Checkly Alternatives for Synthetic Monitoring in 2026

If you run synthetic monitoring today, you have probably written or maintained a Checkly check at some point, and you are now looking at Checkly alternatives because something about the fit, the pricing, or the workflow stopped working for you. Maybe the per-check billing grew faster than your coverage, maybe you wanted your monitored journeys to live in the same repo as your code, or maybe you just wanted to write "log in, add an item to the cart, and verify the order confirmation" in plain English instead of hand-maintaining Playwright scripts. This guide walks through the synthetic-monitoring landscape in 2026 — Checkly itself, Ghost Inspector, Datadog Synthetics, and a free CLI approach — and is honest about where each one wins.

Synthetic monitoring is a specific job: you script a critical user journey, run it on a schedule from outside your infrastructure, and page someone when it breaks. The tools below all do that, but they sit at very different points on the cost, control, and ownership spectrum. I have used scripted browser checks in anger for years, so this is not a feature-table-with-checkmarks piece. It is a comparison of what these tools actually feel like to operate, and which constraint each one solves best.

What synthetic monitoring actually needs to do

Before comparing Checkly alternatives, it helps to agree on what a synthetic monitor is supposed to deliver. A real synthetic check has five jobs, and a lot of tools nail three of them and quietly fall down on the other two.

First, it has to drive a real browser through a real flow — not just hit a URL and check for a 200, but actually log in, click through pages, and assert that the human-facing thing happened. Second, it has to run on a schedule without you babysitting it, which in practice means cron, a CI scheduler, or a hosted runner. Third, when it fails, it has to give you evidence — a screenshot, ideally a video, and a clear pass/fail signal — because "the checkout monitor is red" is useless without knowing whether it was a deploy, a third-party outage, or a flaky selector. Fourth, it should alert the right people through whatever channel they actually read. Fifth, and most underrated, the check definition itself should be cheap to write and cheap to change, because a monitor you are afraid to edit is a monitor that slowly drifts away from your real product.

Checkly is strong on jobs one through four and aims at job five with its monitoring-as-code story. The alternatives differ mostly on jobs one and five: how the browser flow is authored, and how much that authoring costs you in time and money over a year.

Where Checkly fits, honestly

Checkly is a monitoring-as-code platform built around Playwright. You write checks in JavaScript or TypeScript using the Playwright API, define them in code with the Checkly CLI or constructs, and Checkly runs them on a schedule from its global locations, then alerts you on failure. It also handles API checks, uptime, and has dashboards and alerting integrations. As of 2026 its pricing is usage-based around check runs and monitored resources; exact tiers change, so check their pricing page rather than trusting a number in any blog post, including this one.

Here is the honest read: if your team already lives in Playwright, wants checks reviewed in pull requests next to application code, and wants a managed global runner with mature alerting and a polished dashboard, Checkly is a genuinely good product and you may not need an alternative at all. Its monitoring-as-code model is the right idea, and the developer experience for a TypeScript-comfortable team is clean.

The reasons people go looking for Checkly alternatives tend to cluster:

• Cost at scale. Usage-based pricing on check runs means a fleet of high-frequency browser checks can get expensive, and the bill grows with exactly the thing you want more of — coverage and frequency.
• You have to write Playwright. That is a feature for engineers and a wall for QA folks, PMs, and support engineers who know the flow cold but do not write TypeScript selectors.
• Cloud-only execution. Checks run on Checkly's infrastructure. That is the point of a managed service, but some teams want monitors that can also run locally, inside a private network, or on their own runners with zero data leaving the building.

None of those are dealbreakers for everyone. They are the seams where a different tool fits better.

Ghost Inspector: codeless recording for non-developers

Ghost Inspector takes the opposite stance from Checkly on authoring. Instead of writing Playwright, you record a browser session — click through your flow once — and Ghost Inspector turns it into a repeatable test you can schedule and monitor. It supports assertions, scheduling, screenshots, and alerting, and there is a browser-extension recorder for building tests without code. Pricing is plan-based around test runs and frequency; as of 2026 you should confirm current tiers on their site.

