How do you define 'tested users'?

A tested user is any visitor included in any experiment (A/B Testing, Personalization, or Survey) and visible in the reporting area. For example, if 500 users see the control page and 500 see the variation page in an A/B test, you consume 1,000 tested users.

Can the Omniconvert code be installed using Google Tag Manager?

Yes, you can install our code using GTM. However, this may slow down code deployment, causing issues like screen flickering during A/B tests or delayed triggering for popups.

Can Omniconvert be used for any type of website?

Omniconvert works for most self-hosted websites, including custom-coded sites, CMS platforms (e.g., WordPress, Joomla, Drupal), and eCommerce platforms (e.g., Shopify, Magento, BigCommerce). The main requirement is access to the source code to paste in Omniconvert's code.

LaunchDarkly vs Optimizely

Comprehensive feature comparison. Scroll down on the right to view all rows.

Read other comparisons between LaunchDarkly and Optimizely.

Google Content Experiments vs Optimizely Sitecore vs Optimizely Apptimize vs Optimizely CrazyEgg vs Optimizely Drift vs Optimizely Dynamic Yield vs Optimizely Evergage vs Optimizely Firebase A/B Testing vs Optimizely Formisimo (Zuko) vs Optimizely Fullstory vs Optimizely Hello Bar vs Optimizely Hotjar vs Optimizely Instapage vs. Optimizely Intercom vs Optimizely Kissmetrics vs Optimizely Klaviyo vs Optimizely LaunchDarkly vs Fullstory LaunchDarkly vs Hotjar LaunchDarkly vs Unbounce LaunchDarkly vs VWO Mailchimp vs Optimizely Mixpanel vs Optimizely Monetate vs Optimizely Mouseflow vs Optimizely OneSignal vs Optimizely Optimizely vs VWO Optimizely vs Convert Experiences Price Intelligently vs Optimizely Profitwell vs Optimizely Quaraloo vs Optimizely Split.io vs Optimizely Sumo vs Optimizely Survicate vs Optimizely Typeform (2012) vs Optimizely Unbounce vs Optimizely Zoho PageSense vs Optimizely Convert vs LaunchDarkly

Attribute	LaunchDarkly	Optimizely	Comparison in Detail (Info)
Tool name LaunchDarkly and Optimizely approach tool name from different product perspectives without direct overlap.	LaunchDarkly LaunchDarkly and Optimizely approach tool name from different product perspectives without direct overlap.	Optimizely LaunchDarkly and Optimizely approach tool name from different product perspectives without direct overlap.	LaunchDarkly and Optimizely approach tool name from different product perspectives without direct overlap.
Vendor LaunchDarkly and Optimizely approach vendor from different product perspectives without direct overlap.	LaunchDarkly Inc. LaunchDarkly and Optimizely approach vendor from different product perspectives without direct overlap.	Optimizely Inc. LaunchDarkly and Optimizely approach vendor from different product perspectives without direct overlap.	LaunchDarkly and Optimizely approach vendor from different product perspectives without direct overlap.
Website URL This row lists the official websites for LaunchDarkly and Optimizely and does not represent a functional distinction.	launchdarkly.com This row lists the official websites for LaunchDarkly and Optimizely and does not represent a functional distinction.	optimizely.com This row lists the official websites for LaunchDarkly and Optimizely and does not represent a functional distinction.	This row lists the official websites for LaunchDarkly and Optimizely and does not represent a functional distinction.
Category or type LaunchDarkly is primarily a feature management and flagging platform, while Optimizely is positioned as a digital experimentation suite.	Feature‑management and experimentation websites LaunchDarkly is primarily a feature management and flagging platform, while Optimizely is positioned as a digital experimentation suite.	Digital‑experience websites with experimentation and feature flags LaunchDarkly is primarily a feature management and flagging platform, while Optimizely is positioned as a digital experimentation suite.	LaunchDarkly is primarily a feature management and flagging platform, while Optimizely is positioned as a digital experimentation suite.
