Optimizely built much of the modern experimentation playbook, and for large enterprises it still does the job. The problem most teams run into is not the product. It is the price tag and the way the contract is structured.

Optimizely does not publish pricing. You talk to sales, and reported figures for Web Experimentation on its own start at roughly $36,000 a year and climb into six figures once you bundle personalisation or feature management. Pricing scales with monthly tracked users or impressions, so crossing a tier threshold can trigger a sharp jump in cost. There is no free plan for the web testing product either. None of that is a deal breaker if you are a large company running hundreds of tests a quarter. It is a real problem if you are a startup or a mid-market team that wants to run a handful of meaningful experiments a month.

This guide covers nine alternatives that are genuinely worth a trial. Each one suits a different kind of team, so the section headers tell you who should look first.

How to pick an A/B testing tool

Before the list, three questions sort the field quickly.

  • Client-side or server-side? Marketing teams testing copy, layout, and offers want a visual editor that works without engineering. Product teams testing features, pricing logic, or onboarding flows want feature flags and SDKs.
  • How are you billed? Monthly tracked users (MTUs) and impressions are the usual meters. Open-source and warehouse-native tools often bill by seat or event volume instead, which behaves very differently as you scale traffic.
  • What compliance do you actually need? Healthcare, finance, and EU-first teams should shortlist on GDPR, CCPA, and HIPAA support before anything else, because retrofitting compliance later is painful.

Keep your real numbers handy: monthly traffic, how many people will build tests, and your data warehouse if you have one. Those three figures decide most of this for you.

VWO and AB Tasty: the direct enterprise replacements

The biggest story in this space recently is that VWO and AB Tasty are no longer two separate companies. On 20 January 2026 the two combined under Everstone Capital ownership into a single digital experience optimisation business with more than 4,000 customers, as reported by TechCrunch. They still run as distinct platforms for now, so it is worth understanding both.

VWO is the closest like-for-like swap for most marketing teams. It bundles A/B testing, multivariate testing, heatmaps, session recordings, and surveys, with a visual editor that non-technical users can run. Pricing is MTU-based: the Growth plan for testing runs around $665 a month at 100,000 monthly tracked users, billed annually, with higher tiers quoted on request.

AB Tasty leans further into enterprise personalisation and AI. It does client-side testing, server-side testing, and feature management, plus its EmotionsAI segmentation and an evidence-focused assistant called Evi. Pricing is custom and quote-only.

  • Suits: mid-market and enterprise marketing teams that want a polished visual editor plus personalisation, and prefer a managed vendor over wiring up their own stack.

Convert Experiences: transparent pricing for mid-market CRO

Convert is the alternative people reach for when Optimizely’s pricing model is the actual objection. It publishes its rates. The Growth plan starts at $299 a month billed annually for 100,000 tested users, and every plan includes the full feature set rather than gating capabilities behind higher tiers. There is a 15-day trial with no card required.

On the substance, it covers A/B, split URL, and multivariate testing with advanced targeting, and it is built around first-party cookies. Convert markets itself as GDPR compliant and HIPAA and CCPA ready, and states it does not resell data, which matters for privacy-sensitive teams that still want a hosted tool rather than self-hosting.

  • Suits: mid-market ecommerce and SaaS CRO teams that want predictable, published pricing and strong privacy defaults without an enterprise contract.

Kameleoon: experimentation for regulated industries

Kameleoon is the pick when compliance is non-negotiable. Its A/B testing, full-stack, and personalisation products are GDPR, CCPA, and HIPAA compliant, with data centres in the US, Europe, and Asia, and it will sign Business Associate Agreements with healthcare providers. That BAA support is the detail that lets regulated companies actually use it for both testing and personalisation.

It is a unified platform spanning web experimentation, feature experimentation, and AI-driven personalisation, with 300-plus integrations. Its prompt-based experimentation feature lets you draft a test by describing it in plain language, which lowers the barrier for non-developers.

  • Suits: privacy-first teams in healthcare, finance, and the EU that need full-stack testing with compliance built in rather than bolted on.

GrowthBook: open-source and warehouse-native

GrowthBook is the strongest open-source option for product and engineering teams. The core is MIT licensed, and the self-hosted edition is free with unlimited users, feature flags, and experiments. The hosted Starter plan is free for up to three users.

Its defining feature is being warehouse-native. Instead of ingesting your events, it writes SQL against your existing data in BigQuery, Snowflake, Databricks, and other sources, so there is no per-event pricing and no second copy of your data. The stats engine is serious too, with CUPED variance reduction, sequential testing, Bayesian analysis, bandits, and sample ratio mismatch checks. It bills by seat, not by traffic, which is the opposite of Optimizely’s model and far friendlier as your volume grows.

  • Suits: engineering-led product teams with a data warehouse who want a feature-flag-plus-experimentation stack they can self-host or pay for per seat.

PostHog: all-in-one for product teams

PostHog folds experiments into a wider product platform that includes analytics, session replay, feature flags, surveys, and error tracking. If you want to ship a feature to 10 percent of users, watch the replays, and check whether conversion moved without leaving one tool, this is the setup.

