Synthetic monitoring vs uptime monitoring: what's the difference?
If you only track uptime, you'll miss the bugs that customers feel most.
Last updated: February 2026Definitions (plain English)
Uptime monitoring pings a URL and checks that it returns a 200 status code. It tells you "the server responded."
Synthetic monitoring (also called workflow or transaction monitoring) navigates your product like a real user — filling in forms, clicking buttons, and verifying that the right things happen. It tells you "the customer journey works."
What uptime checks catch (and miss)
Uptime checks are great for basic availability: is the server reachable? Is the home page loading? They're fast, cheap, and simple.
But they miss everything that happens after the page loads. A login form that loops on submit, a signup flow that silently fails, a checkout page that can't process payments — all of these can happen while uptime reports "100% up."
What synthetic/workflow checks catch
Synthetic checks catch the bugs that exist between "the page loaded" and "the customer succeeded." They test real user journeys end-to-end and verify that each step produces the expected result.
When something breaks, you get the exact failing step, a screenshot, and context — not just "503 error."
When to use both together
Use uptime monitoring for basic infrastructure health: is the site reachable? Are API endpoints responding?
Use synthetic monitoring for customer-facing journeys: can someone actually sign up, log in, and pay?
Most small teams need both. Start with uptime monitoring (you probably already have it), then add synthetic checks for your 3 most critical user journeys.
A simple starter plan for small SaaS teams
Keep your existing uptime monitoring for basic availability.
Add synthetic checks for: (1) Login journey, (2) Signup/onboarding journey, (3) Checkout/upgrade journey.
Run synthetic checks every 10–15 minutes. Tighten the frequency for revenue-critical flows.
By the numbers
Organizations using multiple observability tools is common; many teams report tool sprawl and complexity as a key challenge in monitoring and incident response.
Gartner (Observability/Monitoring market research and practitioner surveys) (2024)A large share of outages are caused by changes (deployments, config updates, or third‑party dependencies), not complete infrastructure failure—making end‑to‑end journey checks valuable.
Google Cloud, DORA research (Accelerate / State of DevOps) (2023)Web performance directly impacts conversion: even small increases in page load time can reduce conversions, especially on mobile—meaning “up” doesn’t equal “usable.”
Google/SOASTA, "The Need for Mobile Speed" (2017)Mean time to detect (MTTD) and mean time to resolve (MTTR) improve when teams combine availability checks with user-journey validation and actionable alert context (screenshots/steps).
Datadog, State of Monitoring / Observability reports (2024)Real-world examples
Green uptime, broken login after cookie policy change
Scenario: A SaaS ships a security update changing cookie SameSite settings. Homepage and /status return 200 OK, so uptime checks stay green. Real users get stuck in a login redirect loop because the session cookie isn’t persisted in the browser.
Outcome: Synthetic login check fails within 2 minutes, capturing the redirect loop and screenshot. Team rolls back the cookie change and restores successful logins before support tickets spike.
Signup works, but email verification link is broken
Scenario: Marketing site and signup endpoint respond normally. However, the verification email template contains an outdated domain, so new users can’t verify and activate accounts. Uptime checks never hit the email flow.
Outcome: Synthetic signup journey detects failure at “Verify email” step by polling a test inbox and following the link. Activation failures are caught the same deploy, preventing a full day of lost trials.
Checkout fails due to third‑party payment misconfiguration
Scenario: Stripe keys are rotated and the frontend is deployed with the wrong publishable key for production. The pricing page loads fine (200 OK), but the payment modal errors when creating a PaymentIntent.
Outcome: Synthetic checkout flow fails on the payment step and logs the exact API error message. Fix is deployed in under 30 minutes; revenue-impacting outage is limited to a short window.
API is up, but the UI is unusably slow after a deploy
Scenario: Backend health checks pass and uptime is green. A new analytics script blocks rendering, pushing Time to Interactive beyond 15 seconds for many users. Users abandon onboarding.
Outcome: Synthetic browser check flags step timing regression (e.g., “Dashboard loads > 10s”) and alerts before conversion metrics drop significantly. Team disables the script and restores normal load times.
Key insights
1.
Uptime monitoring validates reachability (status codes/timeouts), but it does not validate business-critical outcomes like “user can log in” or “payment succeeds.”
2.
The highest-impact incidents for SaaS often come from changes: deploys, config flips, DNS/CDN behavior, and third-party dependencies—areas where synthetic journeys catch regressions that still return 200 OK.
3.
Synthetic monitoring is most valuable when you define 3–5 “golden journeys” (signup, login, checkout, password reset) and treat them like production tests running continuously.
4.
Browser-based synthetics detect real user blockers (JS errors, broken selectors, CORS, cookie issues, bot protection) that API-only checks frequently miss.
5.
Alert quality matters more than alert quantity for small teams: a failing synthetic check with a screenshot, step logs, and last-known-good deploy SHA reduces time-to-triage.
6.
A combined approach is best: frequent lightweight uptime checks for core endpoints + slightly less frequent multi-step synthetics for revenue/activation flows.
7.
Designing synthetics requires production-safe test data and cleanup/idempotency patterns; otherwise checks can create noisy artifacts (fake users, carts, emails).
Pro tips
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Start with 3 golden journeys: (1) login, (2) signup → onboarding first success action, (3) checkout/upgrade. If you can’t describe the steps in 1–2 sentences, it’s too broad—split it.
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Use a dedicated monitoring tenant and accounts (e.g., monitor-login@, monitor-checkout@) and make flows idempotent: reuse the same user, reset state via API, and clean up carts/orders where possible.
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Alert on outcomes + thresholds: not just “step failed,” but also “step slower than X seconds.” Many incidents show up as performance regressions before they become hard failures.
How CheckyWorky compares
vs Datadog Synthetics
Powerful enterprise platform with deep integration into Datadog APM/logs; can be heavier to configure and manage for small teams. CheckyWorky’s positioning is typically simpler “golden journey” setup and small-team friendly workflows for SaaS app flows.
vs Checkly
Developer-centric synthetic monitoring with strong Playwright support and CI/CD checks. CheckyWorky’s approach can emphasize ready-to-run “pretend customer” templates (signup/login/billing) and faster time-to-value for non-specialist teams.
vs UptimeRobot
Excellent low-cost uptime/heartbeat monitoring (is it up?) but limited for validating multi-step browser journeys (is it usable?). CheckyWorky focuses on transaction-style checks that confirm critical paths like login and checkout actually complete.