Built for small teams who hate surprise breakages
Most monitoring tells you 'it's up' \u2014 but customers need 'it works.'
We made CheckyWorky for a simple reason: if you've ever shipped a release and then discovered signup was broken, you get it.
CheckyWorky runs the same journeys your customers run \u2014 and pings you with evidence when something goes sideways. No mystery alerts, no false alarms, just proof.
Our values
Simple over complicated
Small teams need tools that work in 10 minutes, not 10 sprints.
Proof over noise
Every alert includes what failed, where, and a screenshot. No ambiguity.
Small teams deserve great tooling
You shouldn't need an SRE team to know your signup is broken.
Want to chat?
We love hearing what you're monitoring (or struggling to monitor). Get in touch and we'll suggest a starter set of checks.
By the numbers
Organizations with higher observability maturity are more likely to resolve outages faster and reduce downtime impact, with top performers reporting materially improved MTTR outcomes compared to low-maturity teams.
New Relic, Observability Forecast (2023)The DevOps Research and Assessment (DORA) research consistently finds that elite software delivery performers recover from incidents significantly faster than low performers, highlighting the business value of fast detection and response.
Google Cloud, DORA / Accelerate research (2023)API and application outages frequently originate from changes (deploys/config), not hardware failure—making continuous post-deploy verification and synthetic checks a practical guardrail.
PagerDuty, State of Digital Operations (2024)Monitoring and incident response trends show teams increasingly rely on multiple signals (metrics, logs, traces, and synthetic/user journey checks) to detect customer-impacting failures earlier than support tickets.
Datadog, State of Monitoring (2024)Real-world examples
The “Login is up, but nobody can log in” redirect loop
Scenario: A small SaaS ships a minor auth change. The homepage and health endpoint are fine, but the OAuth callback URL is misconfigured in production. Real users hit a redirect loop after clicking “Sign in with Google.”
Outcome: A synthetic login journey fails within minutes of deployment and includes a screenshot + step log showing the redirect loop. The team rolls back the config quickly, avoiding hours of support tickets and preventing a spike in churn from blocked logins.
Stripe checkout succeeds—but the app never upgrades the plan
Scenario: Checkout completes successfully, but a webhook secret rotation breaks signature verification. Payments go through, yet customers remain on the free plan and can’t access paid features.
Outcome: A workflow check verifies “paid → upgraded → feature unlocked” end-to-end (including webhook receipt). The alert fires immediately after the first failed upgrade, reducing revenue leakage and eliminating a backlog of manual account fixes.
Password reset emails delivered… to spam (or not at all)
Scenario: An email provider changes sending reputation or a DNS record (SPF/DKIM) is modified. Password reset requests appear successful, but the email never arrives in time.
Outcome: A synthetic password reset flow checks for email arrival + link validity. The team catches the issue before users do, fixes DNS/auth, and avoids a wave of “I can’t log in” tickets.
The “it only broke for new accounts” onboarding failure
Scenario: A feature flag defaults incorrectly for new tenants, blocking the onboarding wizard at step 2. Existing users never see it, so internal testing misses the bug.
Outcome: A sign-up + onboarding workflow runs with a fresh synthetic tenant on a schedule. The failure is detected the same day, with clear reproduction steps, preventing lost trials and protecting activation rates.
Key insights
1.
Small teams don’t lose sleep over CPU graphs—they lose sleep over broken login, broken onboarding, and broken billing. Monitoring customer journeys maps directly to revenue and support load.
2.
“Green uptime” can coexist with a completely broken product experience (auth redirects, permissions, JS errors, feature flags, webhook failures). Synthetic workflows catch what pings and health checks miss.
3.
The fastest incident is the one you prevent: running a post-deploy workflow check is a lightweight guardrail that catches bad config and dependency issues before customers notice.
4.
Alert quality matters more than alert quantity for 2–15 person teams; requiring consecutive failures and attaching proof (screenshots/step logs) turns alerts into quick fixes instead of investigations.
5.
Third-party dependencies often fail in ways that look like your bug to customers (payment confirmation, email links, SSO callbacks). End-to-end checks measure the real impact.
6.
Production-only failures are common: secrets, DNS/TLS, caching, regional outages, and vendor incidents. Monitoring should run in the same environment your customers use.
7.
The most valuable first checks are usually 3–5 workflows: sign up/login, password reset, core object creation, invites/permissions, and upgrade—because they correlate strongly with churn and tickets.
Pro tips
💡
Pick 3 workflows and make them sacred: (1) sign up + first login, (2) password reset, (3) upgrade + entitlement. Run them every 5–10 minutes and after every deploy.
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Design “synthetic-safe” test data: create a dedicated workspace/tenant for checks and add cleanup steps (delete created records) to keep your database and analytics tidy.
💡
Make alerts self-debugging: include a screenshot, last successful run time, failing step, and a link to reproduction steps. For small teams, this is the difference between a 5-minute fix and a 2-hour investigation.
How CheckyWorky compares
vs Datadog Synthetics
Powerful enterprise-grade suite, but can feel heavyweight for tiny teams. CheckyWorky’s positioning emphasizes small-team workflows, friendly alerting, and “proof-first” incident context so you can fix issues fast without building a full monitoring program.
vs Checkly
Developer-first synthetic monitoring with strong CI/CD integration. CheckyWorky differentiates with an explicitly small-team tone and workflow-centric onboarding—aiming to make “monitor the customer journey” the default rather than a build-your-own approach.
vs UptimeRobot
Excellent for basic uptime/heartbeat checks, but it doesn’t validate multi-step user journeys like signup → login → action → billing. CheckyWorky focuses on catching surprise breakages inside the app even when the site responds.