Google Optimize shut down in 2023, leaving teams to choose between dedicated experimentation suites, product analytics platforms, feature-flag systems, and lighter website testing tools. The correct replacement depends on where the experience lives and who deploys it. A marketing team changing a landing-page headline needs a visual editor and simple targeting; a product team testing onboarding logic needs server-side assignment, feature flags, event data, and engineering controls.
Shortlist by use case
| Platform | Best fit | Testing approach | Main drawback |
|---|---|---|---|
| VWO | Mid-market website optimization teams | Visual, full-stack, personalization, and related CRO tools | Product packaging and traffic-based pricing require a quote or careful review |
| Optimizely | Large organizations with mature experimentation programs | Web and feature experimentation, personalization, enterprise governance | Enterprise cost and implementation overhead |
| AB Tasty | Enterprise marketing and product teams | Client-side tests, personalization, feature experimentation | Quote-based pricing and broad suite complexity |
| Convert Experiences | Privacy-conscious agencies and web teams | Visual editor, audience targeting, multipage tests | Smaller ecosystem than the largest enterprise vendors |
| Statsig | Engineering-led product experimentation | Feature flags, experiments, product analytics, warehouse integrations | Not designed primarily as a marketer’s visual-page editor |
| PostHog | Product teams wanting analytics and experiments together | Feature flags, experiments, event analytics, session replay | Requires sound event implementation and technical ownership |
| GrowthBook | Teams wanting open-source or warehouse-aware control | Feature flags, SDKs, experimentation, self-hosting options | More setup and analysis responsibility |
Check current price and limits for monthly tested users, events, domains, workspaces, seats, server-side SDKs, personalization, warehouse queries, and support. “Free†can be excellent for a small product test but insufficient for a high-traffic site or a company needing single sign-on and audit controls.
VWO: the practical dedicated CRO suite
VWO combines website A/B testing with a visual editor, targeting, goals, reporting, and optional products for insights, personalization, and full-stack experimentation. It is approachable for marketers who need to change copy, layouts, forms, or calls to action without waiting for a full release cycle. Heatmaps, recordings, surveys, and funnel-related capabilities can help generate hypotheses, depending on the package.
The platform is broad enough to support a growing optimization program. Teams can use URL, device, behavior, and audience conditions; test several variations; and connect outcomes to business goals. More sophisticated deployments can move beyond browser changes to server-side or feature experimentation.
The disadvantages are commercial and operational. VWO’s products and allowances can be difficult to compare from a headline price, and higher traffic or advanced capabilities increase cost. A visual editor can also produce flicker, layout shifts, or fragile selectors when a site changes. Use the asynchronous snippet as directed, test performance, and have developers review experiments that alter important interactions.
Our pick: VWO for website CRO; Statsig for engineering-led product experimentation
Convert Experiences: focused website testing
Convert Experiences is a dedicated experimentation platform offering a visual editor, custom code, audience targeting, goals, multipage experiments, split-URL tests, and integrations. It has long emphasized privacy-conscious operation and transparent experimentation controls, making it attractive to agencies and web teams that do not need a large marketing cloud.
Its narrower focus can be an advantage. Teams get robust web experimentation without purchasing content management, commerce, or product-planning products. Agencies should inspect account structure, domains, collaborator permissions, traffic allowances, and client reporting on the current plan.
Convert is still a client-side web testing tool in many common deployments. Dynamic single-page applications, checkout flows, and core product logic may require custom code or a server-side approach. Test the visual editor on the actual framework and consent configuration before committing.
Statsig, PostHog, and GrowthBook for product teams
Statsig centers experimentation on feature gates, server- and client-side SDKs, metrics, and product data. Engineers can assign users consistently, expose a feature gradually, monitor guardrails, and analyze an experiment without inserting a visual-editor overlay. It is well suited to onboarding, pricing presentation, recommendation logic, or application features where assignment must happen before the interface renders.
PostHog brings event analytics, session replay, feature flags, experiments, surveys, and related product tools into one platform. Cloud usage pricing and self-hosting options should be checked against current documentation. The integrated model is helpful for a startup that wants to define events once and use them for funnels and experiments. The risk is treating easy instrumentation as correct instrumentation; duplicate events, bot traffic, changing definitions, and identity errors invalidate results.
GrowthBook provides feature flags and experimentation with open-source roots, SDK support, warehouse integrations, and self-hosting choices. It appeals to technical teams that want control over data and analysis. That control comes with responsibility for deployment, metric definitions, identity, warehouse performance, security, upgrades, and statistical settings. It is not a no-maintenance replacement for Google Optimize.
Client-side versus server-side testing
Client-side tools change a page after browser code loads. They are fast for copy, images, order, styling, and simple funnel variations. The tradeoffs include flicker, performance overhead, blocked scripts, DOM-selector breakage, and exposure of variation code. They should not be used to secure features or enforce prices because visitors can inspect or bypass browser logic.
Server-side experiments assign a variation in the application or service. They support pricing logic, algorithms, APIs, and product features without visible page rewriting. Implementation takes engineering work, and analysts must ensure assignment, exposure, outcomes, and user identity are recorded consistently. A user should remain in the same variation across devices when the hypothesis requires it.
Split-URL tests send traffic to separate pages and can reduce DOM manipulation, but redirects add latency and analytics complexity. Search engines need correct canonicals and consistent content practices. Do not use experimentation to show crawlers a materially different experience from users.
Statistics that prevent false wins
Define the hypothesis, primary metric, guardrails, target population, minimum detectable effect, and stopping rule before launch. If a team watches results daily and stops whenever significance crosses a threshold, false positives increase. Use the platform’s stated statistical method correctly; frequentist, Bayesian, and sequential approaches produce different outputs and assumptions.
Estimate sample size from baseline conversion, desired detectable lift, significance level, and power. A low-traffic site may need months to detect a small improvement. In that case, test a larger change, use a higher-frequency outcome that remains meaningful, combine qualitative research with implementation, or accept that an experiment is not the right decision tool.
Track guardrails such as refunds, cancellations, page speed, support contacts, errors, and revenue per visitor. A variation that raises form completion by hiding important information can reduce lead quality. Segment analysis by device or channel only when planned or clearly exploratory; searching dozens of segments after the test manufactures stories from noise.
Implementation checklist
Start with an instrumentation audit. Confirm that conversions fire once, money uses the correct currency and units, internal users and bots are excluded where appropriate, consent behavior is documented, and user identifiers do not change unexpectedly. Compare experiment events with backend transactions rather than assuming browser analytics are complete.
Use a quality-assurance matrix covering supported browsers, mobile breakpoints, logged-in states, campaign parameters, localization, accessibility, and slow connections. Verify that screen readers, keyboards, error messages, and focus behavior remain usable. Test mutually exclusive experiments and audience rules so one visitor does not receive incompatible changes.
Assign an owner and a rollback path. Save screenshots, code, targeting, metrics, dates, sample calculations, and the final decision. Remove losing client-side code promptly; abandoned experiments create technical debt. When a variation wins, implement it in the product or site code rather than leaving a testing script to serve it forever.
Verdict
VWO is the best general Google Optimize replacement for a marketing-led website program because it combines a usable visual workflow with room to expand. Convert Experiences is a focused alternative for agencies and privacy-conscious web teams. Optimizely and AB Tasty make sense when enterprise governance, personalization, and multiple teams justify the investment. For software products, Statsig is the strongest engineering-led choice, while PostHog offers exceptional value when analytics and flags should share one system; GrowthBook suits teams wanting open-source control. Select the deployment model first, validate instrumentation, and run fewer well-powered experiments instead of chasing constant low-confidence wins.