What is AI Observability?
AI observability is the ability to monitor, understand, and diagnose AI system behavior in production environments. It includes tracking performance metrics, API costs, response quality, latency, model drift detection, and anomalies.
Three pillars
Like DevOps observability, AI observability rests on three pillars: metrics (latency, throughput, cost per query, token usage), logs (full query path from input to output, including prompts and responses), and traces (linking queries to specific agents, models, and call chains).
Why is this critical?
Without observability, you don't know: if the model started generating worse responses (drift), how much AI actually costs monthly, which queries take the most time, or whether the system is vulnerable to attacks.