What Capacity Planning Solves#

Running out of capacity during a traffic spike causes outages. Over-provisioning wastes money continuously. Capacity planning is the process of measuring what you use now, projecting what you will need, and ensuring resources are available before demand arrives. Without it, you are either constantly firefighting resource exhaustion or explaining to finance why your cloud bill doubled.

Capacity planning is not a one-time exercise. It is a recurring process – monthly for fast-growing services, quarterly for stable ones.

Kubernetes Cost Audit and Reduction#

Kubernetes clusters accumulate cost waste silently. Resource requests padded “just in case” during initial deployment never get revisited. Load balancers created for debugging stay running. PVCs from deleted applications persist. Over six months, a cluster originally running at $5,000/month can drift to $12,000 with no corresponding increase in actual workload.

This operational plan works through cost reduction systematically, starting with visibility (you cannot cut what you cannot see), moving through quick wins, then tackling the larger structural optimizations that require data collection and careful rollout.

Kubernetes FinOps: Decision Framework for Cost Optimization#

FinOps in Kubernetes is the practice of bringing financial accountability to infrastructure spending. The challenge is not a lack of cost-saving techniques – it is knowing which ones to apply first, which combinations work together, and which ones introduce risk that outweighs the savings. This article provides a structured decision framework for selecting and prioritizing Kubernetes cost optimization strategies.

The Five Optimization Levers#

Every Kubernetes cost optimization effort works across five levers. Each has a different risk profile, implementation effort, and savings ceiling.

Why Load Test#

Performance problems discovered in production are expensive. A service that handles 100 requests per second in dev might collapse at 500 in production because connection pools exhaust, garbage collection pauses compound, or a downstream service starts throttling. Load testing reveals these limits before users do.

Load testing answers specific questions: What is the maximum throughput before errors start? At what concurrency does latency degrade beyond acceptable limits? Can the system sustain expected traffic for hours without resource leaks? Will a traffic spike cause cascading failures?

“I’m Not Seeing Metrics” – Systematic Diagnosis#

This is the most common observability complaint. Work through these steps in order to isolate where the pipeline breaks.

Step 1: Is the Target Being Scraped?#

Open the Prometheus UI at /targets. Search for the job name or target address. Look at three things: state (UP or DOWN), last scrape timestamp, and error message.

Status: UP    Last Scrape: 3s ago    Duration: 12ms    Error: (none)
Status: DOWN  Last Scrape: 15s ago   Duration: 0ms     Error: connection refused

If the target does not appear at all, Prometheus does not know about it. This means the scrape configuration (or ServiceMonitor) is not matching the target. Jump to the ServiceMonitor checklist at the end of this guide.

What OpenTelemetry Is#

OpenTelemetry (OTel) is a vendor-neutral framework for generating, collecting, and exporting telemetry data: traces, metrics, and logs. It provides APIs, SDKs, and the Collector – a standalone binary that receives, processes, and exports telemetry. OTel replaces the fragmented landscape of Jaeger client libraries, Zipkin instrumentation, Prometheus client libraries, and proprietary agents with a single standard.

The three signal types:

• Traces: Record the path of a request through distributed services as a tree of spans. Each span has a name, duration, attributes, and parent reference.
• Metrics: Numeric measurements (counters, gauges, histograms) emitted by applications and infrastructure. OTel metrics can be exported to Prometheus.
• Logs: Structured log records correlated with trace context. OTel log support bridges existing logging libraries with trace correlation.

The OTel Collector Pipeline#

The Collector is the central hub. It has three pipeline stages:

The Action Item Problem#

Post-mortem reviews produce action items. Teams agree on what needs to change. Then weeks pass, priorities shift, and items quietly decay into a backlog nobody checks. The next incident hits the same root cause, and the post-mortem produces the same action items again.

Studies of recurring incidents consistently show the root cause was identified in a previous post-mortem, and the corresponding action item was never completed. Action item tracking is the mechanism by which incidents make systems more reliable instead of just more documented.

Prometheus Architecture#

Prometheus pulls metrics from targets at regular intervals (scraping). Each target exposes an HTTP endpoint (typically /metrics) that returns metrics in a text format. Prometheus stores the scraped data in a local time-series database and evaluates alerting rules against it. Grafana connects to Prometheus as a data source and renders dashboards.

Scrape Configuration#

The core of Prometheus configuration is the scrape config. Each scrape_config block defines a set of targets and how to scrape them.

Pull-Based Scraping Model#

Prometheus pulls metrics from targets rather than having targets push metrics to it. Every scrape interval (default 15s in the global config), Prometheus sends an HTTP GET to each target’s metrics endpoint. The target responds with all its current metric values in Prometheus exposition format.

This pull model has concrete advantages. Prometheus controls the scrape rate, so a misbehaving target cannot flood the system. You can scrape a target from your laptop with curl http://target:8080/metrics to see exactly what Prometheus sees. Targets that go down are immediately detectable because the scrape fails.

Instant Vectors vs Range Vectors#

An instant vector returns one sample per time series at a single point in time. A range vector returns multiple samples per time series over a time window.

# Instant vector: current value of each series
http_requests_total{job="api"}

# Range vector: last 5 minutes of samples for each series
http_requests_total{job="api"}[5m]

You cannot graph a range vector directly. Functions like rate() and increase() consume a range vector and return an instant vector, which Grafana can then plot.