About
Checkly is a developer-first application reliability platform that combines synthetic monitoring, uptime monitoring, and AI-powered observability into a single, unified workflow. Built around the concept of Monitoring as Code, Checkly allows engineering teams to write, version, and deploy monitors using JavaScript/TypeScript, Terraform, or Pulumi — treating infrastructure monitoring the same way they treat application code. At its core, Checkly offers powerful Playwright-based browser checks for monitoring complex user flows like logins, checkouts, and multi-step transactions. API checks provide fast, reliable uptime and performance monitoring for REST and GraphQL endpoints. Monitors can be deployed to 20+ global locations with a single command, ensuring worldwide coverage. Rocky AI, Checkly's AI agent, delivers automated root cause analysis and can be integrated directly into AI-driven engineering workflows via skills and prompts — enabling teams and agents to create monitors from natural language instructions. OTEL-based tracing accelerates incident resolution by linking monitors to distributed traces. Checkly also includes Status Pages for transparent customer communication, deep alerting via Slack, PagerDuty, and other channels, and real-time dashboards for visibility across environments. Whether used for test, staging, or production, Checkly helps modern engineering teams ship confidently and continuously.
Key Features
- Monitoring as Code: Write and version monitors as JavaScript/TypeScript, Terraform, or Pulumi — integrating monitoring directly into your existing CI/CD and IaC workflows.
- Playwright Browser Checks: Proactively detect issues in complex user flows like logins, checkouts, and multi-step transactions using real Playwright-powered browser automation.
- Rocky AI Root Cause Analysis: AI agents automatically analyze failures, identify root causes, and surface actionable insights — reducing mean time to resolution for production incidents.
- Global Synthetic Monitoring: Deploy uptime and API monitors to 20+ locations worldwide with a single command, ensuring performance and availability from every corner of the globe.
- Status Pages & Alerting: Communicate incidents to customers via hosted Status Pages and receive deep, reliable alerts via Slack, PagerDuty, email, and other integrations.
Use Cases
- Monitoring critical e-commerce user flows such as product search, cart management, and payment checkout using Playwright browser checks to catch regressions before customers do.
- Continuously verifying the uptime and response times of REST and GraphQL API endpoints across multiple global regions to meet SLA requirements.
- Integrating synthetic monitoring into CI/CD pipelines so that every deployment automatically runs checks against staging environments before promoting to production.
- Using Rocky AI to automatically diagnose production incidents by correlating synthetic check failures with distributed traces, reducing mean time to resolution.
- Publishing real-time status pages and configuring multi-channel alerting (Slack, PagerDuty, email) to keep customers and on-call teams informed during outages.
Pros
- Developer-native workflow: Code-first approach means monitors live in version control alongside application code, making collaboration, review, and rollback natural.
- AI-accelerated incident resolution: Rocky AI automates root cause analysis and integrates with AI agents via prompt-based skills, dramatically reducing time spent diagnosing failures.
- Broad ecosystem integration: Works seamlessly with Terraform, Pulumi, Playwright, OTEL, Slack, PagerDuty, and many more tools commonly used in modern engineering stacks.
- Global coverage out of the box: Monitors deploy to 20+ global regions instantly, giving immediate worldwide visibility without complex infrastructure setup.
Cons
- Developer-centric learning curve: The code-first, CLI-driven approach may be challenging for non-technical users or teams without JavaScript/TypeScript experience.
- Playwright dependency: Browser-level synthetic checks are tightly coupled to Playwright, which may not suit teams already invested in other automation frameworks.
- Cost at scale: Running checks at high frequency across many global locations and complex Playwright scripts can increase costs significantly at enterprise scale.
Frequently Asked Questions
What is synthetic monitoring in Checkly?
Synthetic monitoring in Checkly involves proactively simulating user interactions — such as logins, form submissions, and checkout flows — using Playwright-powered browser checks. This allows teams to detect issues before real users encounter them.
How does Rocky AI work?
Rocky AI is Checkly's AI-powered root cause analysis engine. When a monitor fails, Rocky AI automatically analyzes traces, logs, and check results to identify the likely cause of the issue and surface actionable insights to engineers — reducing manual investigation time.
What is Monitoring as Code?
Monitoring as Code is Checkly's approach to defining monitors in code (JavaScript/TypeScript, Terraform, or Pulumi) rather than through a UI. This allows monitors to be versioned, reviewed, and deployed alongside application code using standard engineering workflows.
Can I use Checkly with AI agents?
Yes. Checkly provides agent-ready skills that allow AI agents to create and manage monitors from natural language prompts. For example, an agent can be instructed to 'add monitors to all API routes' and Checkly will automatically generate and deploy the appropriate checks.
What platforms and integrations does Checkly support?
Checkly supports integrations with Terraform, Pulumi, Slack, PagerDuty, and many other tools. It exposes a flexible API for custom integrations, and its CLI enables seamless integration into any CI/CD pipeline. Monitors can be deployed to 20+ global locations.
