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LLM Observability with Self-Hosted Langfuse: Cost Tracking, Prompt A/B Testing, and a Grafana Dashboard with Anomaly Alerts
Introduction You deployed your LLM-powered feature to production three weeks ago. Since then, you've watched your OpenAI bill climb from $200 to $1,847 with no clear explanation. You know some users are generating massive responses, but which ones? You suspect the new prompt variant is slower, but you have no latency data to prove it. Your on-call engineer got paged at 2 AM because the error rate spiked, but the logs only show "API error" with no trace context. You're flying
Pranav S
3 hours ago17 min read
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