Otter vs Optimizely

The procurement reality

Optimizely doesn't publish pricing; every plan is individually packaged and sold through sales. Third-party contract data (Vendr) reports a median of roughly $81,000/year across recorded purchases, with contracts ranging from about $31,500 to over $266,000. Negotiation guides also report multi-year discounts, annual price escalators of 3–7%, and implementation costs that can add materially to the license. Treat all of those as estimates — Optimizely doesn't confirm them — but the shape is consistent: this is enterprise procurement with enterprise timelines.

Otter's entire pricing model fits in a sentence: $39/mo for one project, $79/mo for five, $149/mo for unlimited, every feature on every plan, unlimited visitors, cancel anytime. You can be running your first experiment before an Optimizely discovery call would have been scheduled.

Two different statistics philosophies

Optimizely's Stats Engine is built for continuous peeking: sequential, always-valid results with false-discovery-rate control across many metrics. That design shines at enterprise scale, where dozens of stakeholders check dashboards constantly.

Otter takes the planned-test path: you size the test up front (a built-in sample-size advisor helps), choose frequentist or Bayesian analysis per test, and read decision-ready results. Frequentist tests use a two-proportion z-test — with Fisher's exact test for small samples — at 80/90/95/99% confidence, and when a test has multiple challengers the significance threshold is automatically Bonferroni-adjusted so A/B/n tests don't inflate false positives. Bayesian tests report the probability each variant beats control. Auto-stop rules can end a test when it reaches significance, a visitor limit, or a conversion cap.

Neither philosophy is universally better: sequential methods cost some statistical power in exchange for peeking safety; fixed-horizon tests are more powerful when you actually run them to plan. Otter's job is to make running-to-plan easy.

GA4: goals vs data push

Optimizely's GA4 integration pushes experiment and variation data into GA4 for analysis there, and can import GA4 audiences for targeting. What it doesn't do is use your GA4 conversion events as goals on its own results page — Optimizely's docs are explicit that GA4 numbers and Optimizely results won't match.

Otter treats GA4 as a first-class goal source: pick an existing GA4 conversion event as an experiment goal and results are reported against it directly, alongside Otter's own pageview, click, custom-event, and revenue goals. If GA4 is already your source of truth for conversions, you don't have to re-instrument anything.