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The latest News and Information on Application Performance Monitoring and related technologies.

How Inkeep Monitors Their AI Agent Framework with SigNoz

AI agents are fundamentally different beasts to monitor compared to traditional applications. A single user request can trigger a cascade of 10+ internal operations: sub-agent transfers, tool executions, LLM calls, API requests, each with unpredictable latency and failure modes. When something goes wrong (and with LLMs, things go wrong in creative ways), you need to see the entire execution flow to debug effectively.

How Datadog Manages 50,000 Apache Iceberg Tables at Scale

Think managing a few database tables is hard? Try 50,000 production Iceberg tables storing petabytes of data with 8 million scans per day. In this clip, Datadog's platform team reveals the architecture choices behind their managed Iceberg implementation that serves hundreds of internal engineering teams.

Datadog at AWS re:Invent, Bits AI SRE, MCP Server, CloudPrem, and more | This Month in Datadog

Get a closer look at features we announced at AWS re:Invent in the latest episode of This Month in Datadog. Tune in for spotlights of Bits AI SRE, now generally available, and Datadog’s MCP Server, which connects AI agents to our platform by ingesting prompts and mapping them to Datadog resources and data. Plus, we cover how to: This Month in Datadog brings you the latest updates on our newest product features, announcements, resources, and events.

Datadog on Apache Iceberg

Historically, Datadog has relied on technologies like Snowflake and Apache Spark on raw parquet files (lacking consistent table structure) to power internal analytics and data science at scale. As usage grew across product teams, more features depended on data science teams, and our datasets grew to include more telemetry data, these systems became complex to manage and govern both technically and financially. The need for a more flexible and scalable solution led Datadog to adopt Apache Iceberg, an open source table format for data lakes that brings reliability and performance while remaining SQL-friendly.
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Adding a CDN to a load balancer (for a much faster website)

Here at Raygun, we like to go fast. Really fast. That's what we do! When we see something that isn't zooming, we try to figure out how to make it go faster. So today, we're answering a simple (and relevant) question; how do we make our public site, raygun.com, much, much faster? The answer, at first glance, is simple-we build it into a Content Delivery Network (CDN). But what if you have a load balancer serving your website, and you don't want to rebuild everything to serve from a CDN? Well, that's more complicated. Let's start by describing the issue.

Optimize Your Oracle Cloud (OCI) Spend with Datadog Cloud Cost Management

Support for Oracle Cloud Infrastructure (OCI) is now live in Datadog Cloud Cost Management. In this short demo, you’ll learn how to: Get granular visibility into OCI cost and usage—by service, compartment, tag, and resource tier. Uncover savings opportunities by combining cost data with observability metrics like CPU, memory, and storage utilization. Set up anomaly monitors and budgets to avoid cost overruns—especially for high-risk workloads like AI and GPU training.

Datadog Bits AI SRE: Your new teammate for on-call shifts

Bits AI SRE is an always-on SRE agent built to handle complex troubleshooting and late-night alerts. Developed against thousands of real-world incidents and powered by Datadog’s platform, Bits AI SRE analyzes your entire stack, tests hypotheses, and identifies root causes in minutes. Resolve faster, get back to sleep sooner, and give your on-call team the confidence and capacity they need.

Patterns for Deploying OpenTelemetry Collector at Scale

So, you've embraced OpenTelemetry, and it's been great. Pat, Pat. That single, vendor-neutral pipeline for your traces, metrics, and logs felt like the future. But now, the future is getting bigger. That simple OTel Collector configuration that worked perfectly for a few services is starting to show its limits as you scale. The data volume is climbing, reliability is becoming a concern, and you're wondering if that single collector instance is now a bottleneck waiting to happen.