Where Ghost Inspector wins is the audience. If the people who own a critical journey are not engineers — a growth team that needs the signup funnel watched, a support lead who wants the password-reset flow monitored — record-and-replay is dramatically more approachable than a Playwright file. You record once, schedule it, and get screenshot evidence on failure.

The trade-offs are the classic record-and-replay ones. Recorded steps can be brittle when the UI shifts, because the recording captured specific element paths at a moment in time, and a redesign or an A/B test can break a check that the product team considers "unchanged." You are also fully in a hosted SaaS model: the tests live in Ghost Inspector's app, not as plain files in your repo, so versioning and code review work differently than monitoring-as-code. For a non-developer team watching a handful of important flows, that is often a fine trade. For an engineering org that wants monitors diffed in pull requests, it can feel like a separate silo.

Datadog Synthetics: monitoring inside the observability platform

Datadog Synthetic Monitoring is the option you reach for when you are already a Datadog shop. It offers both API tests and browser tests, the browser tests can be built with a recorder or configured in the UI, and — this is the real pull — the results land in the same place as your metrics, traces, and logs. A synthetic failure can sit next to the APM trace and the infrastructure dashboard for the same time window, which makes root-cause work much faster than correlating across separate tools.

That correlation is the entire value proposition, and it is a strong one. If you already pay for Datadog and your on-call engineers live in Datadog dashboards, adding synthetics there means one fewer tool, one alerting pipeline, and instant context when something breaks. Pricing follows Datadog's usage model and is billed per test run by type; as with the others, confirm current rates rather than trusting a figure here.

The flip side is that Datadog Synthetics makes the most sense as part of the Datadog platform commitment. If you are not already on Datadog, adopting it just for synthetic monitoring brings the platform's pricing and operational model along for the ride, which is a lot of surface area for "watch my checkout flow." It is a heavyweight, enterprise-grade choice, and that is exactly right for some teams and overkill for others.

A different shape: a free CLI for plain-English monitored journeys

There is a fourth option that does not fit cleanly on the same axis as the three above, and it is worth understanding because it changes the cost and authoring math. BrowserBash is a free, open-source (Apache-2.0) command-line tool from The Testing Academy. You write a plain-English objective, an AI agent drives a real Chrome or Chromium browser step by step — no selectors, no page objects, no recorder — and you get back a clear verdict plus structured results, with screenshots and video on demand.

The relevant part for synthetic monitoring is the shape of the output. BrowserBash returns proper Unix exit codes — 0 for passed, 1 for failed, 2 for an error, 3 for a timeout — which is the contract cron and CI schedulers were built around. That means a monitored journey is just a command in a crontab or a scheduled CI job, and your existing alerting (a failing job notifies your on-call exactly like any other failed cron) does the paging. You are not buying a separate monitoring product; you are turning a browser journey into a script that exits non-zero when the user's path is broken.

Here is what a monitored checkout journey looks like as a single command:

browserbash run "Go to shop.example.com, log in as the test user, \
add the blue running shoes to the cart, complete checkout, and \
verify the page shows 'Thank you for your order!'" \
  --record --headless

The --record flag captures a screenshot and a full .webm session video via ffmpeg, so when the 3 a.m. run goes red you have the video of exactly where the journey died, not just a stack trace. Run it on a schedule with cron and the exit code does the rest:

# crontab: run the checkout monitor every 15 minutes, page on failure
*/15 * * * * /usr/local/bin/browserbash run "log in to shop.example.com, \
add an item to the cart, check out, and confirm the order succeeded" \
  --headless --record || /usr/local/bin/notify-oncall "checkout monitor failed"

For an automation-agent or CI pipeline that needs to parse results rather than read prose, --agent mode emits NDJSON — one JSON event per line on stdout — so a controller can consume each step without scraping human text. That is the difference between a tool built for dashboards and a tool built to be a Unix citizen.

