Operations | Monitoring | ITSM | DevOps | Cloud

The risk of headline-grabbing incidents, like Samsung’s ChatGPT data leak, related to AI usage outside of the authorization and control of IT (a.k.a. shadow AI) is clear. Most IT teams recognize that a high-profile incident can have serious repercussions. However, the risk of shadow AI goes well beyond the risk of a single incident. In fact, the recent Komprise IT Survey indicates that 79% of organizations have experienced negative outcomes from sending corporate data to AI.

MongoDB’s strength of flexible schema and fast iteration can also hide costly queries until they surface as user-facing latency, replica lag, or spiky CPU. A handful of slow operations can impact the cache, starve other workloads, and cascade into timeouts across services. Monitoring slow queries gives you an early warning system for index gaps and query-plan regressions introduced by code deploys, schema changes, or shifting data shapes.

There’s a new way to ingest AWS metrics into Grafana Cloud that makes observing your AWS resources more cost-effective, easier to operate, and more accurate. You can now stream metrics into the AWS Observability app in Grafana Cloud in near real-time thanks to our new integration with Amazon CloudWatch and Amazon Data Firehose. We’re already using it internally, and we’re finding that it’s not only easier to operate—it’s at least five times more cost-effective.

In the August episode of This Month in Datadog, Jeremy shares how you can make more informed cloud cost decisions, gain insights into your LiteLLM-powered applications, and secure Kubernetes infrastructure with Datadog Workload Protection. Later in the episode, Danny puts the spotlight on Datadog Kubernetes Autoscaling, which helps you deliver cost savings without sacrificing performance.

There are a lot of ways to gauge the performance of your DevOps teams and the health of your software, but DORA metrics have emerged as the industry standard. If you aren’t familiar with DORA metrics, take a few minutes to read this comprehensive guide to understanding DORA metrics. DORA metrics were designed to offer a high-level, long-term view of how your teams are performing.

When we started building Logs in Sentry we had one goal: make them useful for real debugging, not just another high-volume text storage. This meant making them "trace connected" from day one. This let us ensure they were tightly connected to the actions and performance happening in your application, right where developers already go to investigate errors, performance, and latency issues. Now, Logs is out of beta and generally available to everyone.

APM tracing records the complete execution path of a request as it travels through your system, including database queries, external API calls, cache lookups, message queue events, and inter-service requests. Each step is captured with precise start and end timestamps, duration, and context such as service name, operation name, and relevant attributes. This lets you pinpoint where latency or errors originate without piecing together metrics and logs manually.

A bit more behind-the-scenes work than usual this month, but I still managed to ship some public-facing features you might be interested in. Logging in, and clicking around the dashboard just got 60% faster, and we're just getting started.

The most challenging question in AI today isn’t how to build with it. It’s whether you can prove it’s worth what you’re spending on it. Every week, I hear the same thing from engineering and finance leaders: “We know the AI bills are big.

If your SaaS organization is experimenting with OpenAI, your cloud bill just got a new line item. And unless you know exactly what drives it, that line item can go from manageable to margin-killer, fast. It’s also worth clarifying that OpenAI is not the same as ChatGPT. ChatGPT is the familiar end-user app with a flat monthly subscription. OpenAI, meanwhile, is the platform behind it — a mix of models, features, and usage-based pricing that shifts depending on what you build.