See every LLM call inside your traces
Instrument your app with OpenLLMetry and each model call becomes a span in KloudMate, with token counts, cost, and latency, in the same traces as the rest of your stack.
AI request failures are hard to debug without an end-to-end view.
KloudMate treats AI workflows as part of the wider application path, so teams can trace model behavior, cost, and latency inside the same distributed system context they already use for the rest of the stack.
What teams can do with LLM Observability
Instrument AI apps with OpenLLMetry, then investigate them with the same traces, dashboards, and discipline you already use for the rest of your services.
Trace model-backed workflows end to end
Follow the request path across retrieval, model calls, tool use, and downstream services in one trace, not AI metrics watched in isolation.
Watch token usage, cost, and latency
Every span carries model, prompt and completion tokens, cost, and latency. Build dashboards from those attributes to balance performance against spend.
Instrument with OpenLLMetry
Add the OpenLLMetry SDK, built on OpenTelemetry, to capture spans from OpenAI, Anthropic, LangChain, and vector stores like Pinecone, through the same pipeline as the rest of your stack.
Debug slow or failed AI requests
Open the request trace behind a bad AI response, high latency, or retry storm and inspect where the workflow actually broke down.
Understand prompt and workflow behavior
The operational goal is to connect model behavior to the full application path so AI incidents can be debugged like any other distributed workflow.
Instrument with the OpenLLMetry SDK
Add the OpenLLMetry SDK so model calls, retrieval, and tool use emit spans into the same request path as the rest of your app.
Compare usage, latency, and cost
Review the request classes or model calls driving the highest latency, token volume, or operational cost.
Open the failing workflow trace
Inspect the prompt, model, tool, and downstream steps in order to see where the AI path actually became slow or failed.
Share the finding operationally
Use reporting, logs, or incident workflows when the AI issue becomes something more than a one-off debugging task.
Track LLM usage, latency, and workflow health
LLM observability should expose the operational shape of AI traffic, not only its output. KloudMate keeps model usage and request health close enough to compare and act on them together.
- Review token usage and request latency for the AI workflows that matter most
- Understand fallback or retry behavior before it turns into a reliability or cost problem
- Compare model-backed request patterns with the rest of the application path
Debug failed or slow AI requests in the same trace flow
AI requests often fail in the spaces between the model and the rest of the application. An end-to-end trace helps teams see whether the real bottleneck is the model call, a fallback path, or a downstream dependency.
- Open the full request path for one degraded AI interaction
- Compare model latency with tool and downstream service timing
- Use the same observability workflow for AI paths and non-AI service calls
Use KloudMate Assistant to summarize degraded AI workflows
Assistant can help teams explain which AI request pattern is regressing, whether the issue looks model-driven or workflow-driven, and which trace or cost signal deserves attention first.
- Summarize Explain the model-backed workflow that changed first
- Separate Distinguish model latency from tool or downstream latency
- Guide Point responders toward the next trace, report, or log slice worth opening
Related Features
Keep the rest of the workflow close by so teams can move between detection, investigation, and response without losing context.
APM & Distributed Tracing
Trace requests across services, inspect dependencies, and move from latency symptoms to request-level evidence.
Learn moreLog Management
Search, filter, inspect, and stream logs with trace and incident context attached.
Learn moreReporting
Turn dashboards, alerts, and incidents into scheduled operational reports and summaries.
Learn moreKloudMate Assistant
Use natural language to correlate telemetry, summarize incidents, and guide the next investigation step.
Learn moreGet started
From telemetry to root cause,
in one platform.
Connect your OpenTelemetry pipeline, AWS integrations, or eBPF agent. Distributed tracing, log management, alerting, and AI-assisted investigation: unified, with predictable pricing.