Technical debt is often deprioritized until it leads to incidents. But when framed through the lens of error budgets, it becomes quantifiable—and urgent.

In this post, we’ll explore how error budgets give SREs and platform teams the leverage to escalate, prioritize, and fund tech debt work—using real data, not gut feeling.

📌Where Reliability Meets Engineering Trade-offs

Imagine a microservice that powers order fulfillment for an e-commerce platform. The service has an SLO of 99.95% success rate for requests under 500ms. That's a monthly error budget of ~21.6 minutes of degraded performance.

The latency SLI is defined as:

slo:order_latency_under_500ms = 
  sum(rate(http_request_duration_seconds_bucket{le="0.5", service="order"}[5m])) 
  / 
  sum(rate(http_request_duration_seconds_count{service="order"}[5m]))

🔍The Problem: Silent SLO Violations Due to Latency Spikes

Although availability is green, long-tail latency spikes are quietly breaching the SLO. Root cause analysis shows:

• A high-volume query on the order_items table lacks an index on the created_at column.

• Under peak traffic, this leads to increased I/O and CPU contention on the primary DB node.

• Latency rises above 500ms for ~1% of requests during promotions.

Error budget burn is not due to incidents, but chronic slowness.

📈 The Data: Burn Rate Analysis

A dedicated burn rate panel in Grafana reveals:

• 🔥 Short-term burn rate (5m) = 4.0

• 🧊 Long-term burn rate (1h) = 1.5

That’s 4x consumption rate of the error budget—and it’s not even incident-driven.

This is where traditional metrics fall short. But error budgets give you leverage.

🛠 Engineering Decision: Tech Debt Justified with Budget Metrics

This is where error budgets become powerful:

• Visibility: The burn rate makes the impact visible in dashboards and weekly ops reviews.

• scalation: Budget consumption is cited in RCA and SLO trend reports.

• Prioritization: A ticket titled:

  Title: "Add index to order_items.created_at"
  Context: Backed by burn rate graphs and impact analysis

  Now it’s no longer a “cleanup” task—it’s a reliability blocker.

The budget breach justifies:

• Adding the index via a non-blocking migration strategy.

• Increasing DB read capacity via replicas.

• Adding query performance SLI metrics to observability stack.

🚀 Outcome: Proactive Reliability Engineering

Post-mitigation:

• Latency P99 drops from 800ms → 320ms during load.

• Burn rate stabilizes to <0.4 in both short and long windows.

• Error budget alerts vanish—even during traffic spikes

More importantly, SREs were able to use metrics—not opinions—to drive action.

“In high-velocity teams, the error budget is your only real permission slip to say ‘stop’—and be taken seriously.”