AI Observability — see everything your models do
Token cost, latency, and quality dashboards for every model call in your stack.
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Without observability, AI products ship blind. You can't improve what you can't measure — but most teams track LLM calls with printf logs and spreadsheet cost estimates. AI Observability delivers a purpose-built telemetry layer: every prompt, every response, every tool call traced with cost and quality metrics queryable in real time.
What's included
How we work
Best practices for AI Observability
Instrument at the SDK wrapper level, not the HTTP level
Framework-level instrumentation captures tool calls, streaming chunks, and retry counts that raw HTTP tracing misses entirely.
Define your quality rubric before collecting data
Retrofitting evaluation criteria onto stored traces is far harder than collecting structured quality scores from the start.
Sample intelligently
100% tracing in staging. Sampled in production, with full traces automatically retained on any error, cost anomaly, or quality regression.
Alert on quality regressions, not just latency and cost
Silent quality degradation is the failure mode teams most often discover from user complaints rather than dashboards.
From Evolve Edge
“Good infrastructure should be boring. The goal is to build it once, document it well, and never think about it in a crisis.”
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