7 lessons for IT leaders on using observability to monitor AI applications

Elastic’s IT team argues that LLM observability should be built into AI applications from their earliest releases so organizations can measure business outcomes, quality, operational performance, and costs rather than relying on projected value or anecdotes. Drawing on its internal experience, including reported operational time savings and increased digital support resolution, the company recommends defining success metrics before launch, tracking probabilistic AI-specific signals such as token usage, retrieval quality, and user intent alongside uptime and latency, and retaining baselines to compare model, prompt, and retrieval changes over time. The article advocates using OpenTelemetry’s evolving generative AI conventions instead of proprietary telemetry schemas, recording provider-reported usage data rather than estimates, and controlling high-cardinality data and sampling policies to avoid escalating monitoring expenses. It also emphasizes that many apparent model failures originate in incomplete or poorly ranked retrieved data, requiring visibility into the documents and context used for each response. Finally, it distinguishes cost monitoring from quality assurance, urging teams to connect evaluations of groundedness, accuracy, and tool use to the same traces as spending and to assess cost per successfully completed task, particularly as AI systems evolve from assistants that answer questions to agents that take multi-step actions.

No tracked trend matches for this post yet.

Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.