12 Best A/B Testing Tools to Improve Conversions in 2026
Compare the 12 best A/B testing tools in 2026, including pricing, targeting, analytics, feature flags, privacy, and guidance for choosing the right platform.
Short answer: VWO is the strongest broad CRO choice; Convert Experiences is the clearest option for transparent self-serve pricing and full-stack testing; Optimizely and Adobe Target fit mature enterprise programs; GrowthBook suits teams that want open-source control; PostHog and Amplitude Experiment combine analytics with experimentation; LaunchDarkly and Statsig connect experiments to feature delivery. The right choice depends on where you test, how much engineering control you need, your statistical requirements, privacy constraints, and whether pricing is based on tested users, events, traffic, or an annual contract.
Google Optimize closed on 30 September 2023, so teams replacing it need to evaluate more than a visual editor. A sustainable program needs reliable assignment, guardrail metrics, audience targeting, quality warnings, integrations, and a way to connect results to product releases. This guide compares 12 leading options and gives a practical selection process.
How to evaluate an A/B testing platform
Score each candidate against the same questions before booking a demo or migrating code:
- Surface area: Does it test websites only, or also mobile apps, server-side code, APIs, and feature flags?
- Implementation: Can marketers launch tests in a visual editor, or will engineers edit application code and SDKs?
- Targeting: Are audiences based on URL, device, geography, behavior, account attributes, or custom events?
- Measurement: Can you define a primary conversion, secondary metrics, and guardrails such as errors, revenue, or retention?
- Statistics: Does the platform explain sample size, uncertainty, sequential monitoring, and experiment quality warnings?
- Operations: Are feature flags, progressive rollouts, approvals, audit logs, and rollback available?
- Data and privacy: Where is data hosted, which identifiers are collected, and can you control retention?
- Economics: Is the bill based on tested users, events, traffic, seats, or a negotiated annual contract?
At-a-glance comparison
| Tool | Best fit | Coverage | Pricing signal |
|---|---|---|---|
| Convert Experiences | Mid-market teams needing transparent full-stack testing | Web, server-side, feature flags, API access | $299/month annually or $399 monthly starting price |
| Optimizely | Complex enterprise experimentation programs | Web, product, personalization, experimentation | Quote-based enterprise contracts |
| VWO | Broad CRO across web, mobile, and feature testing | Web, mobile, server-side, targeting, heatmaps, recordings | Plan limits and packaging vary |
| Adobe Target | Organizations invested in Adobe Experience Cloud | Experimentation and personalization | Enterprise quote |
| Amplitude Experiment | Product teams combining analytics and testing | Product behavior and experimentation | Plan dependent |
| GrowthBook | Technical teams wanting open-source flexibility | Feature flags and experimentation | Self-hosting or managed options |
| Statsig | Developer-supported product experimentation | Feature experimentation and product analytics | Plan dependent |
| PostHog | Teams wanting analytics and experiments together | Product analytics and experimentation | Usage and plan dependent |
| Kameleoon | AI-assisted optimization | Web experimentation and personalization | Quote-based or plan dependent |
| LaunchDarkly | Release workflows with experimentation | Feature flags, rollouts, experiments | Usage and seat dependent |
| Dynamic Yield | Advanced ecommerce personalization | Personalization and testing | Enterprise quote |
| Crazy Egg | Early-stage teams wanting lightweight testing | Web testing and behavior analytics | Plan dependent |
1. Convert Experiences
Convert is the best starting point when transparent pricing and full-stack capability matter. Its public pricing lists plans from $299 per month when paid annually, or $399 per month when paid monthly. Convert’s comparison model emphasizes tested-user limits, annual price, feature flags, web experimentation, and API access.
Choose it when you need client-side and server-side tests, want to verify allowance limits before purchase, and prefer a public starting price. Confirm the current tested-user allowance and included features before forecasting spend.
2. Optimizely
Optimizely fits mature teams running complex experimentation programs. It is a strong candidate when experimentation spans several products, business units, or personalization workflows and governance matters as much as launching a variation. Enterprise platforms commonly use annual contracts that can begin around $36,000 per year and rise with traffic and features; treat that figure as a market signal, not a quote.
Ask about implementation ownership, approval workflows, statistical controls, data export, and the cost of adding environments or high-volume traffic.
