AVAILABLE FOR WEB, FLUTTER & QA PROJECTS

How to use this tool

1. Input Daily Active Users (DAU)

Enter your daily active user count or total target audience.

2. Select Release Risk Profile

Choose High Risk (billing/auth), Medium Risk (core UI flow), or Low Risk (styling/copy).

3. Define Error Tolerance

Set maximum acceptable delta in 5xx errors or client exception rates (e.g. 0.1%).

4. Review Phased Rollout Schedule

Export the phased timeline (Internal -> 1% -> 5% -> 25% -> 50% -> 100%) and automated rollback triggers.

Formula or logic used

Canary Cohort & Statistical Significance Model

Determines minimum user cohort sample sizes and error rate limits needed to detect regressions before full release.

  • Sample Size: N = (Z^2 * p * (1-p)) / E^2 where Z=1.96 (95% confidence) and E is acceptable error margin
  • Canary Exposure: Users in cohort = Total DAU * (Rollout Percentage / 100)
  • Standard Cadence: 1% Canary (4h soak) -> 5% (8h soak) -> 25% (24h soak) -> 50% (24h soak) -> 100% GA
  • Rollback Threshold: Immediate automated revert if canary error rate exceeds baseline by 50%

Examples

Example 1: Fintech Checkout Redesign (100,000 DAU)

Input: DAU: 100,000 | Risk: High (Core Payment Flow) | Error Budget: 0.05%
Calculated Result: Phase 1 (1%): 1,000 users (Soak 4h) | Phase 2 (5%): 5,000 users (Soak 12h) | Rollback trigger: > 2 payment errors/1k users

Limits financial blast radius while collecting statistical proof of payment gateway stability.

Example 2: B2B SaaS Navigation Refresh (10,000 DAU)

Input: DAU: 10,000 | Risk: Medium (Navigation & Search UX) | Error Budget: 0.2%
Calculated Result: Phase 1 (5%): 500 users (Soak 6h) | Phase 2 (25%): 2,500 users (Soak 24h) | Phase 3: 100% GA

Provides sufficient user feedback and telemetry without dragging release cycle past 48 hours.

Common use cases

High-Risk Database Schema Migrations

Deploy dual-write feature flags to 1% of write traffic to verify database latency before cutover.

Mobile App Store Phased Releases

Align backend feature flags with iOS phased releases (1% to 100% over 7 days) to catch crash spikes.

A/B Testing Power Analysis

Calculate whether a small feature flag test cohort will reach statistical significance within the planned sprint window.

Automated CI/CD Rollback Automation

Define quantitative metrics that trigger automated flag kill-switches via Datadog or Sentry webhooks.

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Frequently asked questions

What is the difference between a feature flag and a canary release?

A feature flag toggles code paths at runtime for specific users or percentages, often at the application layer. A canary release traditionally routes server-level network traffic to a new server or container version. Modern practices combine both for granular progressive delivery.

How long should a canary soak phase last?

For high-traffic consumer web applications, 2 to 4 hours is often sufficient to observe error rate anomalies. For lower-volume enterprise B2B apps, 24 to 48 hours is recommended to capture full business cycle interactions.

How do you avoid sample bias when assigning feature flag cohorts?

Use consistent deterministic hashing (such as MurmurHash3 or SHA256 of User ID + Feature Flag Key modulo 100). This guarantees that a user remains in the same cohort across sessions without database state storage.

What should trigger an immediate automated rollback?

Immediate rollback triggers typically include: a 50% spike over baseline in HTTP 5xx errors, unhandled client JavaScript exceptions in the canary cohort, or a measurable drop in checkout conversion.

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