From Datadog to Checkly in minutes

Looking to cut your Datadog bill and modernize your monitoring workflow?
In this session, Dan Giordano and Giovanni Rago show how to migrate your Datadog synthetic monitors to Checkly in minutes, unlocking Playwright, Monitoring-as-Code, and AI-powered automation.

Timestamps:
[00:00:00] Intro — Why Migrate from Datadog
Dan introduces the session, what will be covered, and who it’s for.

[00:00:45] Why Teams Move Off Datadog
Three main reasons: cost, automation, and developer experience.

[00:01:50] What Checkly Solves
Overview of Checkly’s advantages: Monitoring-as-Code, Playwright-native, AI-ready.

[00:03:10] Monitoring-as-Code Explained
How Checkly brings detection, alerts, and status pages into code.

[00:05:00] Why Monitoring-as-Code Matters
Codified setup, collaboration, version control, and AI automation benefits.

[00:07:30] AI-Ready Monitoring
How LLMs and IDEs like Cursor and Cloud Code automate monitoring creation.

[00:09:10] Limitations of Datadog’s Recorder
Why Datadog’s record/replay tools struggle with scalability and maintenance.

[00:10:40] Checkly + Playwright
How Playwright powers fast, reliable, code-based synthetic monitoring on Checkly.

[00:12:30] Debugging and Tracing with Checkly
Unified debugging with traces, videos, and OpenTelemetry integration.

[00:14:50] Pricing Breakdown
Comparing Datadog and Checkly pricing for API, browser, and uptime monitors.

[00:16:20] Cost Savings Example
Enterprise pricing example showing up to 61% savings with Checkly.

[00:18:00] Reliability in Depth
Building layered monitoring: uptime, API, and browser checks together.

[00:20:00] Live Demo: Creating Checks
Giovanni demonstrates Checkly’s interface and CLI for creating monitors.

[00:22:30] Detection, Communication, and Resolution
Explaining the monitoring triad: detection, alerting, and public status pages.

[00:25:00] Root Cause Analysis with AI
Using AI to identify and explain failures automatically.

[00:27:00] Tracing and Playwright Integration
Combining synthetic and real tracing data for faster issue resolution.

[00:29:00] Monitoring-as-Code Deep Dive
Using the Checkly CLI and TypeScript config to define checks in code.

[00:31:00] Deploying Checks from VS Code
Deploying monitoring suites directly from the developer workflow.

[00:33:00] CI/CD Integration
Running Checkly monitors within pipelines to prevent bad releases.

[00:35:00] Scalability and Collaboration
Managing thousands of checks with reusable code and shared ownership.

[00:37:00] Developer Workflow Transformation
Unifying testing and production monitoring through Playwright.

[00:39:00] Final Thoughts and Wrap-Up
Recap: migrate from Datadog in minutes — same coverage, lower cost.