Shared agentsLogs
All indexed, no sampling.
- Observability & Monitoring
//LLM Observability
Debug your agents likeyou debug your services.
Every prompt, tool call and response captured as a span, with token cost attached, scored for quality, beside the infrastructure underneath.
Prompts
Every span down to the prompt
The exact prompt the model got, the response it gave, and the tool call in between, at any depth.
Cost
Cost per call, not per month
Token cost computed onto every span from your own model pricing, so the expensive step is a row you can sort.
Privacy
Prompts stay on your infrastructure
Self-host or run in your own cloud, with redaction at ingest and field-level masking.
Purpose-built LLM tools can tell you the model took nine seconds.
Your agent wasn't slow. Your vector database was.
//Overview
What is LLM observability?
LLM observability captures every prompt, tool call and response in an LLM application or agent as traceable spans, with token usage, cost, latency and quality, so teams can debug and improve AI behaviour.
DataObserve Sense records multi-agent runs as one trace, attaches cost to each call, captures sessions across turns and scores responses, beside the infrastructure underneath, with prompts kept on your own infrastructure.
//Instrument your LLM app
Four steps,no application rewrite.
If you already emit OpenTelemetry, start at step three.
Step 1
Install the SDK
Add the telemetry SDK and your provider's instrumentation, a thin wrapper over OpenTelemetry.
//From execution graph to quality score
Reassemble what happened
Multi-agent runs, one trace
Spans stitch across process boundaries, so you see which handoff caused the failure.
Prompt-to-response analysis
Diff what the model was given against what it produced, to catch prompt drift.
Session capture across turns
Replay whole conversations with cost and quality rolled up. The failure is usually turn 5.
Alerts, SLOs and incidents
Token burn, latency and score drops page the same on-call as any outage.
//Explore more withDataObserve
More from Sense · Observability & Monitoring
Metrics
PromQL and SQL, full resolution, retained.
- Observability & Monitoring
Traces
OpenTelemetry-native, no sampling.
- Observability & Monitoring
Frontend Monitoring
Real user monitoring, session to stack trace.
- Observability & Monitoring
//FAQs
LLM Observability: questions teams ask
How is it different from APM?
APM says a request took nine seconds. LLM observability says which prompt, retrieval or tool call spent it, and what it cost.
Span, trace or session?
A span is one step, a trace one run, a session a whole conversation. Evaluations can score any of them.
Which providers are supported?
Anything on OpenTelemetry: OpenAI, Anthropic, Gemini, Mistral, Ollama, LangChain, CrewAI, LlamaIndex and more.
How do I instrument an LLM application?
Install the OpenTelemetry-based SDK, initialise instrumentation, point the exporter at your DataObserve Sense organisation and open traces. No application rewrite is needed.
Do prompts leave my infrastructure?
No. DataObserve Sense can run in your environment, so prompts and responses stay on infrastructure you control.
