Elastic Observability 8.16 introduces enhancements to LLM observability, OpenTelemetry for Kubernetes via EDOT, and streamlined onboarding processes — simplifying setup and providing deeper insights into traditional and AI-powered apps.

Introducing Elastic Observability 8.15: Elastic Distribution for OTel Collector, native OTLP profiling, AI Assistant support for Google Vertex (Gemini 1.5 Pro model), LLM observability for Azure OpenAI, and logs data set quality improvements.

Elastic Observability 8.9 introduces new AI Assistants for APM, logs, hosts, alerts, and profiling. In addition, new Time Series Data Stream (TSDS)-ready Elastic Agent integrations unlock storage efficiencies optimizing storage cost by up to 70%.

Elastic Observability’s popular integrations now use the cost-efficient time series index mode for metrics by default. Save up to 70% on storage costs for Kubernetes, Nginx, System, AWS, Azure, RabbitMQ, Redis, and more TSDS enabled integrations.

Kubernetes can be an important part of your cloud deployment, but the same capabilities that make it resilient and robust can pose challenges when something does go wrong. Learn about what IT operations teams often face when managing Kubernetes.

Elastic Observability 8.7 introduces a new user experience for working with synthetics monitors, distributed tracing for Azure Functions, and monitoring for Google Kubernetes Engine (GKE) Autopilot clusters.

Observing Kubernetes deployments can be a challenge for an SRE or IT operations professional, but it all starts with the basics: knowing which components are observable, what can be observed, and how to understand and use that telemetry data.