Production-Grade Observability with Prometheus + Grafana
Real-time metrics, dashboards, and SLO visibility for distributed systems. Monitor what matters, respond faster, and maintain reliability at scale.
Live Prometheus Metrics
What is Observability?
Observability is the ability to understand the internal state of a system by examining its outputs. Unlike traditional monitoring, which asks predefined questions, observability enables you to ask any question about your system's behavior.
Using Prometheus for metrics collection and Grafana for visualization, I implement comprehensive observability across all production systems. This includes:
RED Metrics
- • Rate: Requests per second
- • Errors: Failed request rate
- • Duration: Request latency (p50, p95, p99)
USE Metrics
- • Utilization: Resource usage (CPU, memory)
- • Saturation: Queue depth, backlog
- • Errors: System-level failures
Real Use Case: Text2SQL Query Engine
How observability helped maintain 99.5% query accuracy for an AI-powered natural language to SQL system
Challenge: The Text2SQL Query Engine needed to maintain sub-500ms p95 latency while translating natural language queries to SQL with high accuracy. Without visibility into the LLM pipeline, debugging query failures would require parsing logs across the API, schema resolver, and LLM layers.
Solution: Implemented Prometheus metrics for:
- • Token usage per request
- • LLM response latency (p50, p95, p99)
- • Schema context cache hit rate
- • SQL syntax validation rate
- • Query execution success rate
- • Fallback/retry frequency
Impact: Reduced mean time to detection (MTTD) from hours to minutes. When LLM latency spiked due to context overflow, alerts fired before users reported timeouts, allowing proactive token optimization.
How It Helps
SLO Tracking
Define and track Service Level Objectives. Know when you're burning error budget before SLA violations occur.
Incident Response
Correlate metrics across services during incidents. Identify root causes faster with historical data and trend analysis.
Capacity Planning
Use historical trends to predict resource needs. Scale proactively based on data, not guesswork.