Observability: Monitoring, Metrics, Prometheus & Grafana Guide
Metrics, dashboards, and alerting for production systems — Prometheus, Grafana, Kubernetes, and AI workloads.
Observability is not optional in production systems.
If you are running:
- Kubernetes clusters
- AI model inference workloads
- GPU infrastructure
- APIs and microservices
- Cloud-native systems
You need more than logs.
You need metrics, alerting, dashboards, and system visibility.
This pillar covers modern observability architecture with a focus on:
- Prometheus monitoring
- Grafana dashboards
- Metrics collection
- Alerting systems
- Production monitoring patterns
What Is Observability?
Observability is the ability to understand the internal state of a system using external outputs.
In modern systems, observability consists of:
- Metrics – quantitative time-series data
- Logs – discrete event records
- Traces – distributed request flows
Monitoring is a subset of observability.
Monitoring tells you something is wrong.
Observability helps you understand why.
In production systems — especially distributed systems — this distinction matters.
Monitoring vs Observability
Many teams confuse monitoring and observability.
| Monitoring | Observability |
|---|---|
| Alerts when thresholds are crossed | Enables root cause analysis |
| Focused on predefined metrics | Designed for unknown failure modes |
| Reactive | Diagnostic |
Prometheus is a monitoring system.
Grafana is a visualization layer.
Together, they form the backbone of many observability stacks.
Prometheus Monitoring
Prometheus is the de facto standard for metrics collection in cloud-native systems.
Prometheus provides:
- Pull-based metrics scraping
- Time-series storage
- PromQL querying
- Alertmanager integration
- Service discovery for Kubernetes
If you are running Kubernetes, microservices, or AI workloads, Prometheus is likely already part of your stack.
Start here:
This guide covers:
- Prometheus architecture
- Installing Prometheus
- Configuring scrape targets
- Writing PromQL queries
- Setting up alert rules
- Production considerations
Prometheus is simple to start with — but subtle to operate at scale.
Grafana Dashboards
Grafana is the visualization layer for Prometheus and other data sources.
Grafana enables:
- Real-time dashboards
- Alert visualization
- Multi-datasource integration
- Team-level observability views
Getting started:
Installing and Using Grafana on Ubuntu
Grafana transforms raw metrics into operational insight.
Without dashboards, metrics are just numbers.
Observability in Kubernetes
Kubernetes without observability is operational guesswork.
Prometheus integrates deeply with Kubernetes through:
- Service discovery
- Pod-level metrics
- Node exporters
- kube-state-metrics
Observability patterns for Kubernetes include:
- Monitoring resource usage (CPU, memory, GPU)
- Alerting on pod restarts
- Tracking deployment health
- Measuring request latency
Prometheus + Grafana remains the most common Kubernetes monitoring stack.
Observability for AI & LLM Infrastructure
This site focuses heavily on AI systems.
Observability is critical for:
- Monitoring LLM inference latency
- Tracking token throughput
- Measuring GPU utilization
- Alerting on model failures
- Monitoring embedding pipelines
Prometheus can expose metrics such as:
- Requests per second
- Latency percentiles (P50, P95, P99)
- GPU memory usage
- Queue depth
- Error rates
For AI systems, observability is not just infrastructure — it is model reliability.
Metrics vs Logs vs Traces
Metrics are ideal for:
- Alerting
- Performance trends
- Capacity planning
Logs are ideal for:
- Event debugging
- Error diagnosis
- Audit trails
Traces are ideal for:
- Distributed request analysis
- Microservice latency breakdown
A mature observability architecture combines all three.
Prometheus focuses on metrics.
Grafana visualizes metrics and logs.
Future expansions may include:
- OpenTelemetry
- Distributed tracing
- Log aggregation systems
Common Monitoring Mistakes
Many teams implement monitoring incorrectly.
Common mistakes include:
- No alert thresholds tuning
- Too many alerts (alert fatigue)
- No dashboards for key services
- No monitoring for background jobs
- Ignoring latency percentiles
- Not monitoring GPU workloads
Observability is not just installing Prometheus.
It is designing a system visibility strategy.
Production Observability Best Practices
If you are building production systems:
- Monitor latency percentiles, not averages
- Track error rates and saturation
- Monitor infrastructure and application metrics
- Set actionable alerts
- Regularly review dashboards
- Monitor cost-related metrics
Observability should evolve with your system.
How Observability Connects to Other IT Aspects
Observability is tightly connected to:
- Kubernetes operations
- Cloud infrastructure (AWS, etc.)
- AI inference systems
- Performance benchmarking
- Hardware utilization
Observability is the operational backbone of all production systems.
Final Thoughts
Prometheus and Grafana are not just tools.
They are foundational components of modern infrastructure.
If you cannot measure your system, you cannot improve it.
This observability pillar will expand as monitoring patterns evolve — from metrics to full system introspection.
Explore Prometheus and Grafana guides above to get started.
