Operations | Monitoring | ITSM | DevOps | Cloud

We built Bits AI SRE to help engineers investigate and solve production incidents, one of the most difficult aspects of operating distributed systems today. As environments grow more dynamic and complex, resolving issues becomes more challenging. Failures now span more services, involve noisier signals, and encompass larger volumes of telemetry data, making it hard for on-call engineers to find root causes quickly. Today, Bits AI SRE is already helping teams decrease time to resolution by up to 95%.

Flaky tests are a significant source of inefficiency that impacts many engineering teams. Along with failing your build, they interrupt your entire development flow, generate excessive CI/CD noise, and, critically, compromise developer trust in the test suite itself. Datadog Test Optimization enables you to manage test suites at scale by pinpointing the flakiest tests, analyzing their history across hundreds of runs, and automatically surfacing the root cause.

In most organizations, leaders are surrounded by data: revenue reports, customer analytics, uptime metrics, support tickets, and more.

The year 2025 marked a major milestone in the Datadog integrations ecosystem as we surpassed 1,000 integrations. Along the way, we also added over 110 new technology partners and expanded coverage across the fastest growing software categories, including AI, distributed security, hybrid infrastructure, and data intelligence. This recap highlights the most impactful integrations we released this year and how they connect to these broader technology trends.

Comprehensive observability is critical for running performant, reliable, and secure serverless workloads. However, configuring and maintaining that visibility across hundreds or thousands of serverless functions can be difficult to scale and sustain. Developers across teams often manage serverless functions using different infrastructure as code (IaC) frameworks, as well as different review, deployment, and update processes.

dbt is one of the most popular solutions for data transformations and modeling. Many commercial data pipelines rely on dozens, or even hundreds, of individual dbt jobs. Data engineers, data platform engineers, and analytics engineers who own these pipelines need to maintain a testing framework to prevent mistakes in data processing that can compromise analysis.

Developers and SREs who rely on GitLab to develop their services often face significant friction when troubleshooting errors or fixing issues that degrade code quality. To understand the context of a problem, they resort to tab-hopping between observability tools and GitLab, connecting stack traces, spans, and profiles back to the right files and commits.

Security teams need visibility across every system they defend, including cloud platforms, SaaS applications, security controls, identity providers, and custom services. But those systems all produce logs in different formats, with inconsistent field names and structures. That lack of standardization makes it harder to correlate events, write reusable detections, and investigate incidents quickly.