Operations | Monitoring | ITSM | DevOps | Cloud

Ship features faster and safer with Datadog Feature Flags

Releasing new features is one of the highest-stakes moments in the software delivery life cycle. Even with CI/CD pipelines in place, plenty of things can still go wrong when a feature goes live for actual users. Most feature flagging tools operate in isolation from important observability tooling, forcing engineers to monitor changes across multiple disconnected systems to fully understand their impact. This slows down development and increases the chance of missing critical issues.

Datadog Feature Flags, track Claude costs, migrate historical logs, and more | This Month in Datadog

See how you can reduce risk during feature rollouts in September’s This Month in Datadog. This episode, we spotlight Datadog Feature Flags, which combines advanced targeting with built-in observability, and guardrails to make rollouts safer and more controlled. Plus, we cover: This Month in Datadog brings you the latest updates on our newest product features, announcements, resources, and events.

From Logs to Insights: Accelerate Customer-Impact Analysis with Datadog Sheets

Datadog Sheets helps you move from log exploration to actionable insights quickly and with no code required. In this demo, see how to enrich logs with Salesforce data, build pivot tables, uncover customer impact trends, and build shareable reporting, all within Datadog.

Model your architecture with custom entities in the Datadog Software Catalog

Every software organization has its own unique architecture and workflows. Beyond services and APIs, teams rely on internal libraries, CI/CD jobs, data pipelines, AI agents, and more to keep systems running smoothly. But as architectures grow more complex and interconnected, it can become difficult to keep track of all the structural dependencies and interactions in one place.

Monitor your data pipelines with Airflow lineage

In complex data pipelines with dozens of jobs and intermediary datasets, it can be difficult to effectively monitor how data travels and changes through various steps. When tracking issues in these pipelines, you need visibility into upstream components where the root cause may originate from, as well as downstream datasets and consumers of data that may be experiencing further impacts.

Proactively monitor Kerberos-authenticated web apps and APIs with Datadog Synthetics

When employee authentication fails or becomes unreliable, users can lose access to the critical systems they need. Authentication enables access to internal tools like HR applications, finance portals, and internal dashboards, so even short outages can interrupt day-to-day work, while persistent issues increase the risk of broader operational disruption.

Datadog in the Era of AI

AI is changing everything. At Datadog, our approach is two-fold: empower you with complete observability across your entire stack, including AI as you incorporate it, and harness emergent technologies to make Datadog even more powerful. Join VP of Product Michael Whetten to see how Datadog is accomplishing these two approaches. He'll share the latest feature updates and new products designed to help you thrive in an AI-powered world. Plus, get a look at our long-term vision for the future of AI and its impact on your work.

Track the performance of your HPC workloads with Datadog's AWS PCS integration

AWS Parallel Computing Service (AWS PCS) is a managed service that helps users run and scale their high performance computing (HPC) workloads. AWS PCS uses Slurm, an open source workload manager, for scheduling and orchestrating simulations, which enables users to build their scientific and engineering models in a familiar HPC environment.