Primary use cases LaunchDarkly is used to control feature releases and progressive rollouts, whereas Optimizely is used to run structured A/B and multivariate experiments.	Feature flag management, progressive rollouts, feature experiments, targeted releases LaunchDarkly is used to control feature releases and progressive rollouts, whereas Optimizely is used to run structured A/B and multivariate experiments.	A/B testing, multivariate testing, content and personalization experiments, digital experience optimization LaunchDarkly is used to control feature releases and progressive rollouts, whereas Optimizely is used to run structured A/B and multivariate experiments.	LaunchDarkly is used to control feature releases and progressive rollouts, whereas Optimizely is used to run structured A/B and multivariate experiments.
Target business size LaunchDarkly is commonly adopted by engineering-led organizations managing complex deployments, while Optimizely serves marketing and product teams running experimentation programs.	Mid‑market to large enterprises and teams focused on engineering overlays LaunchDarkly is commonly adopted by engineering-led organizations managing complex deployments, while Optimizely serves marketing and product teams running experimentation programs.	Mid‑market to enterprises prioritizing marketing, content, and conversions LaunchDarkly is commonly adopted by engineering-led organizations managing complex deployments, while Optimizely serves marketing and product teams running experimentation programs.	LaunchDarkly is commonly adopted by engineering-led organizations managing complex deployments, while Optimizely serves marketing and product teams running experimentation programs.
Pricing model LaunchDarkly pricing typically scales with seats and environments, while Optimizely pricing reflects experimentation scope and traffic volume.	Tiered plan per service connection or client‑side MAU. Entry as developer tier LaunchDarkly pricing typically scales with seats and environments, while Optimizely pricing reflects experimentation scope and traffic volume.	Modular pricing by product bundle and traffic/feature usage. Custom enterprise pricing is widely used LaunchDarkly pricing typically scales with seats and environments, while Optimizely pricing reflects experimentation scope and traffic volume.	LaunchDarkly pricing typically scales with seats and environments, while Optimizely pricing reflects experimentation scope and traffic volume.
Free plan available LaunchDarkly offers limited entry options for smaller projects, while Optimizely generally operates through paid experimentation plans.	Developer plan available starting at zero cost LaunchDarkly offers limited entry options for smaller projects, while Optimizely generally operates through paid experimentation plans.	Free/entry-level flags in some contexts, but full experimentation features are under the paid plan LaunchDarkly offers limited entry options for smaller projects, while Optimizely generally operates through paid experimentation plans.	LaunchDarkly offers limited entry options for smaller projects, while Optimizely generally operates through paid experimentation plans.
Free trial length LaunchDarkly provides structured evaluation access, whereas Optimizely access usually requires sales engagement.	Trial available according to the plan page LaunchDarkly provides structured evaluation access, whereas Optimizely access usually requires sales engagement.	Trial available depending on package (varies) LaunchDarkly provides structured evaluation access, whereas Optimizely access usually requires sales engagement.	LaunchDarkly provides structured evaluation access, whereas Optimizely access usually requires sales engagement.
Starting price per month LaunchDarkly presents tiered pricing tied to feature management scale, while Optimizely positions itself through enterprise experimentation pricing.	$12 per service connection per month (or $10 per 1K client‑side MAU) for the foundation plan LaunchDarkly presents tiered pricing tied to feature management scale, while Optimizely positions itself through enterprise experimentation pricing.	Not publicly disclosed standard starting price for the full experimentation suite LaunchDarkly presents tiered pricing tied to feature management scale, while Optimizely positions itself through enterprise experimentation pricing.	LaunchDarkly presents tiered pricing tied to feature management scale, while Optimizely positions itself through enterprise experimentation pricing.
Billing frequency LaunchDarkly and Optimizely approach billing frequency from different product perspectives without direct overlap.	Monthly or annual, depending on plan LaunchDarkly and Optimizely approach billing frequency from different product perspectives without direct overlap.	Monthly or annual, depending on the package LaunchDarkly and Optimizely approach billing frequency from different product perspectives without direct overlap.	LaunchDarkly and Optimizely approach billing frequency from different product perspectives without direct overlap.
