GoQA is an AI-powered quality assurance platform that generates test cases from acceptance criteria, URLs, or screenshots and audits websites for accessibility, security, and performance issues.

What test frameworks does GoQA support?

GoQA generates tests in Playwright, Cypress, Selenium, and Gherkin BDD format — all open standards with no vendor lock-in.

Is there a free plan?

Yes, the free Starter plan includes 5 AI generations per month, 50 test cases per request, and CSV/Markdown export with no credit card required.

Is my data used for training AI models?

No. By contract with our AI providers, your inputs are never used to train or fine-tune AI models. All data is encrypted at rest with AES-256 and in transit with TLS.

QA Lab AI vs Mabl

Teams evaluating a Mabl alternative are usually weighing a low-code cloud test runner against an option that gives engineers more control over the runner, the test format, and what gets checked beyond functional flows. This page compares QA Lab AI and Mabl on those points, based on publicly documented capabilities at time of writing.

At a glance

Feature	QA Lab AI	Mabl
Authoring model	AI generation from text, URL, screenshot, OpenAPI	Low-code journey builder, AI assists
Output format	Gherkin .feature, JSON, Excel	Mabl journeys, native cloud runner
Runners	Playwright, Cypress, Selenium, WebdriverIO	Mabl cloud runner
Live-site audits	WCAG (axe-core), Lighthouse, OWASP, SEO, broken links, mobile	Functional and basic accessibility checks
Free tier	$0 forever, 200 cases/run, 5 AI gens/mo	See vendor site
Enterprise	SAML SSO, SCIM, 99.95% SLA, Jira/Zephyr/ADO sync	See vendor site

Where they overlap

Both QA Lab AI and Mabl use AI to lower the cost of building and maintaining web tests. Both can run against staging or production URLs, both flag visual and functional regressions, and both integrate into CI. Each is built around the idea that QA teams should not be writing every assertion by hand.

Both also let you organize tests centrally and surface results to non-engineers — Mabl through its journey UI, QA Lab AI through the Test Repository.

Where QA Lab AI is different

Two differences matter most.

It pairs case generation with live-site audits. A single QA Lab AI run can output BDD test cases and an audit covering WCAG via axe-core, Lighthouse performance, OWASP checks, SEO issues, broken links, cross-browser checks, and mobile rendering. Mabl focuses on functional journeys; broader site-quality coverage is less central. If you need accessibility and performance evidence on the same release as functional regression, QA Lab AI consolidates that.

It is engineer-led on the runner side. QA Lab AI exports Gherkin (.feature), JSON, or Excel, and the Gherkin runs against Playwright, Cypress, Selenium, or WebdriverIO — whichever your team already uses. Mabl runs tests on its own cloud, which is convenient if you want a managed runner but constraining if you have an existing Playwright suite you do not want to rewrite.

QA Lab AI also accepts inputs Mabl typically does not: an OpenAPI spec for backend contract cases, a screenshot run through OCR, or a raw URL scraped for elements. See /test-cases for input options.

Where Mabl may fit better

Mabl is the better tool in two specific scenarios:

Low-code-only QA teams. If your testers do not write code and you do not want them to, Mabl's managed cloud runner removes the runner-selection question entirely. QA Lab AI assumes someone on the team will pick a runner and wire it into CI.
Single managed platform. If you want one vendor handling authoring, execution, and reporting, Mabl is closer to that. QA Lab AI authors and audits; you bring your own execution.

If either applies, Mabl is likely the right call.

Pricing comparison

QA Lab AI's pricing is public:

Starter: $0 forever, 200 cases per run, 5 AI generations per month
Pro: $39/month, or $31/month billed annually
Enterprise: custom, with SAML SSO, SCIM, 99.95% SLA, and Test Repository sync with Jira, Zephyr, and Azure DevOps

Mabl does not publish a flat monthly price comparable to Pro; see vendor site. Full tier details are at /pricing.

Migration notes

Moving from Mabl to QA Lab AI:

Export your Mabl journey definitions or step descriptions as plain-language acceptance criteria. These become the input for QA Lab AI generation.
Generate Gherkin and review it. The eighteen-plus case types include negative, boundary, accessibility, security, and performance scenarios that may not have existed in your Mabl journeys.
Choose a runner. Playwright is the common pick for new estates; Cypress, Selenium, and WebdriverIO are supported.
On Enterprise, connect Jira, Zephyr, or Azure DevOps so cases live where your team already triages.
Use the audits — WCAG, Lighthouse, OWASP, SEO — as a separate gate, not just at the end of regression.

/services and /contact cover assisted migration.

FAQ

Can QA Lab AI run tests on its own cloud like Mabl does? No. QA Lab AI authors and exports tests; execution happens on the runner you choose, on your infrastructure or any cloud grid you use.

Does the WCAG audit replace a manual accessibility review? It covers what axe-core can detect automatically, which is a large but partial portion of WCAG. Manual review is still needed for full conformance.

How does QA Lab AI handle API tests if I upload an OpenAPI spec? It generates BDD scenarios for documented endpoints, including happy-path, negative, and boundary cases, exportable as Gherkin you can run with the same runner as your UI tests.

Try it

Run a free site audit at /free-audit or generate your first BDD cases at /test-cases.