🚀 2400+ AI tools in the Mega Directory — catalogue updated November 2026.

Optimizely

Digital experimentation platform for A/B testing and feature rollouts.

MarketingPaidCustom pricing — contact sales

Optimizely is a digital experimentation platform for A/B testing and feature rollouts at enterprise scale. It lets product, engineering, and marketing teams test changes, roll features out gradually with flags, and personalize experiences, all with rigorous statistics and governance built in.

Key Features

  • Web and feature experimentation at scale
  • Feature flags and progressive rollouts
  • Personalization and audience targeting
  • Rigorous statistics and experiment governance
  • Program management for testing teams
  • Enterprise integrations and security

Pricing

PlanPriceDetails
CustomCustomPricing is quoted by the vendor on an annual enterprise contract; contact sales for a quote.

Prices as listed in 2026 research. Always check the official site for the latest price — plans change. Free plan: No free plan — paid plans only.

Best for: large companies that need enterprise-grade experimentation, feature flags, and personalization.
Popular with: Marketers, Developers, Business Owners

How it works

The platform covers web experimentation, feature experimentation for apps and services, and content management, so a large organization can run its whole testing and rollout program in one place. It also offers strong analytics, program management, and integrations with the rest of the enterprise stack.

Optimizely does not publish prices and offers no free plan. Pricing is custom and quoted by the vendor, typically on an annual enterprise contract. It is built for large companies with dedicated experimentation teams, not for small sites that need a quick A/B test.

Pros and Cons

✓ Pros

  • Enterprise-grade experimentation and feature flags
  • Strong statistics and governance controls
  • Scales to very large testing programs

✗ Cons

  • No published pricing; everything is a custom enterprise quote
  • Complex platform that needs dedicated staff to run well
  • Overkill for small teams and simple A/B tests

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Last reviewed: November 2026

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