Contract term required LaunchDarkly and Optimizely approach contract term required from different product perspectives without direct overlap.	Flexible for developer plan and enterprise plans have contract terms LaunchDarkly and Optimizely approach contract term required from different product perspectives without direct overlap.	Enterprise packages involve custom contracts and commitments LaunchDarkly and Optimizely approach contract term required from different product perspectives without direct overlap.	LaunchDarkly and Optimizely approach contract term required from different product perspectives without direct overlap.
Additional or hidden costs LaunchDarkly and Optimizely approach additional or hidden costs from different product perspectives without direct overlap.	Possible per‑MAU or service‑connection scaling costs depending on usage LaunchDarkly and Optimizely approach additional or hidden costs from different product perspectives without direct overlap.	Higher traffic volume or advanced modules increase the cost considerably LaunchDarkly and Optimizely approach additional or hidden costs from different product perspectives without direct overlap.	LaunchDarkly and Optimizely approach additional or hidden costs from different product perspectives without direct overlap.
Types of tests supported Optimizely supports A/B, multivariate, and split URL testing, while LaunchDarkly focuses on feature toggles rather than formal experimentation.	Feature toggles progressive rollouts, controlled experiments, back-end, and front‑end flag‑based experiments Optimizely supports A/B, multivariate, and split URL testing, while LaunchDarkly focuses on feature toggles rather than formal experimentation.	A/B tests, multivariate tests, feature flag experiments, content experiments, and personalization workflows Optimizely supports A/B, multivariate, and split URL testing, while LaunchDarkly focuses on feature toggles rather than formal experimentation.	Optimizely supports A/B, multivariate, and split URL testing, while LaunchDarkly focuses on feature toggles rather than formal experimentation.
Client-side testing support Optimizely enables client-side experimentation for web experiences, whereas LaunchDarkly client-side SDKs are used for feature control rather than test variations.	Full support via client‑side SDKs (web and mobile) Optimizely enables client-side experimentation for web experiences, whereas LaunchDarkly client-side SDKs are used for feature control rather than test variations.	Supported via JavaScript snippets and visual editors Optimizely enables client-side experimentation for web experiences, whereas LaunchDarkly client-side SDKs are used for feature control rather than test variations.	Optimizely enables client-side experimentation for web experiences, whereas LaunchDarkly client-side SDKs are used for feature control rather than test variations.
Server-side testing support Both LaunchDarkly and Optimizely support server-side implementations, though LaunchDarkly emphasizes feature control while Optimizely emphasizes experimentation logic.	Supported through server‑side SDKs, enabling backend and infrastructure experiments Both LaunchDarkly and Optimizely support server-side implementations, though LaunchDarkly emphasizes feature control while Optimizely emphasizes experimentation logic.	Supported through backend integrations (Full Stack / Feature Experimentation products) Both LaunchDarkly and Optimizely support server-side implementations, though LaunchDarkly emphasizes feature control while Optimizely emphasizes experimentation logic.	Both LaunchDarkly and Optimizely support server-side implementations, though LaunchDarkly emphasizes feature control while Optimizely emphasizes experimentation logic.
Feature flagging support LaunchDarkly centers its platform around advanced feature flag management, while Optimizely includes flagging primarily as part of its experimentation workflows.	Core strength with robust flag management, including rollouts and instant toggles LaunchDarkly centers its platform around advanced feature flag management, while Optimizely includes flagging primarily as part of its experimentation workflows.	Available as part of feature experimentation and rollout tools LaunchDarkly centers its platform around advanced feature flag management, while Optimizely includes flagging primarily as part of its experimentation workflows.	LaunchDarkly centers its platform around advanced feature flag management, while Optimizely includes flagging primarily as part of its experimentation workflows.
Traffic allocation methods LaunchDarkly and Optimizely approach traffic allocation methods from different product perspectives without direct overlap.	Percentage rollout, gradual exposure, custom targeting segments LaunchDarkly and Optimizely approach traffic allocation methods from different product perspectives without direct overlap.	Percentage splits the multi‑armed bandit traffic allocation option in some experimentation plans LaunchDarkly and Optimizely approach traffic allocation methods from different product perspectives without direct overlap.	LaunchDarkly and Optimizely approach traffic allocation methods from different product perspectives without direct overlap.
