Operations | Monitoring | ITSM | DevOps | Cloud

Cloud object storage powers a wide range of workloads, from AI training datasets to customer-facing media libraries. As your data grows into the petabyte scale, managing storage costs and ensuring reliability requires fine-grained visibility. You need answers to questions like: Which specific teams, services, workloads, or datasets are driving spend? Which data is cold and should be archived? What fixes will have the biggest impact on cost and performance?

Ensuring digital services remain accessible, reliable, and secure is a high priority for any organization operating at scale. For the Department of Veterans Affairs (VA), this focus is central to its mission of providing quality care to veterans, their families, and caregivers. Often described as “the largest IT shop in the United States,” the VA manages 2.7 million pieces of equipment across a vast network of interconnected systems.

Modern engineering teams face competing priorities. Developers are expected to deliver new features faster than ever, but users expect rock-solid reliability with every release. Shipping quickly can feel like you’re gambling with user trust. If you move too fast, you risk outages, but if you move too slowly, innovation stalls.

Engineering, operations, and security teams can struggle to make sense of their telemetry data in isolation. Logs, metrics, and events tell what is happening but are often missing critical metadata like who owns what, where it's coming from, or indicators of attack. These gaps in visibility slow down incident response, complicate cost control, and make business or security analytics much harder.

Organizations that run applications on Amazon Elastic Container Service (Amazon ECS) often juggle signals across container and task metrics, logs, and events while they hunt for the change or condition that broke a deployment. This work adds operational overhead and extends incident timelines as teams switch between tools and manually correlate symptoms.

We recently released the 2025 State of Containers and Serverless report, which examines cloud usage data from tens of thousands of Datadog customers. The study shows adoption trends across container orchestration platforms and serverless offerings, and it explores how organizations use those resources to optimize workloads for efficiency, cost, and simplicity.

Security teams face a constant trade-off between detection coverage and alert fatigue. Broad, rule-based detection approaches surface every possible indicator of compromise (IoC) but generate unmanageable alert volumes. Narrow, tightly scoped rules reduce noise but risk missing critical signals. And while individual indicators of compromise can highlight suspicious behavior, they often lack the surrounding context needed to tell a complete story of how an attack unfolded.

In October’s episode of This Month in Datadog, Jeremy shows how you can use AI to query with natural language in DDSQL Editor, ensure teams stay continually updated during outages, mitigate the impact of flaky tests, and ingest OpenTelemetry Protocol (OTLP) metrics from serverless and third-party SaaS environments.

When an application slows down or fails, pinpointing the cause isn’t always simple. Is it a backend regression, a misbehaving API, or a bottleneck somewhere deep in the network? Without full visibility, teams waste precious time troubleshooting across disconnected tools and layers. Datadog Synthetic Monitoring now supports Network Path to help you proactively identify whether user-facing issues stem from your code or from the underlying network.

Getting started with monitoring for Microsoft Azure environments can be a lengthy and manual process. Many tools require users to create app registrations, assign permissions, and enable log forwarding or telemetry data collection across multiple portals and scripts. These fragmented steps slow down onboarding and introduce opportunities for misconfiguration, making it harder for teams to quickly achieve full visibility.