Frugal Signals

When your observability stack costs more than your cloud compute, something’s broken.

Plenty of engineering teams have been there. You check the invoice and—bam—$10,000 for monitoring tools that mostly send noise, not insight. These fancy SaaS platforms? Sure, they’re powerful. But they often charge for stuff you don’t use and data you didn’t mean to send. Suddenly, observability feels like a luxury.

It shouldn’t be.

With a smart setup and the right open-source tools, full-stack observability—metrics, logs, traces, and dashboards—can run under $200 a month.

The SaaS Sticker Shock

Take a mid-sized e-commerce team—we’ll call them SpendThrift Inc. They signed up for a slick observability platform packed with features: real-time dashboards, auto-scaling agents, AI-driven everything.

What they got?

• A flood of alerts
• A confusing, opaque bill
• A monthly charge close to $10,000

Worse, they only used maybe 10% of the features.

So they stepped back and asked: What do we actually need?

Turns out, not that much:

• Pod and container metrics
• Centralized logs
• Simple alerting
• Basic tracing for latency issues

They rebuilt their stack using open-source tools:

• Prometheus for metrics
• Grafana for dashboards
• Loki for logs
• Terraform for infrastructure

Here’s a simplified Prometheus deployment using Terraform:

provider "kubernetes" {
  config_path = "~/.kube/config"
}

resource "kubernetes_deployment" "prometheus" {
  metadata {
    name = "prometheus"
    labels = { app = "prometheus" }
  }

  spec {
    replicas = 1
    selector { match_labels = { app = "prometheus" } }
    template {
      metadata { labels = { app = "prometheus" } }
      spec {
        container {
          name  = "prometheus"
          image = "prom/prometheus:v2.30.3"
          ports { container_port = 9090 }
        }
      }
    }
  }
}

They hooked logs into Loki, visualized everything in Grafana, and wrote a few alerting rules. Done.

Total cost? Around $200/month—mostly storage and compute. And no more bloat.

Tracing Without the Price Tag

Another case: HyperScale Solutions, a growing SaaS startup. They were tight on budget and drowning in incidents. Their enterprise-grade observability platform? Expensive. And not much help when things broke.

So they went DIY.

They built a clean, lean observability pipeline:

• OpenTelemetry for collecting traces
• Jaeger to view them
• Prometheus and Grafana for metrics
• Loki for logs

Here’s what tracing looked like in Kubernetes, using environment variables:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: orders-service
spec:
  replicas: 3
  selector:
    matchLabels:
      app: orders
  template:
    metadata:
      labels:
        app: orders
    spec:
      containers:
      - name: orders
        image: orders-service:latest
        env:
        - name: OTEL_SERVICE_NAME
          value: "orders-service"
        ports:
        - containerPort: 8080

Now they could trace a request across services, tie logs to slow spans, and fix problems faster.

The results?

• 70% drop in downtime
• Fewer false alerts
• $200/month total spend

They also cut time-to-resolution and kept their users happier.

Cost Breakdown (and the Tradeoffs)

Rough monthly spend: ~$200
Covers compute, storage (logs and metrics), and bandwidth.

A few tradeoffs to note:

• Setup isn’t magic. You’ll need engineering time to deploy and maintain.
• Support is DIY. It’s you, GitHub issues, and community Slack.
• Security is your job. You own the stack, so you lock it down and scale it right.

What to Remember

• You don’t need to spend five figures for observability.
• Open-source tools are powerful and production-ready.
• A lean stack that fits your needs will outperform a bloated one that doesn’t.
• You trade ease for control—and serious cost savings.

If your monitoring bill feels like a second cloud bill, maybe it’s time for a reset. With $200 and a little elbow grease, you can build something better.

And yes—you’ll still have enough left over for pizza.