Checkly

Checkly is a monitoring-as-code platform that brings synthetic monitoring into the developer workflow using Playwright-based browser checks and API monitoring. In this Checkly review, we break down its architecture, pricing tiers, ideal use cases, and how it stacks up against Datadog, Better Stack, and other observability tools. Checkly targets engineering teams that want programmable, version-controlled monitoring rather than point-and-click dashboards. Don't use this tool if you need full-stack APM with distributed tracing, log aggregation, or infrastructure metrics -- Checkly focuses exclusively on synthetic monitoring and does not cover backend observability.

Overview

Checkly operates as a synthetic monitoring platform built on Playwright and Node.js, letting teams define browser checks, API monitors, and alerting rules as JavaScript or TypeScript code. Unlike traditional uptime monitors that only ping endpoints, Checkly executes real browser sessions that interact with pages, fill forms, and validate multi-step user flows. The platform runs checks from 20+ global data center locations and reports latency, availability, and visual regression data.

The core differentiator is the monitoring-as-code approach via the Checkly CLI and SDK. Teams write checks as .check.ts files alongside application code, store them in version control, and deploy them through CI/CD pipelines. This eliminates the drift between what is tested in staging and what is monitored in production. The CLI integrates directly with GitHub Actions, GitLab CI, Vercel, and other deployment platforms, enabling pre-deploy verification where Checkly runs synthetic tests against preview deployments before promoting to production.

Key Features and Architecture

Playwright-Based Browser Checks

Checkly runs full Playwright browser sessions in isolated Chromium instances across its global infrastructure. Each check can navigate pages, click elements, fill forms, extract text, and assert on page content using the standard Playwright API. This means any Playwright script that works locally can run as a Checkly monitor with zero modification. Checks execute in parallel across selected regions, and failures trigger alerts with screenshots, console logs, and trace files for debugging.

API Monitoring with Multi-Step Checks

API checks go beyond simple HTTP pings. Checkly supports multi-step API workflows where each request can extract values from the response (headers, JSON body, status codes) and pass them to subsequent requests. This handles real-world scenarios like authenticating via OAuth, fetching a resource ID, then verifying downstream state. Assertions support JSON path matching, response time thresholds, and status code validation.

Monitoring as Code with the CLI and SDK

The Checkly CLI (npx checkly) lets teams define checks, alert channels, and dashboards as TypeScript or JavaScript configuration files. The SDK provides constructs like BrowserCheck, ApiCheck, and AlertChannel that compile into Checkly resources. Running checkly deploy pushes the configuration to the Checkly cloud, and checkly test executes checks locally or in CI before deployment. This workflow integrates monitoring into the standard software development lifecycle -- checks are code-reviewed, tested, and deployed alongside application changes.

CI/CD Integration and Pre-Deploy Testing

Checkly integrates with CI/CD pipelines to run synthetic tests against preview or staging environments before production deployment. The checkly test command runs all checks against a specified base URL and fails the pipeline if any check breaks. This catches regressions in user flows, API contracts, and page performance before they reach production. Native integrations exist for GitHub Actions, GitLab CI, Vercel, and generic webhook-based triggers.

Alerting and Incident Management

Checkly sends alerts through Slack, PagerDuty, OpsGenie, email, SMS, and custom webhooks. Alert escalation policies allow teams to configure retry thresholds, degraded-state notifications, and recovery alerts. The platform also features AI-powered root cause analysis that examines check failures, network traces, and screenshot diffs to suggest probable causes.

Visual Regression Testing

Available on the Team plan ($64/month) and above, visual regression testing captures screenshots during browser checks and compares them against baseline images. Pixel-level diffs highlight layout shifts, missing elements, and CSS regressions. This catches visual bugs that functional assertions miss -- a button might still be clickable but shifted 200 pixels off-screen.

Ideal Use Cases

DevOps teams managing user-facing web applications benefit most from Checkly. If your team already uses Playwright for end-to-end testing, reusing those scripts as production monitors eliminates duplicate effort and keeps test coverage aligned with monitoring coverage.

API-first companies that expose public APIs or depend on third-party API integrations should use Checkly's multi-step API checks to monitor authentication flows, rate limits, and data consistency across endpoints. The ability to chain requests and assert on intermediate responses catches issues that simple uptime pings miss entirely.

CI/CD-heavy engineering teams that deploy multiple times per day gain the most from pre-deploy synthetic testing. Running Checkly checks against preview deployments catches broken user flows before production, reducing rollback frequency and incident response overhead.

Platform engineering teams building internal developer portals or multi-tenant SaaS applications use Checkly's private locations to monitor internal services behind firewalls that external monitoring tools cannot reach.

Pricing and Licensing

Checkly uses a tiered pricing model with a free Hobby tier and three paid plans. All paid plans include email and community support; the Enterprise tier adds dedicated support and SAML/SSO.

Add-on pricing scales linearly: an additional 25 monitors costs $8, and 1,000 extra browser check runs cost $4. Checkly offers a 30% startup discount and a 30% nonprofit discount, both applied to the base plan price.

The free Hobby tier is genuinely usable for personal projects or proof-of-concept evaluations -- 10 monitors with 1,000 browser checks per month covers basic smoke testing. The jump from Starter ($24/month) to Team ($64/month) is justified by the 4x increase in browser check volume, 30-second check frequency, visual regression testing, and private locations. Enterprise pricing requires contacting sales and includes 180-day raw data retention, custom check limits, and 1-second check frequency for latency-sensitive SLAs.

Pros and Cons

Pros:

• Native Playwright support means existing E2E test scripts work as monitors without rewriting
• Monitoring-as-code workflow integrates checks into version control and CI/CD pipelines
• Generous free tier with 10 monitors and 1,000 browser checks per month
• Pre-deploy testing catches regressions before production deployment
• Multi-step API checks support real-world authentication and data flow validation
• 20+ global checkpoint locations with sub-minute check frequencies on paid plans

Cons:

• No APM, log management, or infrastructure monitoring -- Checkly covers synthetic monitoring only
• Visual regression testing requires the $64/month Team plan, not available on Starter
• Playwright-only for browser checks; teams using Cypress or Selenium must port their scripts
• Check authoring requires JavaScript or TypeScript knowledge, which limits adoption for non-developer teams

Alternatives and How It Compares

Datadog Synthetic Monitoring is the better choice if you already run Datadog for APM, logs, and infrastructure metrics. Datadog offers synthetic monitoring as part of its broader observability platform, so you get correlated traces and metrics in one dashboard. The trade-off is usage-based pricing that scales unpredictably -- Checkly's flat monthly tiers are more budget-friendly for teams that only need synthetic monitoring.

Better Stack (formerly Uptime.com) provides uptime monitoring, status pages, and incident management in a single product. Choose Better Stack if you need a public status page alongside your monitors. Checkly lacks built-in status pages and focuses purely on programmable checks.

Grafana Cloud Synthetic Monitoring integrates with Grafana's dashboarding and alerting ecosystem. If your team already runs Grafana for metrics visualization, adding synthetic checks inside the same platform reduces tool sprawl. Grafana's synthetic monitoring is less developer-centric than Checkly's CLI-first approach, though.

Sentry excels at error tracking and performance monitoring from inside the application (real user monitoring). Use Sentry when you need to catch runtime exceptions and trace slow transactions from actual user sessions. Checkly complements Sentry by testing from the outside -- synthetic checks verify that endpoints respond correctly, while Sentry captures what happens when they do not.