The Business Case for Chaos Engineering: An ROI Calculator for Testing Application Reliability

Source: Dev.to

Chaos engineering is one of the best ways to proactively test application reliability, but many leadership teams have never heard of the concept. Engineering teams need to be able to frame a strong business case to explain the value of chaos engineering and reliability testing to budget holders. When there’s a major outage, the value of application reliability becomes immediately clear, but a solid ROI plan can help earn and maintain executive support while your systems are steady.

Introducing the Interactive ROI Calculator

We have just released an interactive ROI calculator that can help SRE teams frame the business value of proactive reliability efforts like chaos engineering.

Why Chaos Engineering Isn’t “More Chaos”

“We have enough chaos already!” – a common response when we ask teams about their approach to system reliability.

When done correctly, chaos engineering doesn’t add chaos to your systems. Instead, it runs controlled scenarios to validate how resilient your systems are under stressful conditions. Examples include:

• Availability‑zone outage
• Delayed dependency response
• Sudden surge of users

These experiments create a feedback loop earlier in the software development cycle, enabling teams to design more fault‑tolerant systems for their users.

Benefits of Reliability Testing

• Risk discovery – Find and address issues that could lead to critical incidents.
• Improved MTTR – Reduce average time to remediation (Mean Time To Recovery).
• Incident reduction – Focus on decreasing the number of high‑severity incidents (Sev0, P1, etc.).

Initial Premise

We first assumed that implementing chaos engineering would lead to a percentage reduction across all incident tiers. This made the calculation simple once a cost was assigned to each tier.

Problem:

• Not all incidents should be avoided; some low‑level alerts indicate improved visibility.
• Counting every incident could create incentives to under‑report lower‑severity events.

Refined Focus

We shifted to measuring savings from reducing critical incidents year‑over‑year. This metric is easier to track and aligns with business goals without encouraging under‑reporting.

Experiment Scaling & Diminishing Returns

• 0 → 100 runs: New performance gaps and reliability risks are uncovered.
• 100 → 200 runs: Additional runs reveal fewer new issues (diminishing returns).

We initially built a model that assumed a fixed percentage of experiments would reveal issues at different risk levels, with teams fixing a certain share based on capacity. However, this approach proved too one‑dimensional:

• Lack of documented detection‑rate references.
• Required new reporting mechanisms for risk mitigation tracking.

Nuance: Some teams automate experiments as regression tests in CI/CD pipelines, further complicating the model.

Real‑World Validation: Fidelity Investments Case Study

During our iteration, we saw a presentation by Keith Blizard and Joe Cho at AWS re:Invent 2024 that highlighted Fidelity Investments’ progress with chaos engineering:

• MTTR improvements as chaos testing coverage scaled across applications.
• Correlation between percentage of applications with chaos coverage and incremental MTTR reduction.

We used these metrics to:

1. Plot the relationship between coverage and MTTR impact.
2. Apply it against an industry‑wide average MTTR of 175 minutes (2024 PagerDuty report).

Downtime Cost Estimates

• $4,000 – $15,000 per minute (study estimate).
• $14,056 per minute for organizations with >1,000 employees (2024 BigPanda report).

Our calculator asks for Annual Company Revenue to select the most relevant downtime‑cost figure.

Our Conservative Assumption

Based on insights from Steadybit customers and industry studies:

• 30 % reduction in critical incidents per application per year when reliability tests are run regularly.

The calculator therefore:

1. Requests the total number of applications and the number with reliability‑testing coverage.
2. Multiplies the 30 % reduction by the coverage percentage to estimate the overall incident reduction for the organization.

How to Use the Calculator

1. Enter Annual Company Revenue – Determines downtime cost per minute.
2. Provide total application count and coverage count.
3. Review the ROI – See estimated savings from reduced MTTR and fewer critical incidents.

Scaling Chaos Experiments

If you want to run chaos experiments at scale, you will likely need to onboard a commercial reliability platform or chaos‑engineering tool. Open‑source solutions can be a viable alternative for smaller teams, but enterprise‑grade platforms provide:

• Centralized experiment management
• Automated scheduling & reporting
• Integration with CI/CD pipelines

Ready to Calculate Your ROI?

Try the interactive ROI calculator now and start building a data‑driven business case for chaos engineering in your organization.

Deploying Chaos Engineering at Scale

Deploying chaos testing across teams and technologies can quickly become time‑intensive. We used general license estimates based on market knowledge and projected experiment activity.

Implementation Effort

• Testing Rollout Managers – measured in FTEs (40 hr/week).
• Salary benchmark: average SRE salary of $160 k per year.

These assumptions help estimate the cost of the implementation effort.

ROI Calculator

1. Input – Project how you’ll roll out chaos engineering:

   • Unique test types
   • Number of experiments
   • Coverage across applications

2. Output – The calculator provides:

   • A summary and detailed view of projected savings
   • Implementation costs
   • Return on investment

When you model multi‑year adoption goals, you’ll build a solid business case that frames the value of this investment.

Reporting Progress

• Incident‑management platforms (e.g., Splunk, PagerDuty) often already expose MTTR metrics.
• Observability tools (e.g., Datadog, Dynatrace, Grafana Labs) can track the number of critical incidents.

These metrics should demonstrate clear improvements. Even if your systems become more complex—especially with the rise of AI agents—maintaining your current reliability posture can be considered a win.

Sharing Wins

Highly available applications don’t attract the same attention as outages, so you must intentionally share successes:

• Celebrate when a major reliability vulnerability is discovered and fixed before it impacts customers.
• Highlight any reliability improvements to keep momentum and nurture a culture of reliability.

Get Started with Steadybit

If you’d like help getting started with chaos testing and adopting a proactive reliability program, our team of experts at Steadybit is ready to assist.

• Explore our reliability platform with a 30‑day free trial.
• Book a quick call to discuss how you can implement chaos engineering and start saving money today.