Checkly vs Datadog Synthetics Testing (2026)

TL;DR

Checkly is the stronger default for code-first teams that want native Playwright and TypeScript monitoring as code. Datadog Synthetics is usually better for organizations already operating in Datadog that need broad protocol coverage and direct correlation with traces, logs, RUM, infrastructure, and incident workflows.

Key Takeaways

• Choose Checkly when Playwright and TypeScript are the source of truth for browser monitoring.
• Choose Datadog Synthetics when failures must correlate directly with Datadog logs, traces, RUM, infrastructure, and incidents.
• Compare run consumption, locations, retries, and suite duration instead of comparing headline prices alone.
• Prototype the same API check and critical browser flow in both products before committing.
• Keep scheduled production monitors separate from broad regression suites to control noise and spend.
• Use blocking CI gates only for stable, release-critical checks with controlled data.
• Treat ownership and incident workflow as primary selection criteria, not secondary implementation details.

Checkly vs Datadog synthetics testing is mainly a choice between a developer-native monitoring workflow and a synthetic testing capability embedded in a broad observability platform. Pick Checkly when QA and frontend engineers want standard Playwright tests, TypeScript constructs, local execution, and repository review. Pick Datadog Synthetics when SRE and operations teams need a failed journey connected to Datadog APM, RUM, logs, infrastructure metrics, dashboards, and on-call response.

Neither product wins every category. Checkly gives code-oriented teams a shorter path from a Playwright test to a scheduled monitor. Datadog covers more network protocols and keeps diagnosis inside the same telemetry system many enterprises already use. This guide compares the workflows, implements equivalent checks, and shows how to make the decision with evidence rather than a feature-counting exercise.

TL;DR

The practical verdict is simple. Start with Checkly if the people who own synthetic tests already write Playwright. Start with Datadog if the people who respond to failures already live in Datadog. Run the proof of concept below before signing a long contract.

What You Will Build

You will create a small, comparable evaluation rather than two unrelated demos:

• One HTTP check against the same public endpoint in both products
• One Playwright browser journey that verifies navigation and visible content
• A CI gate that can trigger each vendor's checks on demand
• A cost worksheet based on runs, locations, retries, and frequency
• A decision record tied to ownership, authoring, alerting, and diagnosis

The public endpoints keep the tutorial reproducible. Replace them with a non-destructive health endpoint and a dedicated production test account during a real pilot. If API coverage is new to your team, review the API testing roadmap before deciding which assertions belong in a monitor.

Prerequisites

Use Node.js 20 or newer, npm, Git, and Terraform 1.1.5 or newer. You also need Checkly and Datadog accounts. Checkly CLI automation uses CHECKLY_API_KEY and CHECKLY_ACCOUNT_ID. Datadog uses an API key plus an application key with permission to manage or execute Synthetic tests. Store all four values in your shell secret manager and CI secret store. Never commit them.

Create a disposable project and install the documented CLIs:

mkdir synthetics-comparison
cd synthetics-comparison
npm init -y
npm install --save-dev checkly @playwright/test jiti @datadog/datadog-ci
npx checkly --version
npx datadog-ci version
terraform version

Verify this prerequisite by confirming that all three version commands exit with status 0. Initialize Checkly with npx checkly init, then authenticate interactively with npx checkly login. For unattended runs, export the two Checkly secrets instead. Install the Datadog Synthetics CLI plugin with npx datadog-ci plugin install synthetics.

Checkly vs Datadog Synthetics Testing Comparison Matrix

Do not score every row equally. A team that spends most of its incident time joining browser failures to backend traces should weight correlation heavily. A team that changes critical journeys in every pull request should weight Playwright reuse and code review more heavily. The same distinction appears in GitHub Actions for Playwright: execution is only one part of a maintainable automation workflow.

Step 1: Define the Evaluation Before Configuring Either Product

Write a one-page scorecard with five weighted outcomes. A useful starting distribution is authoring and maintenance 25%, production diagnosis 25%, alert quality 20%, coverage 15%, and cost 15%. Change the weights before seeing results so product preference does not reshape the test.

