Operations | Monitoring | ITSM | DevOps | Cloud

Debug live production code without redeploying with Datadog Live Debugger

Some production bugs don’t show up clearly in logs or traces, and they often cannot be reproduced in a local or staging environment. When developers need more runtime detail, they typically fall back on a familiar but slow workflow: add log lines, open a pull request, wait for review and CI/CD, deploy the change, and wait for the issue to happen again. If the new logs don’t capture the right variable values or execution path, the loop starts over.

Beyond the $1 AI era: How federal agencies can build the evidence for FY27 renewals

Over the past year, federal agencies gained broad access to enterprise AI through the OneGov initiative, at prices unlike any normal software deal. The current OneGov portal lists OpenAI ChatGPT Enterprise at $1 per agency, Anthropic Claude at $1 per seat, and Google Gemini for Government at $0.47 per agency. Those introductory offers begin expiring on September 30, 2026, the final day of fiscal year (FY) 26, which places renewal squarely in the FY27 planning cycle.

How Datadog saves over $1 million each month by optimizing AI usage

At Datadog, we want to expose our engineers to high-quality AI tools and workflows. However, token usage can be expensive, and finding a balance between AI cloud spend and the return on investment can be difficult. But what if engineers could maintain their current AI workflows using the same tools, but at a lower cost?

Golden Paths for AI agents: What changes when platform users aren't human?

Agents are already calling your self-service APIs, querying your catalog, and independently provisioning resources around the clock. According to Gartner’s 2026 Hype Cycle for Agentic AI report, agents have had the most aggressive adoption curve of any emerging technology. Gartner even considers agents to be a formal user persona, referring to the agent experience (AX) throughout the report.

Build your own Bits Agent with Datadog Bits Agent Builder

Datadog Bits Agent Builder lets you build AI agents that use your observability data to automate operational tasks. In this walkthrough, see how to build an agent that analyzes monitor and alert activity, identifies patterns, and provides actionable recommendations to improve your monitoring strategy. With Bits Agent Builder, you can give agents access to Datadog data and tools, customize their instructions and models, and run them automatically to continuously analyze and act on your environment.

Monitor Azure Functions across every hosting plan with Datadog

Microsoft’s Azure Functions serverless compute service abstracts away infrastructure management to simplify how teams deploy and operate applications. However, the managed environment can make it harder to understand what happens inside those applications after deployment. Traditional approaches that rely on agents, extensions, and sidecars for direct collection of telemetry data aren’t available across every Azure Functions hosting plan, which can leave gaps in end-to-end visibility.

Control trace volume with OpenTelemetry tail-based sampling

OpenTelemetry (OTel) tail-based sampling helps teams control trace volume by retaining errors, slow requests, and other traces worth investigating while dropping lower-value traffic. In distributed systems, a single request can fan out across many services, each emitting spans. That volume adds up quickly. Some applications produce millions of traces per hour, while large clusters generate more than 10 billion spans per day.

How to add Software Catalog metadata at scale with Terraform | Datadog Tips & Tricks

Adding metadata to Software Catalog entities manually is a tedious process that doesn’t scale as your service count grows. This video shows you how to automate that work with Terraform so you can add shared metadata across existing Software Catalog entities at scale.

Trace AWS Lambda durable functions with Datadog

AWS Lambda durable functions let you build long-running, multi-step workflows for use cases such as payment processing, order fulfillment, and AI workflows with human approval. A single durable execution can pause for a wait or callback, retry failed work, and resume in a fresh Lambda invocation without losing its state. The strong resilience provided by durable executions, however, creates an observability challenge because each invocation produces its own telemetry data.

Two ways to measure the cumulative impact of experiments

Mature experimentation programs eventually have to report the cumulative impact of their shipped changes. The request might come as an ROI story for leadership, a revenue update for finance, or a gut check on the quarter’s progress. The tempting shortcut is to sum the observed lift from each winning experiment and report the total. That naive sum almost always overstates the truth because of a statistical artifact called the winner’s curse.