Targeting and segmentation options LaunchDarkly targets feature exposure by user attributes and environments, whereas Optimizely applies segmentation directly to experiment audiences.	Granular targeting based on custom context attributes, geolocation, and user properties LaunchDarkly targets feature exposure by user attributes and environments, whereas Optimizely applies segmentation directly to experiment audiences.	Targeting through rulesets, behavioral and demographic criteria, and segmentation through audience definitions LaunchDarkly targets feature exposure by user attributes and environments, whereas Optimizely applies segmentation directly to experiment audiences.	LaunchDarkly targets feature exposure by user attributes and environments, whereas Optimizely applies segmentation directly to experiment audiences.
Personalization rules engine Optimizely personalizes user experiences within experiments, while LaunchDarkly controls which features users see rather than dynamically personalizing content.	Support for custom targeting to personalize feature exposure per audience Optimizely personalizes user experiences within experiments, while LaunchDarkly controls which features users see rather than dynamically personalizing content.	Personalization and targeted content capabilities are integrated into a broader website Optimizely personalizes user experiences within experiments, while LaunchDarkly controls which features users see rather than dynamically personalizing content.	Optimizely personalizes user experiences within experiments, while LaunchDarkly controls which features users see rather than dynamically personalizing content.
Recommendation engine available LaunchDarkly and Optimizely approach recommendation engine available from different product perspectives without direct overlap.	Not core focus. Websites emphasize flags and rollouts over content recommendations LaunchDarkly and Optimizely approach recommendation engine available from different product perspectives without direct overlap.	Websites support experimentation and personalization, but are not marketed as a recommendation engine core LaunchDarkly and Optimizely approach recommendation engine available from different product perspectives without direct overlap.	LaunchDarkly and Optimizely approach recommendation engine available from different product perspectives without direct overlap.
Number of concurrent experiments allowed LaunchDarkly and Optimizely approach number of concurrent experiments allowed from different product perspectives without direct overlap.	Scalable with plan and resources and supports multiple feature flags and experiments in parallel LaunchDarkly and Optimizely approach number of concurrent experiments allowed from different product perspectives without direct overlap.	Supports multiple concurrent experiments per flag or page, depending on the plan LaunchDarkly and Optimizely approach number of concurrent experiments allowed from different product perspectives without direct overlap.	LaunchDarkly and Optimizely approach number of concurrent experiments allowed from different product perspectives without direct overlap.
Built-in reporting depth Optimizely delivers experiment performance reporting, whereas LaunchDarkly reports on feature adoption and rollout metrics.	Basic feature usage metrics and analytics for flag performance and rollout outcomes Optimizely delivers experiment performance reporting, whereas LaunchDarkly reports on feature adoption and rollout metrics.	Stronger analytics, experimentation, reporting, conversion tracking integration with analytics tools Optimizely delivers experiment performance reporting, whereas LaunchDarkly reports on feature adoption and rollout metrics.	Optimizely delivers experiment performance reporting, whereas LaunchDarkly reports on feature adoption and rollout metrics.
Funnel and journey analysis LaunchDarkly and Optimizely approach funnel and journey analysis from different product perspectives without direct overlap.	Not a core component and primary focus remains flag evaluation and rollout control LaunchDarkly and Optimizely approach funnel and journey analysis from different product perspectives without direct overlap.	Available via experimentation, analytics, and integration with analytics websites for user journey tracking LaunchDarkly and Optimizely approach funnel and journey analysis from different product perspectives without direct overlap.	LaunchDarkly and Optimizely approach funnel and journey analysis from different product perspectives without direct overlap.
Revenue attribution capabilities LaunchDarkly and Optimizely approach revenue attribution capabilities from different product perspectives without direct overlap.	Limited and flag analytics may track feature usage, but not full ecommerce attribution out of the box LaunchDarkly and Optimizely approach revenue attribution capabilities from different product perspectives without direct overlap.	Designed for conversion optimization. Integrates with analytics tools for revenue and conversion attribution LaunchDarkly and Optimizely approach revenue attribution capabilities from different product perspectives without direct overlap.	LaunchDarkly and Optimizely approach revenue attribution capabilities from different product perspectives without direct overlap.
