20 articles
Build production observability in 2026: structured logging with Pino, metrics with Prometheus/Grafana, distributed tracing with OpenTelemetry, error tracking with Sentry, and alerting.
Build a production logging stack in 2026 with Grafana Loki: Promtail log shipping, LogQL queries, structured JSON logging, Kubernetes log collection, Grafana dashboards, log-based alerting, and the full PLG stack (Promtail + Loki + Grafana).
Implement OpenTelemetry for distributed tracing, metrics, and logs.
Implement cost attribution, anomaly detection, and forecasting to prevent runaway LLM spending and optimize your AI infrastructure.
Observe traffic and performance at the kernel level with eBPF. No code changes needed. Learn Cilium, Parca, and continuous profiling.
What eBPF actually is, Cilium for network observability, Parca for continuous profiling, BCC tools, eBPF vs traditional APM, and production safety considerations.
Unify logs, metrics, traces, and profiles in Grafana. Learn Prometheus recording rules, Loki LogQL, Tempo distributed tracing, and correlate signals for faster incident resolution.
Design Kubernetes health checks, dependency health aggregation, and graceful degradation. Learn when to check dependencies and avoid cascading failures.
Master end-to-end LLM observability with OpenTelemetry spans, Langfuse tracing, and token-level cost tracking to catch production issues before users do.
Implement comprehensive LLM observability with LangSmith/LangFuse integration, token tracking, latency monitoring, cost attribution, quality scoring, and degradation alerts.
Structured logging with pino, log levels, Loki setup, LogQL queries, log sampling, correlation IDs, and cost optimization for high-volume services.
Your logs are full. Gigabytes per hour. Health check pings, SQL query text, Redis GET/SET for every cached value. When a real error occurs, it''s buried under 50,000 noise lines. You log everything and still can''t find what you need in a production incident.
Something is wrong in production. Response times spiked. Users are complaining. You SSH into a server and grep logs. You have no metrics, no traces, no dashboards. You''re debugging a distributed system with no instruments — and you will be for hours.
Implement the three pillars: Prometheus metrics, Loki structured logging, and Tempo distributed tracing. Correlate with trace IDs for complete request visibility.
Trace LLM inference with OpenTelemetry semantic conventions. Monitor token counts, latency, agent loops, and RAG pipeline steps with structured observability.
Deploy OpenTelemetry with auto-instrumentation, custom spans, metrics, and the Collector pipeline. Export to Jaeger, Tempo, or Datadog.
Complete OpenTelemetry setup for Node.js, auto-instrumentation, custom spans, trace propagation, OTLP export to Tempo/Jaeger, sampling strategies, and production alerting.
Build comprehensive monitoring for RAG systems tracking retrieval quality, generation speed, user feedback, and cost metrics to detect quality drift in production.
Deploy Istio service mesh for automatic mTLS, traffic management, and observability. Learn sidecar injection, mTLS enforcement, canary deployments with VirtualService, circuit breaking, distributed tracing, and when a service mesh is overkill.
Most loggers are synchronous — they block your event loop writing to disk or a remote service. logixia is async-first, with non-blocking transports for PostgreSQL, MySQL, MongoDB, SQLite, file rotation, Kafka, WebSocket, log search, field redaction, and OpenTelemetry request tracing via AsyncLocalStorage.