3. VWO
VWO is the broadest CRO suite in this shortlist. It covers web, mobile, server-side, and feature testing, with targeting, metrics, reports, heatmaps, and session recordings. VWO’s current testing page advertises 17 industries, 193,000 experiments, 38,000 websites, and 270,000 variations.
Choose VWO when one team wants testing and behavioral diagnostics in one product. Validate how its limits are calculated, especially if you plan to use recordings or run mobile and server-side experiments alongside web tests.
4. Adobe Target
Adobe Target is designed for enterprises already invested in Adobe Experience Cloud. Its appeal is the connection between experimentation, personalization, customer data, and existing Adobe governance. It is most suitable when procurement, identity, permissions, and integration with the broader Adobe stack outweigh the simplicity of a standalone tool.
5. Amplitude Experiment
Amplitude Experiment suits product teams that want behavioral analytics and experimentation in the same stack. It reduces the distance between discovering a funnel problem, defining an audience, and measuring a test. Check event volume, identity resolution, exposure logging, and whether your required statistical reporting is included in the plan.
6. GrowthBook
GrowthBook is the leading choice for technical teams that want open-source flexibility or self-hosting. It is useful when you need control over deployment, data storage, feature flag architecture, or the analysis layer. The tradeoff is operational responsibility: your team must design reliable exposure events, permissions, observability, and upgrades if you self-host.
Before choosing it, document who owns the SDK integration, experiment analysis, warehouse connections, and incident response. Open source lowers platform lock-in but does not eliminate engineering work.
7. Statsig
Statsig targets product-led experimentation with developer support. It is a practical fit when feature flags, product metrics, and experiment decisions are closely connected. Evaluate SDK coverage, latency behavior, identity handling, metric definitions, and how easily a winning variant becomes a controlled rollout.
8. PostHog
PostHog combines product analytics and experimentation. It is attractive to smaller product teams that want funnels, cohorts, feature usage, and tests in one workflow, and it is frequently considered by teams replacing Google Optimize with a lower-cost or more flexible stack. Confirm current usage limits, hosting choices, and the relationship between analytics volume and experimentation cost.
9. Kameleoon
Kameleoon focuses on AI-assisted optimization and experimentation. It belongs on a shortlist when personalization recommendations and automated optimization are strategic requirements. Ask how suggestions are validated, how much control experiment owners retain, and which data is required before automated decisions are reliable.
10. LaunchDarkly
LaunchDarkly is strongest when experimentation is part of a release workflow. Feature flags, progressive delivery, and rollback are central, so engineering teams can expose a change gradually and connect rollout decisions to product results. It may be more platform than a marketing-only visual testing tool; budget for SDK integration, flag hygiene, and ongoing cleanup.
11. Dynamic Yield
Dynamic Yield is aimed at advanced personalization and ecommerce testing. Consider it when recommendations, merchandising, audience rules, and conversion optimization need to work together across a large catalog or multiple customer segments. Confirm integration requirements for your commerce platform, catalog, identity, and consent model.
12. Crazy Egg
Crazy Egg is the lightweight option for early-stage teams that need basic web testing and behavior insight without adopting an enterprise experimentation program. It can be a sensible first step when the team has modest traffic and straightforward page-level hypotheses. Plan a migration path if you expect server-side tests, complex feature flags, or multi-product governance.
Which tool should you choose?
| Requirement | Shortlist |
|---|---|
| Transparent self-serve pricing and full-stack testing | Convert Experiences |
| Broad web, mobile, server-side, targeting, and reporting | VWO |
| Enterprise governance and personalization | Optimizely or Adobe Target |
| Open source or self-hosting | GrowthBook |
| Analytics and experimentation together | PostHog or Amplitude Experiment |
| Feature flags and progressive delivery | LaunchDarkly or Statsig |
| Advanced ecommerce personalization | Dynamic Yield |
| Simple, lightweight web testing | Crazy Egg |
A practical migration plan after Google Optimize
- Inventory tests: Record hypothesis, audience, primary metric, guardrails, traffic, and current owner.
- Separate experiments from rollouts: Decide which changes need statistical measurement and which only need a safe release mechanism.
- Choose an exposure event: Log the moment a user is eligible for and sees a variant. Do not infer exposure from a later conversion.
- Define identity: Keep assignment stable across sessions and devices where your privacy policy permits.
- Run a quality check: Confirm balanced allocation, event delivery, page performance, consent behavior, and bot filtering before trusting results.
