Operations | Monitoring | ITSM | DevOps | Cloud

TL;DR Logz.io AI Observability provides end-to-end tracing for AI agents and LLM applications. Built on OpenTelemetry, it lets you trace every request from prompt to backend service, visualize multi-step agent execution, isolate root causes across models and infrastructure, and track token usage per step, all alongside your existing logs, metrics, and traces.

TL;DR Cutting telemetry volume is only half of cost management. The other half is sending each signal to the right destination and storage tier, adjusting policies as traffic and incidents change, and rolling out every change in phases that teams can measure and reverse.

TL;DR “AI observability” now describes two different jobs. One is watching the LLM apps and AI agents your team ships: prompts, tool calls, token spend, and output quality. The other is putting AI agents to work on your production telemetry so incidents are already being investigated by the time an engineer logs in. Most engineering orgs in 2026 need both, and very few tools cover the two well.

TL;DR Log and telemetry costs climb because every record gets shipped, indexed, and stored at the same priority. The fix starts before the backend: classify signals by operational value, filter what has none, and sample high-volume streams so you keep the evidence that matters and stop paying for the rest.