instead, such as return to an earlier form field, leave the flow for a support page, encounter an error, or take another route entirely. Because actions and views between funnel steps don’t affect the conversion calculation, two very different experiences can produce the same funnel result.

When an issue in production triggers an alert, the people responding to it are often working in Slack while the evidence they need is elsewhere. Responders need to move between conversations, telemetry data, source code, and incident tooling as they form hypotheses, coordinate actions, and keep stakeholders informed. That context switching can slow down a time-sensitive investigation and make updates harder to follow.

A platform engineering team can design robust Golden Paths for agent use yet still be unprepared for what happens after adoption. An agent may authenticate properly, call the correct tools, adhere to approval gates, and complete tasks without incident, but new operational risks arise once multiple teams begin running agents continuously and in parallel. We’ve encountered these risks firsthand at Datadog.

Distributed AI training workloads impose complex scheduling requirements that Kubernetes’s built-in scheduler can’t meet. Kubernetes schedules pods individually and independently, but distributed training introduces two requirements that break this model: Pods must land on hardware with the right inter-GPU bandwidth, and all pods must be scheduled simultaneously. If either requirement goes unmet, training stalls or runs far below the hardware’s potential.

AI coding tools such as Cursor are becoming a significant source of engineering spend. But Cursor costs can be difficult for FinOps teams to manage. Cursor’s usage data alone doesn’t tell you how costs break down across users and models, and fixed-threshold alerts may not catch an unusual cost spike if spend remains below the threshold. Datadog Cloud Cost Management (CCM) brings Cursor costs into the same place where you monitor cloud, SaaS, and other AI spend.

Datadog has been named the Company to Beat for observability platforms in the August 2026 Gartner AI Vendor Race research. Datadog has also been named a Leader in the 2026 Gartner Magic Quadrant for Observability Platforms for the sixth consecutive year. We believe that these recognitions reflect what we have been building toward for more than a decade: a single platform where teams can observe, secure, and act on everything that matters across their technology stack.