Choose two production-safe scenarios. The first should be an idempotent API request with a status, body, and latency expectation. The second should be a critical browser path with three to eight user actions, such as sign in, view an account, and sign out. Avoid checkout completion, email delivery, and data deletion in the first pilot because external side effects obscure platform differences.

For each scenario, record the same success measures: time to author, local feedback time, pull request readability, false alert count, time to identify the failing layer, run consumption, and cleanup effort. Run the pilot for at least one normal release cycle. That exposes credential rotation, planned maintenance, location variance, and alert routing, which a ten-minute demo cannot reveal.

Verify the step by committing synthetics-evaluation.md with named owners, weights totaling 100%, the two scenarios, and a date for the decision review. This is the test oracle for the purchase decision. Without it, the tool with the most polished demo usually wins regardless of operational fit.

Step 2: Implement the Checkly API Check as Code

Create checkly.config.ts. The runtime identifier below is the current value shown in Checkly's configuration example at publication time. Check Checkly's runtime list before standardizing it across repositories.

import { defineConfig } from 'checkly'
import { Frequency } from 'checkly/constructs'

export default defineConfig({
  projectName: 'Synthetics Comparison',
  logicalId: 'synthetics-comparison',
  checks: {
    activated: true,
    muted: false,
    runtimeId: '2025.04',
    frequency: Frequency.EVERY_15M,
    locations: ['us-east-1'],
    tags: ['team:qa', 'env:evaluation'],
    checkMatch: 'src/__checks__/**/*.check.ts',
  },
  cli: {
    runLocation: 'us-east-1',
  },
})

Now create src/__checks__/github-zen.check.ts:

import { ApiCheck, AssertionBuilder, Frequency } from 'checkly/constructs'

new ApiCheck('github-zen-api', {
  name: 'GitHub Zen API',
  frequency: Frequency.EVERY_15M,
  locations: ['us-east-1'],
  maxResponseTime: 5000,
  request: {
    method: 'GET',
    url: 'https://api.github.com/zen',
    headers: [
      { key: 'Accept', value: 'application/vnd.github+json' },
      { key: 'User-Agent', value: 'synthetics-comparison' },
    ],
    assertions: [
      AssertionBuilder.statusCode().equals(200),
      AssertionBuilder.responseTime().lessThan(5000),
    ],
  },
})

ApiCheck and AssertionBuilder are public Checkly constructs. The logical ID is stable identity, so changing github-zen-api later can cause Checkly to treat the resource as a replacement. The explicit response threshold makes latency failure behavior reviewable. For richer contract assertions, adapt patterns from the contract testing guide, but keep volatile response fields out of an availability monitor.

Verify without deploying:

npx checkly test src/__checks__/github-zen.check.ts

Expect one discovered check and a passing status. Then run npx checkly deploy --preview to inspect the planned resource changes. Deploy only after the preview names the correct account and project.

Step 3: Implement the Comparable Datadog API Test

Datadog supports UI authoring and a public API, but Terraform makes this comparison reviewable. Save the following as datadog-synthetics.tf. The provider constraint allows compatible 4.x updates while keeping the major version stable.

terraform {
  required_version = ">= 1.1.5"

  required_providers {
    datadog = {
      source  = "DataDog/datadog"
      version = "~> 4.13"
    }
  }
}

variable "datadog_api_key" {
  type      = string
  sensitive = true
}

variable "datadog_app_key" {
  type      = string
  sensitive = true
}

provider "datadog" {
  api_key = var.datadog_api_key
  app_key = var.datadog_app_key
}

resource "datadog_synthetics_test" "github_zen" {
  name      = "GitHub Zen API"
  type      = "api"
  subtype   = "http"
  status    = "live"
  locations = ["aws:us-east-2"]
  message   = "Synthetic API failure for the QA evaluation"
  tags      = ["team:qa", "env:evaluation"]

  request_definition {
    method = "GET"
    url    = "https://api.github.com/zen"
  }

  request_headers = {
    Accept     = "application/vnd.github+json"
    User-Agent = "synthetics-comparison"
  }

  assertion {
    type     = "statusCode"
    operator = "is"
    target   = 200
  }

  assertion {
    type     = "responseTime"
    operator = "lessThan"
    target   = 5000
  }

  options_list {
    tick_every           = 900
    min_location_failed  = 1
    min_failure_duration = 0
  }
}

output "datadog_synthetic_public_id" {
  value = datadog_synthetics_test.github_zen.id
}

The location names are vendor-specific, so matching city labels is more meaningful than matching identifiers. tick_every = 900 gives the same 15-minute interval as the Checkly construct. In a serious rollout, add a second location only after deciding whether the alert should require one or several failed locations.