- Set stopping rules: Predefine sample size, duration, primary metric, and guardrails. Avoid repeatedly checking results and stopping on a temporary peak.
- Document decisions: Store the winning variant, confidence or uncertainty information, segment effects, and rollout plan.
Minimal implementation pattern
Regardless of vendor, keep assignment and exposure separate from conversion tracking. A simplified browser pattern looks like this:
const variant = experiment.assign('checkout-copy');
experiment.expose('checkout-copy', variant);
renderCheckoutCopy(variant);
button.addEventListener('click', () => {
analytics.track('checkout_started', { experiment: 'checkout-copy', variant });
});For server-side tests, assign the variant before rendering or responding, persist the assignment, and send the exposure event from the service that made the decision. Add guardrails for error rate, latency, cancellations, and revenue so a local conversion lift does not hide product damage.
Performance, reliability, privacy, and cost checks
- Performance: Client-side visual editors can create flicker or delay if loaded late. Measure first render and interaction latency with the experiment enabled.
- Reliability: Decide what happens when the SDK, flag service, or analytics endpoint is unavailable. Use a deterministic fallback and preserve the last safe assignment where appropriate.
- Statistics: Require transparent explanations of uncertainty and quality warnings. A dashboard declaring a winner without context is not a methodology.
- Privacy: Review cookies, consent requirements, retention, regional hosting, data export, and deletion workflows.
- Cost: Model tested users or event volume, not just the advertised monthly price. Include implementation, warehouse, seats, recordings, support, and annual commitments.
Common problems and fixes
| Problem | Likely cause | Fix |
|---|---|---|
| Variants are unbalanced | Assignment runs after conversion or is not persisted | Assign at eligibility, persist the decision, and log exposure once. |
| Conversions appear lower than analytics | Consent, ad blockers, or event naming differences | Reconcile consent state, compare raw events, and document attribution windows. |
| Users see different variants | Identity changes between sessions or devices | Use a stable identifier permitted by your privacy policy. |
| Page flickers | Client-side variation arrives after content paint | Reduce payload, preload safely, or move the decision server-side. |
| Test never reaches significance | Low traffic, diluted audience, or weak effect | Revisit power, audience definition, metric sensitivity, and test duration. |
| Winning result disappears after rollout | Novelty, segment mix, or implementation mismatch | Run a holdback, monitor guardrails, and verify production parity. |
Need clean screenshots for experiment QA?
When you document variants, review landing pages, or generate visual regression evidence, ScreenshotNeo is the alternative to try first. It removes cookie and consent banners, newsletter popups, and chat widgets before capture; bot checks, blank pages, failed loads, timeouts, and cache hits are not billed; and its MCP server lets Claude, Cursor, and other AI agents take screenshots. The Free plan includes 1,000 screenshots per month with no card, and paid plans start at $5 for 3,000 shots.
Or skip the browser setup
Make one request to capture a clean result:
curl -G 'https://api.screenshotneo.com/v1/shot' -d access_key=YOUR_API_KEY --data-urlencode url=https://stripe.com -o shot.webpimport requests
r = requests.get('https://api.screenshotneo.com/v1/shot', params={'access_key': 'YOUR_API_KEY', 'url': 'https://stripe.com'}, timeout=90)
open('shot.webp', 'wb').write(r.content)const q = new URLSearchParams({ access_key: 'YOUR_API_KEY', url: 'https://stripe.com' });
const res = await fetch(`https://api.screenshotneo.com/v1/shot?${q}`);See the full option list and response details in the ScreenshotNeo documentation. Create a free ScreenshotNeo account to start with 1,000 screenshots per month and no card.
FAQ
What replaced Google Optimize?
There is no single replacement. GrowthBook and PostHog are common lower-cost or flexible options, while VWO, Convert, Optimizely, and Adobe Target cover broader or more enterprise needs.
Is a visual editor enough for serious experimentation?
Usually not. Mature programs also need server-side assignment, feature flags, guardrail metrics, quality diagnostics, permissions, and reliable exposure data.
How much should an A/B testing platform cost?
Convert publicly lists $299 per month annually or $399 monthly at the starting level. Enterprise tools often use annual contracts that can start around $36,000 per year and increase with traffic and features. Compare total cost using your tested-user or event forecast.
Should experiments and feature flags use the same tool?
They can, especially when release management and experimentation are tightly connected. Keep separate ownership and cleanup policies so temporary experiments do not become permanent production flags.