The core is MIT licensed and self-hostable. The cloud free tier is generous: one million events, one million feature flag requests, and 5,000 recordings every month, then usage-based pricing after that. Experiments are powered by the same feature flags, with statistical significance built in.

  • Suits: product and growth teams at startups that want experimentation as one piece of a single analytics and product stack, with a real free tier to start.

Statsig: powerful, but check the ownership story first

Statsig earned its reputation as a warehouse-native experimentation platform with a strong following among AI-focused companies. Its free Developer tier is unusually generous, with two million events a month, unlimited seats, and analytics plus experimentation included.

The complication is corporate. OpenAI acquired Statsig in September 2025 and kept the engineering team. Then in May 2026 Amplitude took over the Statsig brand, platform, and customer base while those engineers stayed at OpenAI, as Amplitude described in its own announcement. That leaves Amplitude maintaining a codebase whose original builders work elsewhere, and Amplitude now runs two overlapping experimentation products, so the roadmap carries some uncertainty. The technology is excellent. Just go in with eyes open about who owns and steers it.

  • Suits: technically strong teams that want warehouse-native experimentation and a big free tier, and are comfortable with a platform in transition.

Crazy Egg: the budget heatmap-plus-testing combo

Crazy Egg is the cheapest entry point on this list and the easiest to set up. Plans start at $29 a month billed annually for heatmaps, scroll maps, and recordings, with A/B testing included from the $49 Standard plan upward. You will not get full-stack experimentation or advanced statistics, but for a small ecommerce site that wants to see where people click and run simple page tests, that is fine.

  • Suits: small teams and solo operators who want to understand on-page behaviour and run basic tests cheaply.

Unbounce: landing-page testing without developers

Unbounce is not a general site-wide testing tool. It is a landing page builder with experimentation attached, aimed at marketers running paid campaigns. The Experiment plan at $149 a month (cheaper annually) includes unlimited A/B tests and variants, and the Optimize plan at $249 a month adds Smart Traffic, an AI feature that routes each visitor to the variant most likely to convert for them. All plans include a 14-day trial.

  • Suits: performance marketers who need fast, no-code landing page tests for ad traffic and do not want to involve engineering.

Adobe Target: deep personalisation for Adobe shops

Adobe Target is the enterprise option for companies already inside the Adobe Experience Cloud. Beyond standard A/B and multivariate testing, its Sensei AI powers Auto-Allocate, which shifts traffic to the winning variation automatically, and Auto-Target, which personalises at the individual visitor level. It integrates tightly with Adobe Analytics, AEM, and the Real-Time CDP. Pricing is enterprise and quote-only.

  • Suits: large organisations already standardised on Adobe who want personalisation and testing inside one connected stack.

A note on Google Optimize

If you arrived here looking to replace Google Optimize rather than Optimizely, the two get confused constantly. Google sunset Optimize on 30 September 2023. The free GA-native options that come closest in spirit today are the open-source tools above, GrowthBook and PostHog, though neither replicates Optimize’s old one-click Analytics integration exactly.

Frequently asked questions

Is there a free alternative to Optimizely? Yes. GrowthBook and PostHog both have free tiers and MIT-licensed self-hosted editions with unlimited experiments, and Statsig offers a free Developer tier with two million events a month. These suit technical teams. Hosted marketing tools like Convert and Crazy Egg are paid but start far below Optimizely’s reported $36,000-a-year floor.

Which Optimizely alternative is best for ecommerce? For mid-market ecommerce, Convert Experiences gives you transparent pricing and full features from $299 a month. VWO is strong if you want testing, heatmaps, and surveys in one suite. For small shops on a tight budget, Crazy Egg combines heatmaps and basic testing, with A/B testing from its $49-a-month Standard plan.

What is the difference between client-side and server-side testing? Client-side testing changes the page in the visitor’s browser, usually through a visual editor, and is ideal for copy, layout, and design changes that marketers run without code. Server-side testing changes logic on your servers using feature flags and SDKs, which suits product teams testing features, pricing, and onboarding flows.

Did VWO and AB Tasty really merge? Yes. They combined on 20 January 2026 under Everstone Capital into a single company with more than 4,000 customers. They currently still operate as separate platforms, so you can trial each one individually while watching how the combined roadmap develops.

Which alternatives are HIPAA compliant? Kameleoon is built for regulated industries and will sign Business Associate Agreements for healthcare use, covering GDPR, CCPA, and HIPAA. Convert markets itself as GDPR compliant and HIPAA and CCPA ready. Always confirm current compliance details and request a signed agreement directly from the vendor before handling protected data.

How much should a mid-market team budget for A/B testing? A realistic range is $300 to $1,500 a month for a hosted mid-market tool such as Convert or VWO at moderate traffic, scaling with your monthly tracked users. Open-source tools billed per seat can be cheaper at high traffic. Avoid tools that bill purely on impressions if you expect rapid traffic growth, because that is where costs spike unexpectedly.

If you want to get more out of whichever tool you choose, the bottleneck is rarely the platform. It is test design and sample size. Read our guide to building a structured A/B testing programme before you sign any contract, because the best tool in the world will not save a poorly designed experiment.