Verify syntax and inspect the remote change before creating a billable monitor:

terraform init
terraform fmt -check
terraform validate
TF_VAR_datadog_api_key="$DD_API_KEY" \
TF_VAR_datadog_app_key="$DD_APP_KEY" \
terraform plan

A successful validation proves the configuration matches the installed provider schema. The plan should show one datadog_synthetics_test resource. Apply it only in the intended Datadog organization, then capture the output with terraform output -raw datadog_synthetic_public_id.

Step 4: Compare Browser Authoring With a Real Journey

Checkly's differentiator becomes clearer in browser work. Create src/__checks__/example.spec.ts as a normal Playwright test:

import { expect, test } from '@playwright/test'

test('example domain navigation remains available', async ({ page }) => {
  await page.goto('https://example.com/')
  await expect(page).toHaveTitle(/Example Domain/)
  await expect(page.getByRole('heading', { name: 'Example Domain' })).toBeVisible()

  const moreInformation = page.getByRole('link', { name: 'More information...' })
  await expect(moreInformation).toHaveAttribute('href', 'https://iana.org/domains/example')
})

Reference it from src/__checks__/example-browser.check.ts:

import * as path from 'node:path'
import { BrowserCheck, Frequency } from 'checkly/constructs'

new BrowserCheck('example-domain-browser', {
  name: 'Example Domain navigation',
  frequency: Frequency.EVERY_15M,
  locations: ['us-east-1'],
  code: {
    entrypoint: path.join(__dirname, 'example.spec.ts'),
  },
})

Verify it with npx checkly test src/__checks__/example-browser.check.ts. You should see the browser check pass and the role-based assertions execute. The code can also be reviewed with your normal TypeScript changes. The Playwright tutorial for beginners explains the locator and assertion model if the syntax is unfamiliar.

In Datadog, record the equivalent steps in a Browser Test: open the URL, assert the page title or heading, select the link, and assert its target or resulting page. Replay the journey from the same geographic area and device profile. Datadog's recorder reduces the code requirement, while its managed steps produce screenshots and connect naturally to Synthetic result views. Checkly wins this step when engineers want fixtures, shared Playwright helpers, and pull request diffs. Datadog wins when service operators need to build and investigate journeys without owning a test repository.

Step 5: Add Both Synthetic Gates to CI

A scheduled monitor answers, "Is production healthy now?" A CI run answers, "Is this candidate safe to release?" Keep those purposes explicit. Add the four credentials to GitHub Actions secrets and store the Datadog public test ID as a repository variable. Then create .github/workflows/synthetics.yml:

name: Synthetic release gate

on:
  workflow_dispatch:
  pull_request:
    branches: [main]

jobs:
  synthetics:
    runs-on: ubuntu-latest
    timeout-minutes: 20
    steps:
      - uses: actions/checkout@v4
      - uses: actions/setup-node@v4
        with:
          node-version: 20
          cache: npm
      - run: npm ci
      - name: Run Checkly checks
        run: npx checkly test
        env:
          CHECKLY_API_KEY: ${{ secrets.CHECKLY_API_KEY }}
          CHECKLY_ACCOUNT_ID: ${{ secrets.CHECKLY_ACCOUNT_ID }}
      - name: Install Datadog Synthetics plugin
        run: npx datadog-ci plugin install synthetics
      - name: Run Datadog Synthetic test
        run: npx datadog-ci synthetics run-tests --public-id "${{ vars.DD_SYNTHETIC_PUBLIC_ID }}"
        env:
          DD_API_KEY: ${{ secrets.DD_API_KEY }}
          DD_APP_KEY: ${{ secrets.DD_APP_KEY }}

Dispatch the workflow and wait for its exit status with an authenticated GitHub CLI:

gh workflow run synthetics.yml
gh run watch --exit-status

Both vendor steps must exit 0, and each dashboard should show a CI-triggered execution. Verify this manually before making the workflow a required check. If a test is informational, configure the Datadog execution rule as non-blocking or separate it into a non-required job. Do not hide failures with shell constructs such as || true. The guide to adding CI to a test framework covers branch protection and failure ownership beyond this minimal gate.

