How to Build Your Infrastructure Monitoring in 2026 ·<br>How to Build Your Infrastructure Monitoring in 2026<br>August 5, 2026<br>· observability, monitoring, sre, opentelemetry, victoriametrics, loki, jaeger, vector<br>Every year I get asked the same question by teams starting from scratch: “we have Grafana, we have some dashboards, why do we still get paged for things we didn’t see coming?” Most of the time, the answer isn’t a missing tool. It’s a missing method. Teams jump straight to “let’s install Prometheus” or “let’s buy a SaaS observability platform” before answering a much simpler question: what does “healthy” actually mean for this business?<br>I’ve built infrastructure monitoring from the ground up for several companies now, and I keep coming back to the same seven steps. This article is that playbook, the way I actually apply it in 2026.<br>Requirements<br>Before you touch any tool, you need:<br>A clear list of the critical business flows your system supports (payments, checkouts, logins, API calls…)<br>Buy-in from the team on what “acceptable” looks like for those flows<br>A telemetry stack that can handle metrics, logs, and traces (I’ll give you mine below)<br>If you get stuck at any point : reach out, I’m happy to help you think through your specific setup.
1. Start with the business SLI/SLO, not with the tool<br>This is the step almost everyone skips, and it’s the one that matters the most. Before deciding what to monitor, decide what “working” means for your business.<br>An SLI (Service Level Indicator) is a metric that reflects user-facing behavior. An SLO (Service Level Objective) is the target you set for that metric over a time window.<br>Example, if you work for a banking company:<br>SLI : the ratio of successful payment authorizations over total payment authorization attempts<br>SLO : 99.95% of payment authorizations should succeed over a rolling 30-day window<br>That single sentence changes everything downstream. It tells you:<br>Which service is “tier 0” (payment authorization service)<br>What your error budget is (0.05% of failed authorizations per month)<br>What should page someone at 3am, and what can wait for Monday morning<br>Do this exercise for every critical business flow before writing a single scrape config. If you skip it, you’ll end up monitoring infrastructure CPU graphs while your actual business metric silently burns through its error budget.<br>2. Know what to monitor, then pick your stack<br>Once your SLIs/SLOs are defined, list what you actually need visibility into to measure them:<br>Infrastructure : nodes, Kubernetes clusters, network, databases, message queues<br>Applications : HTTP servers, HTTP clients, background jobs, gRPC services<br>Business layer : the actual events tied to your SLI (a payment authorization call, a checkout event…)<br>Only now do you pick the tech stack, because now you know what it needs to support. Here’s the generic stack I use on most projects:<br>PillarToolRoleMetricsVictoriaMetricsLong-term, cost-efficient metrics storage (Prometheus-compatible)Metrics agentvmagentScraping and remote-writing metricsLogsLokiLog aggregation, indexed by labels not full textLogs & traces ingestionOpenTelemetry CollectorVendor-neutral receiver/processor/exporter pipelineTracesJaegerDistributed trace storage and visualizationThe three pillars, and what each is actually for<br>It’s worth being explicit about this, because teams often use the wrong pillar to answer the wrong question:<br>Metrics : aggregated, cheap to store, great for trending and alerting. They answer “what” and “how much” (error rate is 2%, p99 latency is 800ms).<br>Logs : high cardinality, detailed, expensive to store at full fidelity. They answer “why” during an investigation (this specific request failed because of X).<br>Traces : the causal chain across services. They answer “where” in a distributed call the latency or error actually happened.<br>None of the three replaces the others. Metrics tell you something is wrong, traces tell you where, logs tell you why.<br>Want this set up for your team?<br>I help teams design alerting that doesn't wake anyone up unnecessarily.<br>Book a free audit call<br>3. Implement and scrape, favor auto-instrumentation<br>Now you build the pipeline. My rule of thumb: instrument automatically first, add manual instrumentation only where auto-instrumentation doesn’t reach (custom business logic, internal queues, batch jobs).<br>Use the OpenTelemetry auto-instrumentation libraries for your language. They hook into common frameworks (HTTP servers, HTTP clients, database drivers, gRPC) and emit metrics, traces, and sometimes logs without you writing a single line of instrumentation code.<br>Example, a Java service auto-instrumented and shipped straight to your collector, no code change required:<br>java -javaagent:opentelemetry-javaagent.jar...