The model story and the honest caveat

BrowserBash is Ollama-first. By default it uses free local models, needs no API keys, and nothing leaves your machine — which for a monitor running against an internal or pre-production environment is a real privacy and compliance win. It auto-resolves a local Ollama install first, then ANTHROPIC_API_KEY, then OPENROUTER_API_KEY, so you can stay fully local, bring your own Anthropic Claude key, or use OpenRouter (including genuinely free hosted models such as openai/gpt-oss-120b:free). On local models you can guarantee a $0 model bill, which is the entire point when the alternative is paying per check run.

The honest caveat, because credibility beats hype: very small local models — roughly 8B parameters and under — can be flaky on long, multi-step objectives. A six-step checkout with conditional branches is exactly the kind of flow where a tiny model loses the thread. The sweet spot for reliable monitored journeys is a mid-size local model in the Qwen3 or Llama 3.3 70B class, or a capable hosted model for the genuinely hard flows. If you try to monitor a complex journey on a 7B model and it flakes, that is a model-size problem, not a tool problem, and it is fixable by sizing up. For a managed-runner experience with zero model thinking, Checkly or Datadog will feel more turnkey out of the box — that is a fair point in their favor.

Evidence, replay, and committable monitors

When a synthetic check fails, the first question is always "show me." BrowserBash answers it three ways. Every --record run produces a screenshot and a .webm video; the builtin engine additionally captures a Playwright trace you can open in the trace viewer for step-by-step inspection. There is a free, fully local dashboard via browserbash dashboard for run history, and an optional free cloud dashboard — strictly opt-in via browserbash connect and the --upload flag — that stores run history, video recordings, and per-run replay; free uploaded runs are kept 15 days. No account is needed to run anything; the dashboard is a convenience, not a gate. You can read more about how runs and replay work on the learn pages.

For monitors you want under version control — the monitoring-as-code idea Checkly champions — BrowserBash has committable Markdown tests. A *_test.md file lists each step as a list item, supports @import composition to share a login flow across many checks, and {{variables}} templating; variables marked as secret are masked as ***** in every log line, so credentials never leak into your monitor logs. After each run it writes a human-readable Result.md.

browserbash testmd run ./checkout_monitor_test.md

A monitor file might template the environment and inject a masked password, so the same committed check runs against staging and production without forking it:

browserbash testmd run ./checkout_test.md \
  --var baseUrl=https://staging.example.com \
  --secret password=$STAGING_PASSWORD

That gives you the property Checkly's model is known for — monitors reviewed in pull requests, diffed like code — without the per-run bill, and with plain-English steps instead of Playwright selectors.

Side-by-side: Checkly alternatives compared

No single tool wins every row. Here is the honest layout, with "as of 2026" noted where details are not public or change often. Always confirm current pricing on each vendor's own page.

The pattern in that table: Checkly and Datadog are managed services that own the runner and the schedule; Ghost Inspector is a codeless hosted recorder; BrowserBash is a free CLI that turns a journey into a scriptable command you wire into infrastructure you already run. Different shapes for different constraints.

Where each tool runs the browser

One axis the table compresses deserves its own note, because it drives both cost and what you can monitor. Managed services run the browser on their cloud, which is exactly what you want for outside-in monitoring of public production, and exactly what you do not want when the thing you need to watch lives behind a VPN or on a developer's laptop.

BrowserBash switches where the browser runs with a single --provider flag. The default is local — your own Chrome, ideal for monitoring internal apps and pre-production with nothing leaving your network. There is cdp for any DevTools endpoint, and browserbase, lambdatest, and browserstack when you want the journey to execute on a real cloud-browser grid for geographic or device coverage.

browserbash run "log in and verify the dashboard loads for the EU region" \
  --provider lambdatest --record --upload

So a single tool covers "watch this internal staging app from inside our network on local Chrome" and "watch production checkout from a real cloud browser in another region" without adopting two products. The engines back this up too: stagehand (MIT, by Browserbase) is the default, and builtin is an in-repo Anthropic tool-use loop that adds the Playwright trace.

When to choose each one

This is the part that matters more than any feature list. Pick based on the constraint that is actually hurting you.

Choose Checkly if your team writes TypeScript comfortably, you want a polished managed runner with mature global locations and alerting, and you value monitoring-as-code reviewed in pull requests — and the usage-based bill is acceptable for your check volume. It is a strong product and the right default for a Playwright-fluent engineering org that wants a hosted experience.