Step 6: Test Failure Diagnosis and Alert Quality

Break the endpoint safely by overriding its URL in a temporary branch or cloned monitor. Use a controlled 404 path, then measure from alert receipt until the evaluator identifies whether the cause is DNS, TLS, network, HTTP, frontend behavior, or a backend dependency. Restore the URL immediately after collecting evidence.

Checkly presents a workflow centered on the check definition and its execution. For Browser Checks, Playwright knowledge helps engineers interpret locator failures, page state, traces, and artifacts. This is efficient when the same team owns the journey and application code. Alert channels, escalation policies, incidents, and status pages cover the path from detection to communication, but confirm plan entitlements during procurement.

Datadog's advantage is adjacent telemetry. If the application already emits Datadog traces, logs, RUM, and infrastructure metrics with consistent service, environment, and version tags, the responder can move from a failed synthetic test into backend evidence without changing platforms. That value disappears when tagging is inconsistent or APM is absent, so do not award points for integrations that your service does not actually use.

Verify this step with two timed drills, one API failure and one browser failure. Record the first useful clue, the number of screens or tools opened, alert delivery delay, and total diagnosis time. Compare p95 duration rather than one lucky run. The p95 and p99 latency guide helps teams avoid drawing conclusions from averages.

Checkly vs Datadog Synthetics Testing Pricing and Run Math

Pricing changes, so validate the vendor pages before purchase. As checked on August 8, 2026, Checkly pricing lists Hobby at $0, Starter at $24 per month billed annually, and Team at $64 per month billed annually. The displayed included pools are 1,000 browser and 10,000 API runs for Hobby, 3,000 browser and 25,000 API runs for Starter, and 12,000 browser and 100,000 API runs for Team. Eligible tiers can buy more runs, and overage rates differ from prepaid additions. Parallel locations and retries consume additional runs.

The Datadog pricing list shows annual rates of $5 per 10,000 Synthetic API runs and $12 per 1,000 Synthetic Browser runs. The same list shows higher month-to-month and on-demand rates. Datadog also counts each multistep API step as an API run. A browser transaction covers up to 25 steps per run, with longer tests consuming more units. Confirm commitments, retention, parallel testing, and other platform products in the quote.

Use this formula for either vendor:

monthly scheduled runs = checks x executions per day x days x parallel locations
expected billed runs = monthly scheduled runs + retries + CI-triggered runs

One check every 15 minutes runs 96 times per day. From two locations over 30 days, that is 5,760 scheduled runs before retries. For Checkly, compare that quantity with the included pool and relevant add-on or overage price. For Datadog, divide API runs by 10,000 or browser runs by 1,000 and apply the contracted unit rate. Do not claim Checkly is cheaper because its add-on rate is lower, or Datadog is cheaper because an isolated API unit looks inexpensive. Checkly's base plan bundles allowances and features; Datadog's broader value often depends on other purchased products.

Which Should You Choose

Choose Checkly when the synthetic estate will be owned by developers or SDETs who already work in TypeScript and Playwright. It is especially compelling when you want tests beside application code, reusable fixtures, local execution, construct-based configuration, and a clear test-to-monitor path. A small team can also evaluate meaningful usage on the Hobby plan before adopting a paid tier.

Choose Datadog Synthetics when your organization already standardizes operational telemetry and response in Datadog. Broad protocol checks, codeless browser creation, mobile tests, private execution options, and full-stack investigation can reduce handoffs between QA, SRE, and service teams. The case becomes stronger when service tags and APM instrumentation are mature enough to make correlation real.

Run both when responsibilities are genuinely split, but prevent duplicate paging. One defensible pattern is Checkly for code-owned Playwright release gates and Datadog for a small set of business-critical production journeys tied to service health. Document which platform is authoritative for each signal, route only one page for the same symptom, and review duplicate spend quarterly.

