Operations | Monitoring | ITSM | DevOps | Cloud

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.

NetFlow is a popular network protocol for collecting metadata about traffic flows across your environment so that it can be exported for analysis and monitoring. One of the most common issues that users encounter is NetFlow duplication, which occurs when identical flow records from the same conversation are recorded from different sources. Flow duplication inflates traffic data, undermining capacity planning and making top-talker rankings unreliable.

ChatGPT Work has become a common starting point for data and product teams. Analysts open it to compare launch adoption across segments, diagnose a metric that moved overnight, or turn a week of scattered numbers into a readout that a leader can act on. But the moment teams ask whether their experiment actually caused an effect they’ve observed, the conversation stalls.

When Datadog acquires a company, we usually rebuild the product rather than plugging it in as is. That’s exactly what we did with Eppo, an experimentation and feature-management platform. Eppo’s feature-management capabilities became Datadog Feature Flags, while experimentation became Datadog Experiments. This post focuses on the experimentation platform and four changes we made to help you get to a decision faster.

Kafka is a crucial and widely used technology: 80% of the Fortune 100 rely on the event streaming platform as part of their stack, according to Apache. But Kafka issues can be complex to manage and even more difficult to troubleshoot, as the same symptom can point to very different problems. Suppose consumer lag on your checkout-events topic suddenly exceeds its SLA.