Checkly vs Datadog Synthetic Monitoring vs Sentry

From $24 per month (Starter; Team is $64)

From $5 per 10k API test runs (browser tests are $12 per 1k)

From $26 per month (Team plan, indicative)

Priced by check runs; browser checks cost more than API checks, and frequency multiplies the bill. Team at $64 adds 75 monitors, 30-second frequency, all 22 locations and private locations.

Note the different units: Datadog bills API tests per ten thousand runs and browser tests per thousand, so browser tests are roughly 24x the unit cost. A test run is one execution from one location, so multi-location checks multiply. Costs land inside a wider Datadog contract that is rarely small.

Event-volume pricing. A noisy application can consume the quota quickly, so filtering matters.

Hobby: 10 uptime monitors, 10k API and 1k browser check runs a month across 6 locations.

14-day trial.

Free developer plan with a monthly error and replay allowance.

Widely used

Checkly · since 2018

Category standard

Datadog · since 2019

Category standard

Functional Software (Sentry) · since 2012

• One Playwright script serves as both an E2E test and a production monitor.
• Monitoring-as-code fits GitOps workflows and code review.
• Excellent developer experience and clear, published pricing.

• Correlation with APM traces and logs makes root-cause analysis dramatically faster.
• Private locations let you monitor internal services behind the firewall.
• Recorded browser tests are authorable by people who do not write code.

• Release and commit attribution tells you which deploy introduced the error, and often who to ask.
• Excellent SDK coverage across essentially every language and framework.
• Self-hosting is genuinely supported for teams that need it.

• Playwright-centric; teams on other frameworks get less benefit.
• Browser checks at high frequency across many locations get expensive.
• Not a full observability platform — no APM or log management.

• Only worth it if you are already a Datadog customer; standalone it is expensive.
• Datadog billing complexity is a well-known source of budget surprises.
• The recorder produces tests that are harder to version and review than code.

• Event-based pricing punishes noisy applications until you invest in filtering.
• The product has broadened a lot; some newer modules are shallower than the error core.
• Self-hosting is resource-hungry.

Engineering teams already using Playwright who want production journeys monitored.

Existing Datadog customers who want monitoring in the same pane as their observability data.

Essentially every engineering team — error tracking is the highest-value monitoring you can add first.