Avoid choosing solely from a feature spreadsheet. Assign the decision to the team that will maintain checks and respond at 3 a.m. Use the scorecard, code review, two failure drills, and run forecast as evidence. If the pilot relates to progressive delivery, connect synthetic outcomes to the safeguards in the canary testing guide rather than treating every failure as an automatic rollback.

Common Mistakes

• Migrating an entire regression suite into scheduled monitoring: Synthetic monitors should be small, stable, production-safe signals. Broad regression suites create noise, cost, test-data collisions, and unclear ownership.
• Comparing only the cost per thousand runs: Checkly plans include pools and capabilities, while Datadog exposes usage units inside a larger platform. Calculate the complete monthly workload and the operational products you need.
• Ignoring location multiplication: A five-minute check from three parallel locations consumes three executions per interval. Add retries and CI triggers before estimating the bill.
• Using personal accounts as synthetic actors: Create dedicated least-privilege users, exclude them from business analytics where appropriate, and rotate their credentials through secret management.
• Making flaky journeys blocking: Stabilize selectors, data, dependencies, and environmental assumptions before a synthetic result can stop deployment. A release gate without a fast owner becomes a bypass target.
• Duplicating pages across both tools: If two platforms alert on the same failure, responders may investigate twice or assume someone else owns it. Establish a single paging source and make secondary checks informational.
• Testing only happy-path HTTP status: A 200 response can contain an error page or invalid payload. Add a stable body assertion, a meaningful latency threshold, and a browser-level outcome where business risk justifies it.
• Assuming observability correlation is automatic: Datadog correlation depends on consistent tagging and instrumentation. Validate a real trace and related logs during the failure drill.
• Changing Checkly logical IDs casually: The logical ID represents resource identity. Renaming it can produce a replacement rather than an in-place update, so review previews carefully.
• Letting recorders dictate test design: A recorded Datadog browser flow still needs purposeful assertions, reusable subtests where suitable, controlled data, and a named maintainer. Recording clicks is not the same as designing a reliable monitor.

Troubleshooting

Checkly reports no checks found -> Match checkMatch to the actual .check.ts paths and confirm the browser construct points to the correct spec entrypoint. Run npx checkly test from the directory containing checkly.config.ts.

Checkly works locally but CI authentication fails -> Confirm CHECKLY_API_KEY and CHECKLY_ACCOUNT_ID are available to the job. Pull requests from forks usually cannot read repository secrets, so use a trusted manual workflow or another guarded design.

Terraform returns 403 from Datadog -> Check the Datadog site, application-key scopes, organization, and provider credentials. An API key identifies the organization, while the application key must authorize the requested Synthetic operation.

The Datadog CI command cannot find Synthetics support -> Run npx datadog-ci plugin install synthetics after installing @datadog/datadog-ci, then repeat the command. Cache npm dependencies, not an unverified plugin directory copied from another environment.

A browser monitor passes locally and fails remotely -> Compare DNS access, allowlists, geolocation, consent banners, device profile, time zone, and test-account state. Capture the remote screenshot and network evidence before increasing timeouts.

Alerts fire during deployments -> Use maintenance windows, failure-duration rules, or deployment-aware muting. Keep the underlying result visible so planned suppression does not erase diagnostic history.

Interview Questions and Answers

A strong interview discussion should connect tool mechanics to production ownership. Be ready to explain why code-first authoring changes maintenance, why observability context changes diagnosis, how location count affects alert confidence and billing, and when a CI result should block a deployment. The structured model answers below also cover fair pilot design, credential handling, and duplicate alert prevention.

Conclusion

The Checkly vs Datadog choice becomes straightforward once you identify the primary owner. Checkly is the better default for Playwright-centered engineering teams that want synthetic monitoring to behave like reviewed code. Datadog Synthetics is the better default for observability-centered organizations that need broad protocol coverage and fast movement from a failed journey to system telemetry.

Implement the same API check, reproduce one browser journey, run two controlled failure drills, and calculate a month of real executions. Then select the product that produces the clearest signal for the people who must maintain it and act on it. If you are building your skills while evaluating these tools, use the practice workspace to rehearse the architecture and incident-response questions before an interview.

Interview Questions and Answers

Frequently Asked Questions

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