On September 30, 2023, Google retired both Google Optimize and Optimize 360. No migration path. No recommended replacement. No wind-down grace period beyond the announced date. Every experiment you had running stopped. Every report became read-only. The entire free A/B testing platform that hundreds of thousands of marketers and product teams had built their workflows around was simply gone.
If you are reading this in 2026, you are either still using a patchwork solution you set up in a hurry after the shutdown, evaluating tools for the first time, or reconsidering a platform you settled for under deadline pressure. This guide is for all three situations.
Why Google Killed Optimize (and What It Means for You)
Google cited low usage and its shift toward Privacy Sandbox as reasons for shutting down Optimize. The honest subtext: A/B testing was never a core revenue driver for Google. Optimize was always adjacent to Google Analytics, and once GA4 became the strategic focus, Optimize got deprioritized. The platform stopped receiving meaningful updates years before the shutdown was announced.
The practical consequence for teams is a reminder that free tools built by platforms with other revenue incentives are not permanent infrastructure. The zero cost is real, but so is the dependency risk. If Google Optimize was central to your experimentation program, the shutdown was a painful reminder to treat tooling decisions as real infrastructure choices.
“Every month without a working experimentation program is a month of revenue decisions made on instinct instead of evidence. The cost of the gap is real, even if it is invisible on a balance sheet.”
What to Look for in a Google Optimize Replacement
Before comparing specific tools, it helps to know what made Google Optimize attractive in the first place. Most of its users were not running complex multivariate tests or needing enterprise-grade statistical modeling. They wanted a few things done well.
- Visual editor for non-engineers: click an element, change it, no code required
- Simple A/B and redirect test setup without a developer on every experiment
- Integration with Google Analytics for unified reporting
- No cost or low cost for teams with moderate traffic
- Fast setup: snippet in, experiment running, no IT approval process
- Statistical significance reporting that a non-statistician can act on
Any replacement worth considering should hit most of those marks. Where tools differ is in price, depth of analytics, targeting capabilities, and whether they require developer involvement for anything beyond the most basic tests.
The Best Google Optimize Alternatives in 2026
1. Segmently: Closest to the Google Optimize experience, with a free tier
Segmently is built around the same core premise as Google Optimize: you should be able to run A/B tests on your site without involving an engineer for every experiment and without signing a six-month enterprise contract. The setup is one script tag. The visual editor lets you select any element on your page, modify it, and launch the experiment. Statistical significance reporting tells you when you have a winner.
The free tier supports one active project and includes snippet installation, visitor bucketing, and conversion tracking. Paid plans start at $599 per month and include full analytics, multi-page funnel goals, Slack notifications, and experiment scheduling. There is no sales call required to sign up or upgrade. All pricing is published.
- Free tier with no time limit and no credit card required
- Visual point-and-click editor (works on any site, no framework restrictions)
- One-line JavaScript snippet install
- Statistical significance reporting with per-variant conversion rates
- Anti-flicker protection: visitors never see the original version before changes load
- Slack notifications when experiments reach significance
- Multi-page funnel goal tracking on Professional and above
- Custom event tracking via JavaScript API
The main limitation compared to enterprise tools is that Segmently is optimized for the teams that Google Optimize actually served well: marketing and product teams running sequential A/B tests without dedicated experimentation engineers. If you need server-side experimentation at scale or full-stack feature flag management, you will eventually outgrow it. But for the 95% of Google Optimize use cases, it covers the ground.
2. Optimizely: Enterprise power at an enterprise price
Optimizely is the de facto benchmark for enterprise A/B testing. If your team has a dedicated experimentation function, significant engineering resources allocated to the platform, and a budget that justifies five-figure annual contracts, Optimizely is worth evaluating. The platform supports web, full-stack, and feature experimentation across every channel.
The problem for most Google Optimize users is that Optimizely was never designed for the same market. It requires a sales conversation to get pricing, takes weeks to onboard, and assumes a dedicated data science or engineering resource to extract its full value. Teams that migrate from Optimize to Optimizely typically find themselves paying for capabilities they do not use while re-learning a significantly more complex interface.
3. VWO: Mid-market with a full feature set, but complexity to match
Visual Website Optimizer (VWO) is one of the longest-standing tools in the A/B testing market. Its feature set is broad: A/B testing, multivariate testing, session recordings, heatmaps, and a built-in data platform. For teams that want all of those capabilities in one product, VWO makes a coherent case.
Pricing starts around $199 per month for basic plans but scales meaningfully with traffic volume and feature tier. Unlike Google Optimize, VWO is not designed as a lightweight starter tool. Teams report a steeper learning curve and a more involved setup process. The tradeoff is access to session recordings and heatmaps that simpler tools do not include.
