Email alerts when your critical flows break
Perfect for founders and small teams that don't live in Slack.
What's included in every email
What broke and where (workflow name + environment)
The exact step that failed
Screenshot of the page at failure time
Quick next steps: re-run the check or inspect details
Setup
Add recipients (team emails or individual)
Choose which checks trigger email alerts
Optional: quiet hours or digest mode (coming soon)
Best practices
Founders get billing and checkout alerts
Product team gets signup and onboarding alerts
Don't email everything — start with 2–3 critical checks
By the numbers
Organizations using SRE practices commonly target error budgets and treat user-journey availability as a first-class reliability metric, not just host uptime.
Google, Site Reliability Engineering (SRE) book (concepts widely adopted across industry) (2016)Mean Time to Detect (MTTD) is consistently cited as a key driver of incident cost; faster detection reduces customer impact windows and support load.
IBM Security, Cost of a Data Breach Report (MTTD/containment discussed as cost drivers) (2024)Alert fatigue is a common operational risk; teams report that excessive, low-quality alerts reduce response effectiveness and increase time-to-triage.
PagerDuty, State of Digital Operations (alert noise and operational effectiveness themes) (2024)Synthetic monitoring is frequently used alongside RUM/APM to catch broken critical paths (login/checkout) that infrastructure metrics can miss.
Datadog, State of Monitoring / Observability reporting (synthetics + RUM/APM adoption patterns) (2024)Real-world examples
Checkout button regression caught with screenshot proof
Scenario: A small SaaS ships a CSS/JS change that accidentally disables the “Pay” button on mobile Safari. Backend metrics look normal because no requests are made when users tap the button.
Outcome: Email alert fires after 2 consecutive failures in 2 regions with a screenshot of the disabled button and the failing step (“Step 5/7: Tap Pay”). Team rolls back within 12 minutes, preventing a multi-hour revenue-impacting outage.
Login redirect loop after IdP configuration change
Scenario: An Auth0/OIDC callback URL change introduces a redirect loop only in production. Users see repeated redirects and can’t reach the app.
Outcome: Email alert includes the last successful step (“Enter credentials”) and the failing step (“Callback redirect”), plus the final URL and a screenshot showing the loop. Fix is applied immediately (callback allowlist), reducing support tickets and avoiding a prolonged lockout.
Silent billing failure detected before customers complain
Scenario: Stripe webhook signature verification fails after a secret rotation, so invoice-paid events stop being processed. The UI still loads, but billing state doesn’t update.
Outcome: Workflow check that validates “invoice marked paid” fails and triggers an email with failing-step details and captured response codes. Team restores webhook secret within 30 minutes, preventing days of manual reconciliation.
Digest + quiet hours prevents overnight alert storms from flaky dependency
Scenario: A third-party email provider has intermittent latency spikes at night, causing sporadic timeouts in a non-critical “Invite teammate” flow.
Outcome: Instead of 40+ emails, the team receives a single hourly digest during quiet hours with grouped failures, timestamps, and evidence. Engineers investigate in the morning with full context, while P0 flows remain on immediate alerts.
Key insights
1.
Email alerts work best when they contain triage-ready evidence: failing step number/name, screenshot, error text/status code, and a direct link to rerun the check.
2.
Most “critical flow” incidents don’t show up as CPU/memory problems; they’re often UI regressions, auth redirects, expired secrets, misconfigured feature flags, or third-party timeouts—synthetic workflows catch these earlier.
3.
Quiet hours shouldn’t mean “no visibility”: pair quiet hours with digests and escalation rules for sustained or multi-region failures.
4.
Subject line consistency is an underrated reliability lever—include severity, environment, flow name, and failing step so teams can triage from the inbox.
5.
Alert fatigue is usually a configuration problem: add consecutive-failure thresholds, region quorum, and incident grouping to cut noise without losing coverage.
6.
Screenshots and step-level context reduce mean time to understand (MTTU): engineers can often identify the failure mode (selector change, modal overlay, 500 error page) without reproducing locally.
7.
Treat email alerts as part of an escalation ladder: email for P1/P2 workflow breaks, paging for P0 revenue/auth breaks, and digests for low-priority or flaky paths.
Pro tips
💡
Adopt a simple severity model for workflows: P0 = login/checkout/billing, P1 = core app actions, P2 = secondary flows. Route P0 to immediate alerts (and paging if you use it), P1 to email, P2 to digests.
💡
Tune for signal: require 2–3 consecutive failures and/or 2-region quorum for UI workflows to eliminate most flakes, then whitelist a few P0 checks to alert faster.
💡
Make every alert email contain a single “next click”: link to the failing run with screenshots, plus a “rerun now” button and a link to the runbook/owner (even if it’s just a Notion page).
How CheckyWorky compares
vs Datadog Synthetics
Strong enterprise observability suite, but teams can end up with noisy alerting unless they carefully tune monitors. CheckyWorky’s focus is on “pretend customer” workflows with email alerts that emphasize failing-step evidence (screenshots + step details) and small-team-friendly defaults.
vs Checkly
Developer-centric synthetic monitoring with Playwright and strong CI integration. CheckyWorky differentiates by prioritizing inbox-friendly alert payloads (what broke + proof) and pragmatic workflow monitoring patterns (quiet hours/digests/escalation) aimed at lean teams.
vs Uptime Robot
Great for simple uptime/HTTP checks, but less suited for multi-step user journeys like signup → verify email → checkout. CheckyWorky is built around end-to-end workflows and sends emails that pinpoint the exact failing step with visual proof.