Session replay available Session replay is not a defining capability for either LaunchDarkly or Optimizely in this comparison.	Not a core capability Session replay is not a defining capability for either LaunchDarkly or Optimizely in this comparison.	Not a core capability in the core experimentation suite Session replay is not a defining capability for either LaunchDarkly or Optimizely in this comparison.	Session replay is not a defining capability for either LaunchDarkly or Optimizely in this comparison.
Heatmaps available Heatmaps are not central to either LaunchDarkly or Optimizely, as both prioritize experimentation or feature management.	No built‑in heatmap focus Heatmaps are not central to either LaunchDarkly or Optimizely, as both prioritize experimentation or feature management.	Not core focus unless using full analytics integrations Heatmaps are not central to either LaunchDarkly or Optimizely, as both prioritize experimentation or feature management.	Heatmaps are not central to either LaunchDarkly or Optimizely, as both prioritize experimentation or feature management.
Form analytics available Optimizely evaluates form changes within experiments, while LaunchDarkly does not focus on form analytics.	Not primary functionality Optimizely evaluates form changes within experiments, while LaunchDarkly does not focus on form analytics.	Not primary functionality Optimizely evaluates form changes within experiments, while LaunchDarkly does not focus on form analytics.	Optimizely evaluates form changes within experiments, while LaunchDarkly does not focus on form analytics.
Statistical approach Optimizely applies statistical validation to experiment outcomes, whereas LaunchDarkly focuses on feature rollout metrics rather than hypothesis testing.	Flag‑based rollouts experimentation and not heavy statistical testing by default Optimizely applies statistical validation to experiment outcomes, whereas LaunchDarkly focuses on feature rollout metrics rather than hypothesis testing.	Frequent A/B or multivariate statistical testing engine built in Optimizely applies statistical validation to experiment outcomes, whereas LaunchDarkly focuses on feature rollout metrics rather than hypothesis testing.	Optimizely applies statistical validation to experiment outcomes, whereas LaunchDarkly focuses on feature rollout metrics rather than hypothesis testing.
Sample size calculator available Optimizely provides planning tools for experiment sizing, while LaunchDarkly does not center its platform around statistical experiment design.	Not prominent in basic feature‑flag use cases Optimizely provides planning tools for experiment sizing, while LaunchDarkly does not center its platform around statistical experiment design.	Usually included in test setup flows for experiments Optimizely provides planning tools for experiment sizing, while LaunchDarkly does not center its platform around statistical experiment design.	Optimizely provides planning tools for experiment sizing, while LaunchDarkly does not center its platform around statistical experiment design.
Experiment duration estimator LaunchDarkly and Optimizely approach experiment duration estimator from different product perspectives without direct overlap.	Not core functionality LaunchDarkly and Optimizely approach experiment duration estimator from different product perspectives without direct overlap.	Available through experiment planning tools on the websites LaunchDarkly and Optimizely approach experiment duration estimator from different product perspectives without direct overlap.	LaunchDarkly and Optimizely approach experiment duration estimator from different product perspectives without direct overlap.
Automatic stopping rules Optimizely includes controls for managing experiment duration, whereas LaunchDarkly manages feature exposure rather than test stopping logic.	Not a core focus Optimizely includes controls for managing experiment duration, whereas LaunchDarkly manages feature exposure rather than test stopping logic.	Advanced experimentation settings allow dynamic rollouts and automated decisions in some plans Optimizely includes controls for managing experiment duration, whereas LaunchDarkly manages feature exposure rather than test stopping logic.	Optimizely includes controls for managing experiment duration, whereas LaunchDarkly manages feature exposure rather than test stopping logic.
Support for holdout groups LaunchDarkly and Optimizely approach support for holdout groups from different product perspectives without direct overlap.	Supported by flag and rollout logic via custom targeting and segmentation LaunchDarkly and Optimizely approach support for holdout groups from different product perspectives without direct overlap.	Supported via experimentation configuration and audience definition LaunchDarkly and Optimizely approach support for holdout groups from different product perspectives without direct overlap.	LaunchDarkly and Optimizely approach support for holdout groups from different product perspectives without direct overlap.