Choose Ghost Inspector if the people who own your critical flows are not developers and record-and-replay gets you monitored coverage fastest. For a growth, support, or QA team watching a handful of important journeys without writing code, the approachability is worth the brittleness trade-off.

Choose Datadog Synthetics if you are already a Datadog shop and the killer feature is correlating synthetic failures with APM traces, logs, and metrics in one pane. The platform commitment pays off precisely because everything is already there; if you are not on Datadog, this is a heavy way to monitor a checkout.

Choose BrowserBash if you want a free, open-source tool, you would rather write monitored journeys in plain English than maintain Playwright selectors, you need monitors that run locally with no data leaving your machine (or on a cloud grid via one flag), and you are happy to wire scheduling through cron or CI using exit codes. It is the strongest fit when "$0 model bill on local models" and "monitors committed as Markdown next to my code" are the constraints that matter, and when you accept that you own the scheduling and alerting plumbing instead of buying a managed runner. Be realistic about model size — give long journeys a 70B-class local model or a capable hosted one. You can read a worked example of a full journey, and compare the pricing against per-check billing.

A practical hybrid

These are not mutually exclusive. A common 2026 setup: keep a managed service watching public production from the outside for the polished dashboard and global locations, and run BrowserBash in CI and cron for pre-production, internal apps, and the high-frequency journeys where per-run billing would otherwise sting. You get outside-in coverage where it counts and free, in-house, plain-English monitoring everywhere else, with screenshots and video on every run.

Getting started in five minutes

Trying the CLI option costs nothing and needs no signup. Install it, point it at a flow, and watch a real browser run it.

npm install -g browserbash-cli
browserbash run "open example.com, search for 'monitoring', \
and confirm at least one result appears" --record

Add --headless for a server, --agent for NDJSON in a pipeline, and --provider to move the browser to a cloud grid. When you are ready to schedule it, the exit code is all cron needs. Browse the full feature set and the GitHub repo if you want to read the source before trusting it with your monitors.

FAQ

What is the best free alternative to Checkly for synthetic monitoring?

BrowserBash is the strongest free, open-source option in 2026. It is Apache-2.0 licensed, runs on free local models with no API keys and a guaranteed $0 model bill, and turns a monitored journey into a single command with proper exit codes for cron and CI. You write the check in plain English instead of Playwright, and you get screenshots and video on every run. You own the scheduling and alerting, which is the trade for paying nothing.

Can I run synthetic monitoring without writing code or Playwright scripts?

Yes. Ghost Inspector lets non-developers record a browser flow and replay it on a schedule, and BrowserBash lets you describe the journey in plain English and have an AI agent drive a real browser through it with no selectors or page objects. Both avoid hand-written Playwright. The difference is that Ghost Inspector is a hosted recorder, while BrowserBash is a CLI whose checks can be committed as Markdown files next to your code.

How is Checkly different from Datadog Synthetics?

Checkly is a focused monitoring-as-code platform built around Playwright, where you write checks in TypeScript and run them on Checkly's global runners. Datadog Synthetics is part of the broader Datadog observability platform, so its main advantage is correlating synthetic failures with your existing traces, logs, and metrics in one place. Checkly fits Playwright-fluent teams that want monitoring as code; Datadog fits teams already committed to its platform.

Do I need an account or paid plan to monitor a user journey?

Not with BrowserBash. You can install it with npm and run a monitored journey immediately with no account, no key, and no paid plan, using free local models. An optional free cloud dashboard exists for run history and video replay, opt-in via the connect command and an upload flag, with free runs kept 15 days, but it is never required to run a check. Checkly, Ghost Inspector, and Datadog all require an account and a plan.

Synthetic monitoring should not force you to choose between writing Playwright forever and paying per check run. Install the free CLI with npm install -g browserbash-cli, describe your critical journey in plain English, and wire the exit code into the cron or CI you already run. When you want hosted run history and video replay, sign up for the free dashboard — but an account is entirely optional, and your monitors work the same without one.