Shipping an LLM feature without observability is shipping a black box. Langfuse gives us trace-level visibility–which prompts ran, token costs, latency percentiles, and user feedback tied to specific generations–so regressions are searchable, not anecdotal.
Grafana dashboards aggregate the signals ops teams already trust: error rates, queue depth, embedding API timeouts, and cache hit ratios for retrieval. AI workloads sit next to the rest of the platform, not in a separate silo nobody watches.
MLflow and Weights & Biases handle experiment tracking when teams fine-tune models or compare retrieval strategies. Hyperparameters, evaluation scores, and artifact versions link back to the deployment that went live.
The goal is operational confidence: when a client asks why answers degraded last Tuesday, we have traces, metrics, and experiment history–not a guess about which model version shipped.
