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Decision Guide

Best A/B Testing Tool for SaaS

Updated December 2026
12 min read
TL;DR

For most SaaS companies: Use ExperimentHQ for marketing site tests (pricing pages, landing pages) and PostHog or Statsig for in-app experiments. Key tests to run: pricing page layout, trial length, onboarding flow, and upgrade prompts. Avoid Optimizely unless you have $50K+ budget.

Who this is for

  • SaaS founders and growth teams
  • Product managers optimizing conversion funnels
  • B2B SaaS companies with freemium or trial models

Who this is NOT for

  • E-commerce (see Shopify guide)
  • Enterprise SaaS with dedicated CRO teams (consider VWO/Optimizely)

High-Impact Tests for SaaS

TestImpactWhat to Test
Pricing page layoutHigh3 plans vs 4 plans, feature emphasis, annual toggle
Trial lengthHigh7-day vs 14-day vs 30-day free trial
Onboarding flowHighGuided setup vs self-serve, checklist completion
Upgrade promptsMediumTiming, messaging, and placement of upgrade CTAs
Feature gatingMediumWhich features to gate behind paid plans
Social proofMediumCustomer logos, testimonials, case study placement

Tool Comparison

ToolPriceTrial TrackingPricing TestsVerdict
ExperimentHQ$29/moBest Overall
PostHogFree - $450/moBest with Analytics
StatsigFree - CustomBest for Developers
LaunchDarkly$10/seat/moBest Feature Flags
Optimizely$50,000+/yrEnterprise only

Our Recommendation

For marketing site (pricing, landing pages): Use ExperimentHQ. Visual editor, quick setup, $29/month.

For in-app experiments: Use PostHog (if you want analytics bundled) or Statsig (if you want feature flags).

For pricing experiments: Use server-side testing to avoid showing different prices to the same user.

FAQ

Can I A/B test pricing in SaaS?
Yes, but carefully. Test pricing page layout and presentation first (plan names, feature emphasis, annual toggle). Only test actual prices with server-side testing to ensure consistency.
Should I use the same tool for marketing site and app?
Not necessarily. Marketing site tests (visual changes) work well with visual editors. In-app experiments often need code-based tools with feature flags. Many teams use two tools.

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