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LLM OBSERVABILITY
LLM observability
Prompts, completions, latency, tokens and eval scores for every model call, linked to the user and workflow it served.
The problem
Model metrics alone do not explain failures
A latency spike or quality drop matters only when you know the prompt, model, user and workflow it affected.
How Anosys helps
Model calls in context
Each call is part of a trace with eval scores, token cost and product events attached, so you can see who was affected and why.
Use LLM observability to answer the hard questions
01
What changed?
Track model versions, parameters, prompts, tools, providers, and release changes.
02
What did it cost?
Attribute token and model spend to teams, users, agents, workflows, and outcomes.
03
Was it good?
Connect model behavior to evals, AutoJudge scores, safety checks, and quality trends.
04
Who is affected?
Join model telemetry with customer experience, business KPIs, and process health.