Operations | Monitoring | ITSM | DevOps | Cloud

Bring faster visibility into AWS Lambda functions with remote instrumentation

Comprehensive observability is critical for running performant, reliable, and secure serverless workloads. However, configuring and maintaining that visibility across hundreds or thousands of serverless functions can be difficult to scale and sustain. Developers across teams often manage serverless functions using different infrastructure as code (IaC) frameworks, as well as different review, deployment, and update processes.

Build custom apps in seconds with conversational AI in App Builder

Using a drag-and-drop interface, engineering teams can create apps that support troubleshooting, improve day-to-day operations, and offer self-service access without leaving Datadog. With the new conversational AI feature, teams can turn an idea into a working app in seconds. Watch the video to see how it works..

Troubleshoot faster with the GitLab Source Code integration in Datadog

Developers and SREs who rely on GitLab to develop their services often face significant friction when troubleshooting errors or fixing issues that degrade code quality. To understand the context of a problem, they resort to tab-hopping between observability tools and GitLab, connecting stack traces, spans, and profiles back to the right files and commits.

Check out features we announced at AWS re:Invent in the latest episode of This Month in Datadog

Tune in for spotlights of Bits AI SRE, now generally available, and Datadog’s MCP Server, which connects AI agents to our platform by ingesting prompts and mapping them to Datadog resources and data. Plus, we cover how to: Search logs at petabyte scale in your own infrastructure with CloudPrem Break down costs drivers at the prefix level with Storage Management Create workflows that adapt to real-world complexity with Agent Builder Detect and block credential leaks with Secret Scanning.

Normalize any logs for Cloud SIEM with Datadog's OCSF processor

Security teams need visibility across every system they defend, including cloud platforms, SaaS applications, security controls, identity providers, and custom services. But those systems all produce logs in different formats, with inconsistent field names and structures. That lack of standardization makes it harder to correlate events, write reusable detections, and investigate incidents quickly.

Driving AI ROI: How Datadog connects cost, performance, and infrastructure so you can scale responsibly

AI innovation has accelerated faster than most organizations’ ability to monitor and manage it. The shift from experimentation to production-scale workloads has driven a new class of operational challenges: rising GPU costs, opaque model performance, and the difficulty of linking spend to business value. As AI investments grow, executives need a unified way to measure efficiency and return without slowing down innovation.

Detect, diagnose, and resolve network issues easily with CNM Network Health

In many organizations, developers, SREs, network engineers, and security teams work in specialized domains, which can make it hard to establish a shared view of network health. As a result, engineers often struggle to determine when a network problem that originates outside of their domain of expertise is the root cause of an incident. This lack of visibility slows investigations and delays remediation.

Drive business outcomes with Unit Economics in Datadog Cloud Cost Management

See how Datadog turns cloud usage and performance data into actionable business insights by helping teams calculate unit economics to measure and optimize the efficiency of every service. You’ll discover how to: Datadog bridges the gap between cloud costs and business value—helping organizations get the most value out of their cloud investment.