CMS integrations Optimizely integrates with CMS environments for experimentation, while LaunchDarkly integrates through SDKs for feature control.	Not a core offering. Integrations depend on the implementation environment Optimizely integrates with CMS environments for experimentation, while LaunchDarkly integrates through SDKs for feature control.	Integrates with content management and digital experience infrastructure for full websites Optimizely integrates with CMS environments for experimentation, while LaunchDarkly integrates through SDKs for feature control.	Optimizely integrates with CMS environments for experimentation, while LaunchDarkly integrates through SDKs for feature control.
E-commerce websites integrations LaunchDarkly and Optimizely approach e-commerce websites integrations from different product perspectives without direct overlap.	Implementation‑specific and flag websites agnostic LaunchDarkly and Optimizely approach e-commerce websites integrations from different product perspectives without direct overlap.	Supported via experimentation and analytics integrations for commerce use cases LaunchDarkly and Optimizely approach e-commerce websites integrations from different product perspectives without direct overlap.	LaunchDarkly and Optimizely approach e-commerce websites integrations from different product perspectives without direct overlap.
Analytics integrations Optimizely connects experiment results to analytics platforms, whereas LaunchDarkly integrates rollout data into engineering systems.	Supports external analytics and data‑tracking integrations via SDKs and events Optimizely connects experiment results to analytics platforms, whereas LaunchDarkly integrates rollout data into engineering systems.	Strong integration support with analytics websites and data export Optimizely connects experiment results to analytics platforms, whereas LaunchDarkly integrates rollout data into engineering systems.	Optimizely connects experiment results to analytics platforms, whereas LaunchDarkly integrates rollout data into engineering systems.
CDP or data warehouse integrations LaunchDarkly and Optimizely approach cdp or data warehouse integrations from different product perspectives without direct overlap.	Depends on the user‑side data implementation and is possible via custom integrations LaunchDarkly and Optimizely approach cdp or data warehouse integrations from different product perspectives without direct overlap.	Supported through data export or integration connectors when configured LaunchDarkly and Optimizely approach cdp or data warehouse integrations from different product perspectives without direct overlap.	LaunchDarkly and Optimizely approach cdp or data warehouse integrations from different product perspectives without direct overlap.
Marketing automation or CRM integrations Optimizely integrates experimentation insights into marketing ecosystems, while LaunchDarkly integrations are typically aligned with engineering workflows.	Not core role and integration depends on engineering setup and custom endpoints Optimizely integrates experimentation insights into marketing ecosystems, while LaunchDarkly integrations are typically aligned with engineering workflows.	Possible via integrations and data pipelines when configured through the websites Optimizely integrates experimentation insights into marketing ecosystems, while LaunchDarkly integrations are typically aligned with engineering workflows.	Optimizely integrates experimentation insights into marketing ecosystems, while LaunchDarkly integrations are typically aligned with engineering workflows.
Tag manager integrations LaunchDarkly and Optimizely approach tag manager integrations from different product perspectives without direct overlap.	Compatible via SDK and snippet deployments through consent or data‑tag management strategies LaunchDarkly and Optimizely approach tag manager integrations from different product perspectives without direct overlap.	Deployable through standard tracking/tag management workflows via website scripts LaunchDarkly and Optimizely approach tag manager integrations from different product perspectives without direct overlap.	LaunchDarkly and Optimizely approach tag manager integrations from different product perspectives without direct overlap.
API available Both LaunchDarkly and Optimizely provide APIs to extend their respective feature management and experimentation capabilities.	RESTful APIs and multiple SDKs across languages supported by the websites Both LaunchDarkly and Optimizely provide APIs to extend their respective feature management and experimentation capabilities.	REST APIs and SDKs across languages for full experimentation and flag management Both LaunchDarkly and Optimizely provide APIs to extend their respective feature management and experimentation capabilities.	Both LaunchDarkly and Optimizely provide APIs to extend their respective feature management and experimentation capabilities.