4. Convert: Privacy-first, mid-market, with self-hosted options
Convert is a well-regarded mid-market tool with a strong emphasis on privacy compliance. It supports GDPR, CCPA, and cookieless experimentation modes, which makes it a good fit for teams operating in regulated industries or geographies with strict data residency requirements. It offers server-side and client-side experimentation, a visual editor, and direct integrations with the major analytics platforms.
There is no free tier, and pricing is not publicly listed. Getting started requires a demo conversation. For teams with privacy-sensitive audiences and the budget for a mid-market tool, Convert is worth including in any evaluation. For teams coming from Google Optimize who want a low-friction transition, the onboarding overhead is a real cost.
5. AB Tasty: Strong personalization layer, enterprise positioning
AB Tasty bridges A/B testing and personalization on a single platform. It supports experiment types from simple A/B tests to behavioral targeting segments that serve different experiences based on referral source, device type, or visitor history. The platform is more popular in Europe and is often positioned alongside CDPs for personalization-heavy teams.
Like Convert, there is no self-serve signup. Access requires requesting a demo, and pricing is negotiated. The platform is a genuine consideration for teams with a personalization strategy that extends beyond simple variant testing, but the barrier to entry rules it out for most direct Google Optimize replacements.
Side-by-Side Comparison
- Segmently: Free tier available, published pricing ($599-$9,999/mo), visual editor, no sales call required, anti-flicker protection, Slack alerts
- Optimizely: No free tier, negotiated pricing (typically $50K-$200K+ annually), most powerful feature set, requires engineering resources
- VWO: No free tier, starts at $199/mo, broad feature set including recordings and heatmaps, steeper learning curve
- Convert: No free tier, quoted pricing, privacy-first with GDPR/CCPA modes, demo required to get started
- AB Tasty: No free tier, quoted pricing, personalization-heavy, Europe-popular, demo required
What Most Google Optimize Users Actually Need
The vast majority of Google Optimize experiments were A/B tests on landing pages, CTAs, headlines, and pricing pages. They were run by marketers and product managers, not by experimentation engineers. The winning variants were applied by editing a stylesheet or asking a developer to update a template. The statistical reporting was read by people who understood conversion rates, not Bayesian probability.
That context matters when choosing a replacement. The most common mistake teams make after the Google Optimize shutdown is overbuying. A tool that requires a three-month implementation project and a dedicated analyst to interpret results is not an upgrade from Optimize; it is a category change. Most teams do not need the category change. They need something that runs reliable A/B tests, shows clear winner data, and does not require a procurement cycle to get started.
“The teams getting the most out of A/B testing are not the ones with the most sophisticated platforms. They are the ones running experiments consistently and acting on results quickly.”
Experimentation research, 2025
How to Migrate Off Google Optimize (If You Have Not Yet)
If you have been running without a real A/B testing platform since the shutdown, here is a practical migration path for most teams.
- 1Export any historical Google Optimize reports you want to preserve. The data is no longer accessible through the platform, but if you had GA4 or Universal Analytics synced, segment-level data may still exist in your analytics account.
- 2Audit the types of experiments you were actually running. Most teams were doing CTA copy tests, landing page layout tests, and form optimization. That shapes which replacement is right for your needs.
- 3Install your new tool on a staging environment first. Verify that the visual editor works with your CMS or framework before rolling out to production.
- 4Rebuild your top three highest-impact historical experiments as the first tests on the new platform. You likely already know which variants performed well; validate that the new tool produces consistent results on your current traffic.
- 5Set up conversion goal tracking that maps to your actual revenue metrics. Page views and bounce rate are proxies. Revenue events, trial signups, and checkout completions are the real goals.
- 6Run at least one full test cycle before evaluating the platform. First-experiment results are often skewed by configuration learning and audience warm-up. Judge the tool after you have correctly configured and launched your second or third test.
The Honest Recommendation
For most teams coming from Google Optimize, Segmently covers the use cases that drove Optimize adoption in the first place: fast setup, visual editing without engineering involvement, clear statistical reporting, and pricing that does not require a budget approval cycle. The free tier is a real starting point, not a hobbled trial.
If you need session recordings and heatmaps alongside A/B testing, VWO is the most natural next step. If you operate in a privacy-sensitive market and need GDPR-first tooling, Convert is worth the added evaluation friction. If you are running experimentation at organizational scale with a dedicated engineering team, Optimizely is built for your context.
What none of these situations justifies is continuing to run your site without a systematic experimentation program. The traffic you are generating today is either being optimized or being wasted. Google Optimize being gone changed your tooling situation. It did not change the underlying revenue opportunity sitting in your existing visitor base.
Segmently has a free tier, installs in a single script tag, and does not require a conversation with sales to get started. Most teams have their first experiment live within the same day they sign up. If you have been without a testing platform since the Optimize shutdown, the cost of waiting another month is real, even if it is invisible on a spreadsheet.