Operations | Monitoring | ITSM | DevOps | Cloud

See how Datadog turns cloud usage and performance data into actionable business insights by helping teams calculate unit economics to measure and optimize the efficiency of every service. You’ll discover how to: Datadog bridges the gap between cloud costs and business value—helping organizations get the most value out of their cloud investment.

See how Datadog Cloud Cost Management combines observability and cost data with actionable automation to help teams optimize spend. In this short demo, you’ll learn how to: With Datadog Cloud Cost Management, cost optimization is built into the same platform engineers use every day.

Never contributed to open source and feeling intimidated? Same. Before joining Datadog, Alessandro had zero open source experience. Now he's a regular contributor to Apache Iceberg. Here's exactly how he got started. Step 1: Join the Slack community and answer user questions. Step 2: Look for "good first issue" tags in the repo. Step 3: Remember that opening bug reports and doing code reviews count as contributions too.

Learn more by watching the full episode our latest This Month in Datadog series.

Everyone talks about how great Apache Iceberg is, but nobody warns you about this: without proper maintenance, your tables will bloat, queries will slow down, and your catalog will run out of memory. Here are the 4 critical operations you MUST run regularly. Expiring snapshots prevents metadata bloat (Datadog learned this the hard way with catalog memory pressure). Deleting orphan files cleans up failed writes. Compacting data files keeps streaming workloads fast. Compacting manifests optimizes query planning.

Want to make your logs easier to work with? Excluding unneeded logs from indexing reduces noise and may reduce log management costs. In this video, you’ll learn how to: See for yourself how to improve log utilization with Datadog Log Patterns and log exclusion filters. Then set up an alert to track ingestion spikes.

Training an AI foundation model on over a trillion data points sounds impossible without hitting your production systems. Here's how Datadog did it with Apache Iceberg for their time series forecasting model TOTO. The key challenge: extracting massive historical observability data (metrics spanning years) and running incremental preprocessing pipelines without overwhelming production services. Iceberg solved this by providing schema governance, consistency guarantees, and seamless integration with ML tools like Ray and PyTorch.

Tired of waiting days to see your AWS bill spike? Datadog solved this problem using Apache Iceberg to deliver real-time cloud cost visibility - updating every 15 minutes instead of waiting for billing data. Here's how it works: They sync real-time resource inventory (EC2 instances, Kubernetes pods) into Iceberg tables, then use Trino to join those snapshots with unit pricing data. The result? FinOps teams can catch cost anomalies before they become budget disasters.

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.

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.

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.

We introduced Bits AI SRE, our first AI agent, now generally available. Across industries, customers of all sizes are already seeing faster resolution, stronger reliability, and a better on-call experience for their teams.

Using SigNoz MCP Server & Claude to find root cause of Alerts.

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.

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.