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As AI workloads and cloud-native applications expand, organizations are generating more log data than ever. Each service, container, and model inference produces continuous telemetry that must be stored, secured, and analyzed. As telemetry grows more complex, teams must balance full visibility with new retention and residency needs.

Testing ecosystems contain massive amounts of data, including outlined test scenarios, prerequisite configurations, and the tests themselves. As a result, these ecosystems are prone to data sprawl. This makes it difficult to prevent configuration drift and quickly spin up new tests, especially at the frequency needed to support a fast-growing application. Teams can handle these challenges by treating their tests as part of their application infrastructure.

As AI workloads and cloud-native applications expand, organizations are generating more log data than ever. Each service, container, and model inference produces continuous telemetry that must be stored, secured, and analyzed. As telemetry grows more complex, teams must balance full visibility with new retention and residency needs.

We are thrilled to announce that, for the second consecutive year, Datadog has been named a Leader in the 2025 Gartner Magic Quadrant for Digital Experience Monitoring. We believe that this recognition reflects our continued focus on helping customers observe, secure, and act on everything that matters across their technology stack.

Modern teams often run hundreds of synthetic tests across multiple services, environments, and user journeys. While these tests provide deep visibility, managing them as a flat list can quickly become overwhelming, especially as organizations scale and teams specialize.

Metrics are fundamental to experimentation for two reasons: They set the basis for evaluating ideas and interventions, and they can suggest where to look next. As such, many teams collect a wide variety of metrics, from application performance data to revenue trends. However, doing so often means manually knitting together data from multiple sources and formats. Even then, data silos can make it challenging to understand the full impact of experimental changes. In this post, we’ll explore.

When external SaaS providers or cloud services degrade or go down, engineers often find themselves wondering if the issue they're encountering is local or more widespread. The answers they find are usually slow to surface, limited in detail, or entirely dependent on the provider's updates. Vendor-controlled status pages and third-party aggregators don’t provide the timely, independent visibility that's necessary to quickly and accurately identify the root cause of slowdowns.

High-performance computing (HPC) environments support some of the most critical workloads in the world—from asset pricing models in financial institutions to molecular simulations in drug discovery. These workloads often span hundreds of thousands of cores, depend on specialized infrastructure such as GPUs, and run for extended periods. As a result, performance and efficiency are critical.

Modern applications depend on dozens of external cloud platforms, APIs, and SaaS services to function. But when those providers experience issues, engineers often spend valuable time asking a basic question: Is the problem with us or with them? Provider-maintained status pages are often slow to update, leaving teams waiting for confirmation while incidents escalate. This delay wastes valuable time, prolongs investigations, and risks customer trust.

Historically, developers have relied on languages like C and C++ for explicit control over memory allocation and deallocation. This approach can yield very low overhead and tight control over performance, but it also increases complexity and risk (e.g., memory leaks, dangling pointers, and double frees). This often results in runtime issues that are difficult to diagnose, which can become a drag on team velocity.

Many organizations rely on OpenTelemetry (OTel) to standardize observability across distributed systems. These organizations are at varying stages of adoption and are implementing OTel in complex environments with diverse configurations. To support this range of use cases, Datadog offers many ways to use OpenTelemetry with Datadog.

A synthetic test is only useful if you can trust what it’s telling you. When one fails, the reason may not be obvious. Was the application updated? Did the test change? Or both? As more people contribute and refine the same test, it becomes harder to understand what changed or restore a working version. Without clear visibility into those updates, teams can spend more time tracking down the cause of a failure than resolving it.

Amazon EKS on Fargate is a managed service that reduces the operational overhead of maintaining a Kubernetes cluster by abstracting away the underlying infrastructure. In a serverless Fargate environment, each pod is assigned its own isolated compute resources; there is no direct host-level access.

Engineering teams need to deliver reliable, secure, and high-performing applications, all while keeping costs under control. But engineers often lack visibility into cloud cost data, relying on finance-driven reports that they receive only after the billing cycle closes. Without daily cost insights alongside observability data, they don’t know until it’s too late that an infrastructure change caused a significant cost increase.

Visibility into email performance is indispensable to any organization that counts on its ability to reach people through their inboxes, including Datadog. SREs, FinOps, and many other teams rely on email as a critical channel for communications from our platform, including monitor alerts, usage reports, and service account notifications. At Datadog, we depend on the visibility provided by our integrations for Mailgun, SendGrid, and Amazon SES to optimize our email performance and ensure deliverability.

Datadog Workflow Automation can automate processes and reduce the amount of time spent on time-consuming, repetitive tasks. You can trigger these workflows in real time by tying them to alerts, dashboards, Slack messages, and other signals.

When incidents occur, clear communication can be just as important as fast remediation. Your internal teams need timely updates to stay aligned, and your users want to know what is happening and when they can expect a fix. Without a reliable way to proactively share updates, support teams can get flooded with tickets and customer trust can erode. Datadog Status Pages, now generally available, makes it easy to keep everyone informed through a public or internal web page during outages.

Integrations have always been central to the Datadog platform, enabling customers to collect the data they need directly from the technologies they use every day. By unifying signals from infrastructure and applications to security and SaaS applications, teams gain both high-level visibility and the ability to drill into the details that matter the most. With more than 1,000 integrations now available, the Datadog ecosystem continues to expand alongside the platforms our customers rely on.

Slurm (Simple Linux Utility for Resource Management) is an open source workload management system used to schedule jobs and manage resources for high-performance computing (HPC) Linux clusters. It ensures that jobs and resources are scheduled fairly and efficiently and is scalable across large clusters, an issue that native Linux process management tools struggle with.