Checkly built its reputation on a single conviction: synthetic monitoring should be code, not click-through wizards. You write Playwright tests, Checkly runs them on a schedule from global locations, and you get alerts when a user journey breaks. For teams already fluent in TypeScript and Playwright, it fits like a natural extension of CI.
But Checkly is not the only option, and its trade-offs become clear at scale. Per-run billing means a busy monitoring setup can produce surprise invoices — a single browser check running every 30 seconds from three regions generates 259,200 billable runs per month. There is no recorder for non-developers, no native on-call, and SSO requires the Enterprise plan. If any of those gaps matter to your team, an alternative deserves evaluation.
We compared five alternatives across the dimensions where teams actually switch: pricing predictability, authoring mode, on-call integration, browser engine fidelity, and developer surface (CLI, Terraform, MCP).
What makes Checkly good — and where teams hit limits
Checkly's strengths are real. It runs your actual Playwright suites (multi-file, fixtures, stored state) with the highest fidelity in the category. The developer surface is deep: a CLI (checkly test and checkly deploy), a Terraform provider, Pulumi support, and Prometheus export. If your definition of monitoring as code is "my monitors live in the same repo as my app and deploy in the same pipeline," Checkly delivers that better than anyone else.
The limits show up in three places:
Pricing at scale. Browser checks cost ~$4–6.50 per 1,000 runs, and the platform splits into three separately metered products (Synthetics, Alerting, Private Locations). A team with 20 browser checks at 1-minute intervals from 3 regions pays for ~2.6 million runs/month. That's $10,000–17,000/year just for the browser runs.
Non-developer authoring. There is no recorder. Every check is TypeScript. If your QA team or product managers need to create checks, they cannot — they need a developer to write the code. This limits adoption to engineering teams only.
On-call and incident management. Checkly alerts via webhook, Slack, PagerDuty, or OpsGenie — but has no native escalation, rotation, or incident timeline. You need a separate on-call tool.
Datadog Synthetic Monitoring
Datadog is the enterprise pick, and its differentiator is correlation. A failed browser check links directly to the APM trace, the infrastructure metrics, and the RUM session that explains it. No other tool in this list can show you "the checkout button failed because the payment-intent endpoint spiked to 4s latency because the Postgres replica lagged 12s behind primary" in a single pane.
Where it wins over Checkly: nine test types (including mobile device), self-healing locators in the recorder, SOC 2 + ISO 27001 + HIPAA + FedRAMP compliance, SAML/SCIM with custom RBAC, and native integration with 700+ other Datadog products.
Where it loses: Browser checks cost ~$12–18 per 1,000 runs — roughly 3x Checkly's rate. CI test runs draw from the same quota. The frequency floor in the UI is 5 minutes (1-minute requires a support ticket). The code-first story is weaker — you can write tests in JavaScript, but the workflow assumes the recorder as the starting point. Session replay sits behind separately-billed RUM.
Best for: enterprises already on Datadog that want synthetic checks correlated with full-stack observability and can absorb metered browser pricing. For the broader platform comparison, see Datadog vs Dynatrace.
Grafana Cloud Synthetic Monitoring (k6)
Grafana Cloud has the most generous free tier in synthetic monitoring — 100,000 API plus 10,000 browser executions per month, no credit card — backed by the credibility of open-source k6. If your team values owning the stack and paying nothing to start, Grafana is the obvious candidate.
Where it wins over Checkly: free tier that actually covers meaningful usage, OSS foundation (k6 is MIT-licensed), Playwright-to-k6 script conversion, a first-party authoring MCP, and Terraform support. If you already run Grafana for dashboards, adding synthetic monitoring is a config change, not a vendor decision.
Where it loses: past the free tier, browser pricing gets steep and confusing (~$50 per 10,000 executions, billed per-probe-per-minute). There is no in-product recorder — k6 Studio is a separate desktop app. The browser interval floor is 60 seconds. And the whole-stack complexity is real: you are adopting Grafana's ecosystem, not a focused monitoring tool.
Best for: engineering teams that value OSS, need a strong free tier, and author everything in code. Not for teams that need a recorder or sub-minute browser intervals.
Better Stack
Better Stack bundles uptime monitoring, Playwright browser checks, incident management, on-call rotations, logs, and status pages in one product. Its native on-call and escalation are the best in this list — the one thing Checkly fundamentally lacks.
Where it wins over Checkly: native on-call with escalation policies and rotations, a bundled status page, Playwright/Chromium engine (not Selenium), trace-viewer artifacts on failure, and a Terraform provider. For small teams that want monitoring + on-call + status pages in one bill, Better Stack eliminates three separate vendor relationships.
Where it loses: locations are coarse (four regional groups — US, EU, Asia, Australia). Private synthetic locations are weak and lightly documented. There is no AI authoring and no visual regression. Pricing is per-minute (~$1 per 100 Playwright-minutes) on top of a required $29/responder seat, which gets unpredictable at scale.
Best for: small-to-mid teams that want monitoring, on-call, and status pages bundled. Teams that need deep location control or high-volume browser checks at predictable pricing will hit limits.
Sematext
Sematext is the one predictable pricing model in synthetic monitoring: a flat per-monitor fee (~$2 for HTTP, ~$7 for a browser monitor per month) with no per-run meter. The engine runs Playwright on Chromium, and private locations deploy as Docker containers.
Where it wins over Checkly: completely predictable billing. A team with 20 browser monitors pays $140/month regardless of check frequency or location count. No surprise invoices. No metering math. Docker-based private locations that work without an enterprise contract.
Where it loses: the developer surface is minimal — no Terraform provider, no CLI for synthetics, no MCP. There is no recorder, no video capture, no HAR archive. Multi-step journeys only report the last page's metrics. The browser interval floor is 5 minutes. It is a thin feature set with one clear advantage: predictability.
Best for: teams that want predictable per-monitor pricing on a handful of browser checks and do not need a developer surface or deep forensics.
When you need the layer underneath
All synthetic monitoring tools — Checkly included — run checks from the outside. They tell you that a journey failed. They do not tell you which API endpoint caused the failure, or whether the root cause is your infrastructure or a degraded third-party dependency.
Layering API monitoring underneath your browser checks turns "the checkout flow is red" into "the /payment-intent endpoint is returning 500 because Stripe's API is degraded." That correlation is the difference between a 5-minute diagnosis and a 45-minute scramble.
How to choose
The decision tree is shorter than the feature matrix suggests:
- You write Playwright and want maximum fidelity: Checkly remains the best if you can manage per-run billing.
- You need APM correlation: Datadog, if you can absorb the price.
- You want free and open source: Grafana Cloud / k6.
- You need on-call bundled: Better Stack.
- You need predictable billing above all: Sematext.
- You need API and uptime monitoring with config-as-code underneath your synthetic layer: DevHelm — 50 monitors on the free tier, flat pricing, multi-region, with a status page that updates from the same check data. Your first monitor takes 60 seconds, no credit card.
Whatever you choose, read the synthetic monitoring best practices guide before you configure your first check — the difference between a useful setup and a flaky one that trains your team to ignore alerts is in the details, not the vendor.
Originally published on DevHelm.
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