Operations | Monitoring | ITSM | DevOps | Cloud

As companies grow, they inevitably hit a wall: observability data explodes while privacy requirements become stricter. For years, engineers have faced a painful tradeoff—either ship petabytes of sensitive data to a central cloud (incurring egress costs and compliance risks) or manage a complex self-hosted stack that is painful to scale.

Join Datadog CTO and Co-Founder Alexis Lê-Quôc and a special guest as they discuss emerging technologies and innovation, how they impact businesses today, and the new opportunities they create for you.

Captur builds a mobile SDK that brings real-time image recognition and actionable feedback directly into customers’ apps, running complex machine learning models entirely on device without cloud inference. This architecture delivers privacy and performance, but also creates unique challenges when it comes to observability and debugging, especially as crashes can originate from the host app rather than the SDK itself.

In this technical product demo, see how Datadog Feature Flags helps teams release software with confidence by connecting every feature flag to real-time observability data. Configure progressive, multi-step rollouts with automated guardrails tied to APM, RUM, and Product Analytics so you can pause or roll back instantly if latency, errors, or key business metrics degrade.

When incidents strike, speed and clarity are critical. Datadog Incident Response brings the full incident lifecycle into one platform so teams can move from detection to resolution with confidence. Operate from a single, unified view of your systems, coordinate across the tools your teams already use, and leverage AI that analyzes incidents in real time to surface context, guide decisions, and accelerate resolution.

See how you can ensure trust across the data life cycle in February’s episode of This Month in Datadog. Join us for a spotlight of Datadog Data Observability, which enables you to detect data quality and pipeline issues early, as well as remediate those issues with end-to-end lineage. Plus, we cover: Protecting agentic AI applications from real-time threats with Datadog AI Guard Staying up to date and reducing steps to collaborate with five new Incident Management releases Releasing software with confidence using Datadog Feature Flags.

Pod annotations in Kubernetes with invalid JSON syntax can prevent Datadog Autodiscovery from detecting integrations, resulting in missing metrics and gaps in monitoring. Watch this video for a step-by-step process to write annotations: Note: This video focuses on Datadog Autodiscovery v2 syntax.

How do you maintain 99.99 percent uptime across thousands of Kubernetes hosts and multiple cloud providers? Okta engineers explain why observability is critical to keeping authentication and authorization services running at scale. Watch how Okta uses Datadog to bring metrics, logs, and traces into a single view, speed up root cause analysis, and reduce time to mitigation while controlling costs.

Normalizing security logs into the Open Cybersecurity Schema Framework (OCSF) is often complex, manual, and time-consuming. With Datadog Observability Pipelines, you can easily transform logs into OCSF format—right in your own environment—before routing them to destinations like Splunk, CrowdStrike, and AWS Security Lake. This video show how Security teams can use Observability Pipelines to: Collect, process, and transform logs into OCSF format automatically.

Balancing performance and cost in Kubernetes doesn’t have to be a tradeoff. In this short demo, you’ll see how Datadog helps teams rightsize workloads, reduce over-provisioning, and measure real cost savings using Kubernetes autoscaling.