Operations | Monitoring | ITSM | DevOps | Cloud

From point-of-sale (POS) terminals to cloud-based applications and mobile devices, reliable connectivity is critical to business operations. Even brief disruptions can negatively impact user experiences, resulting in failed transactions, delayed application responses, or repeated attempts to reconnect. Juniper Mist is an AI-powered networking platform that provides insight into wireless environments, including access point performance and radio frequency health.

Many organizations rely on Oracle Fusion Cloud Applications to run core business workflows across finance, HR, and supply chain operations. Because these SaaS-based applications run on Oracle Cloud Infrastructure (OCI), engineering teams have limited visibility into their performance. Without direct access to the underlying stack, they often lack the signals needed to detect regressions or investigate degraded user experience.

Kubernetes environments generate a constant stream of signals across clusters, nodes, pods, and workloads. For teams that have standardized on OpenTelemetry (OTel), maintaining ownership of that data is critical. But in practice, many observability platforms require translation into vendor-specific data formats, leading to fragmented product experiences, blank dashboards, and uncertainty about data integrity.

LLM applications rarely crash. They degrade quietly. Once these applications are shipped to production, subtle quality failures become harder to catch with traditional signals. Tone shifts, hallucinated details, off-topic responses, and incomplete reasoning can emerge while latency and token usage look stable.

Datadog supports managing Datadog configuration as code through the Datadog Terraform provider. As platform engineering practices evolve, we are focused on making this provider more reliable and trustworthy at enterprise scale.

The 2025 State of Containers and Serverless report found that 64% of organizations use the Kubernetes Horizontal Pod Autoscaler (HPA) to manage Kubernetes workload capacity. But only 20% of those deployments scale on custom metrics. The other four-fifths of organizations rely on resource metrics—CPU and memory utilized by their pods—to trigger autoscaling activity.