Operations | Monitoring | ITSM | DevOps | Cloud

On the January episode of This Month in Datadog, join Jeremy Garcia (VP of Technical Community and Open Source) and Daljeet Sandu (Product Manager) for a bonus video that spotlights Datadog On-Call, which is now generally available. Also featured is a roundup of new features that Datadog recently announced. This Month in Datadog is a monthly update of the company’s latest features, product announcements, and more. Subscribe to our YouTube channel to get notifications about future episodes.

Cloud unit economics measures the amount an organization spends on cloud services to achieve a discrete business outcome such as a conversion, sign-up, or checkout. Your cloud spending may increase as your applications get more usage and the complexity of your cloud environment grows.

In modern application development, changes happen constantly: Deployments are pushed, feature flags are toggled, and Kubernetes events reshape infrastructure, to name just a few. While these practices drive innovation and scalability, they also introduce complexity—especially during incidents. Fragmented tools and workflows across teams and organizations make it difficult to pinpoint the root causes of issues, leading to longer resolution times.

Rust’s strong memory safety and efficient code execution make it a top choice for building robust, high-performance systems. But even with its powerful guarantees around memory management and thread safety, Rust applications in production environments can still face challenges such as latency spikes, resource contention, and unexpected bottlenecks. For this reason, monitoring Rust applications is essential to ensure they meet performance expectations and remain reliable under load.

Even with the best monitoring in place, outages are unavoidable. Complex, modern IT environments rely on multiple third-party services, including critical cloud and API providers, and when any one of those goes down, it can trigger a domino effect of increased error rates and latency spikes across your system. And, because you don’t have as much visibility into external services, it can be difficult to identify that the problem is due to an outside outage or disrupted service.

The stress, sudden disruptions, and high stakes of resolving issues while on call is one of the most challenging aspects of an engineer’s job. Many organizations, from startups to large enterprises, still struggle with their on-call experience, which leads to longer resolution times and lower employee retention rates. Constant context switching, managing multiple tools, and racing against time to resolve issues can cause frustration, burnout, and inefficiency.

Modern applications rely on databases, making database performance and reliability essential. As systems grow in scale and complexity, identifying the impact and addressing the root causes of database performance issues—such as long query durations or missing indexes—becomes increasingly challenging. Datadog Database Monitoring (DBM) Recommendations address these challenges by providing a clear, prioritized view of performance bottlenecks.

Deploying microservice-based applications with Kubernetes can be a complex process—more services translate to more Kubernetes resources, pipelines, and service dependencies.

In part 1 of this series, we introduced the key Cloud Run metrics you should be monitoring to ensure that your serverless containerized applications are reliable and can maintain optimal performance. In part 2, we walked through a couple of Google Cloud’s built-in monitoring tools that you can use to view those key metrics and check on the health, status, and performance of your serverless containers.

In Part 1 of this series, we looked at key Cloud Run metrics you can monitor to ensure the reliability and performance of your serverless containerized workloads. We’ll now explore how you can access those metrics within Cloud Run and Google’s dedicated observability tool, Cloud Monitoring. We’ll also look at several ways you can view and explore logs and traces in the Cloud Run UI and Google Cloud CLI.