23 articles
Build production observability in 2026 with structured logging, Prometheus metrics, Grafana dashboards, distributed tracing via OpenTelemetry, and alerting rules that catch issues before users notice.
Build a production logging stack with Grafana Loki in 2026: Promtail log shipping, LogQL queries, structured JSON logs, Kubernetes DaemonSet collection, Grafana dashboards, and log-based alerting.
Build a production monitoring stack with Prometheus and Grafana: metrics collection, PromQL queries, alerting with Alertmanager, Kubernetes monitoring, and dashboard design for DevOps teams.
Deploy the ELK Stack for centralized log management: Elasticsearch indexing, Logstash pipelines, Filebeat shipping, Kibana dashboards, and production tuning for high-volume log ingestion.
Implement OpenTelemetry for vendor-neutral distributed tracing, metrics, and logs: SDK setup for Node.js and Python, collector configuration, Jaeger integration, and context propagation across microservices.
Compare Datadog and New Relic for application monitoring: pricing models, features, integrations, setup experience, and practical guidance for choosing the right APM platform for your team and budget.
A practical guide to Site Reliability Engineering covering SLIs, SLOs, error budgets, toil reduction, blameless postmortems, and on-call practices. For engineers and teams aiming to balance feature velocity with production reliability.
Implement cost attribution, anomaly detection, and forecasting to prevent runaway LLM spending and optimize your AI infrastructure.
eBPF runs sandboxed programs in the Linux kernel to observe traffic and performance without modifying application code. Learn Cilium for network visibility, Hubble for service-to-service flows, Parca for continuous profiling, and bpftrace for ad-hoc investigation.
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, trace correlation, adaptive sampling, and anomaly detection 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.