Optimizely Full Stack vs Optimizely Rollouts

Overview

Optimizely Rollouts

Stacks4

Followers28

Votes0

Optimizely Full Stack

Stacks7

Followers14

Votes0

Optimizely Full Stack vs Optimizely Rollouts: What are the differences?

Feature Set: Optimizely Full Stack is designed for product development teams to experiment with features while Optimizely Rollouts is focused on feature flagging for software development teams. Full Stack offers a wide range of experimentation capabilities such as A/B testing, multivariate testing, and feature rollouts, while Rollouts primarily focuses on feature flag management and targeting.
Integration: Full Stack integrates with a variety of third-party tools for analytics, data management, and customer engagement, allowing for a more comprehensive integration with existing systems. On the other hand, Rollouts is designed to easily integrate with popular version control systems like GitHub and Bitbucket to streamline the feature flagging process.
Target Audience: Full Stack is targeted towards product managers, marketers, and developers who are looking to optimize their product through experimentation and data-driven decisions. Rollouts, on the other hand, is tailored for software developers and DevOps teams who need a simple and efficient way to manage feature flags in their codebase.
Customization Options: Full Stack provides advanced customization options for experiments and feature rollouts, allowing users to define complex targeting rules and experiment variations. Rollouts, on the other hand, focuses on simplicity and ease of use, offering predefined feature flag templates and simple targeting options for faster implementation.
Cost Structure: Full Stack follows a usage-based pricing model where customers are charged based on the number of unique visitors or events tracked in their experiments. Rollouts, on the other hand, offers a free tier for smaller teams with limited feature flagging needs, and a paid plan with additional features for larger teams and enterprise users.
Scale and Performance: Full Stack is built to handle high traffic and complex experimentation scenarios, with features like geo-targeting, experiment grouping, and advanced analytics to support large-scale experiments. Rollouts, on the other hand, is optimized for performance and reliability, ensuring that feature flags can be evaluated quickly and efficiently in real-time environments.

In Summary, Optimizely Full Stack and Optimizely Rollouts offer distinct features tailored to different audiences, with Full Stack focusing on experimentation and feature optimization for product teams, while Rollouts simplifies feature flag management for software development teams.

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Detailed Comparison

Optimizely Rollouts	Optimizely Full Stack
It is unlimited free feature flags and rollouts built on an enterprise-grade platform. Manage your features remotely and roll them out gradually to targeted audiences, without re-deploying your code. Connect Rollouts with Jira and invite your product and engineering teams to work together.	It is an experimentation and feature flagging platform for websites, mobile apps, chatbots, APIs, smart devices, and anything else with a network connection. You can deploy code behind feature flags, experiment with A/B tests, and rollout or rollback features immediately. All of this functionality is available with zero performance impact via easy-to-use SDKs.
Unlimited feature flags; Staged rollouts; REST API; Roll out everywhere; Change history; Audience targeting; Cloud dashboard; Advanced user permissions; Enterprise security and compliance	Feature flags; Event tracking; Enterprise-ready security; Account-level experimentation; Open platform; Scalable architecture
Statistics
Stacks 4	Stacks 7
Followers 28	Followers 14
Votes 0	Votes 0
Integrations
React Python C# Jira Node.js Java JavaScript Golang Ruby PHP	Objective-C Python C# Swift Node.js Java JavaScript Ruby PHP

What are some alternatives to Optimizely Rollouts, Optimizely Full Stack?

ConfigCat

Cross-platform feature flag service for Teams. It is a hosted or on-premise service with a web app for feature management, and SDKs for all major programming languages and technologies.

Unleash Hosted

It is a simple feature management system. It gives you great overview of all feature toggles across all your applications. You decide who is exposed to which feature.

LaunchDarkly

Serving over 200 billion feature flags daily to help software teams build better software, faster. LaunchDarkly helps eliminate risk for developers and operations teams from the software development cycle.

Airship

Airship is a modern product flagging framework that gives the right people total control over what your customers see & experience - without deploying code.

Flagr

Open-source Go microservice supports feature flagging, A/B testing, and dynamic configuration. Logs data records and impressions.

Split

Feature flags as a service for data-driven teams: Split automatically tracks changes to key metrics during every feature rollout. Split serves billions of impressions, helping organizations of all sizes to rapidly turn ideas into products.

Rollout

Advanced feature flag solution that lets your dev teams quickly build & deploy applications without compromising on safety. A simple way to define target audiences allows devs & PMs optimize feature releases and customize user experience

Flagsmith

It lets you manage features flags and remote config across web, mobile and server side applications. Use our hosted API, deploy to your own private cloud, or run on-premises.

FF4J

It is an implementation of Feature Toggle pattern : Enable and disable features or your applications at runtime thanks to dedicated web console, REST API, JMX or even CLI. It handle also properties and provide generic interfaces.

Bullet Train

Manage feature flags across web, mobile and server side applications. Deliver true Continuous Integration. Get builds out faster. Control who has access to new features.

Related Comparisons

Optimizely Full Stack vs Optimizely Rollouts: What are the differences?

Feature Set: Optimizely Full Stack is designed for product development teams to experiment with features while Optimizely Rollouts is focused on feature flagging for software development teams. Full Stack offers a wide range of experimentation capabilities such as A/B testing, multivariate testing, and feature rollouts, while Rollouts primarily focuses on feature flag management and targeting.
Integration: Full Stack integrates with a variety of third-party tools for analytics, data management, and customer engagement, allowing for a more comprehensive integration with existing systems. On the other hand, Rollouts is designed to easily integrate with popular version control systems like GitHub and Bitbucket to streamline the feature flagging process.
Target Audience: Full Stack is targeted towards product managers, marketers, and developers who are looking to optimize their product through experimentation and data-driven decisions. Rollouts, on the other hand, is tailored for software developers and DevOps teams who need a simple and efficient way to manage feature flags in their codebase.
Customization Options: Full Stack provides advanced customization options for experiments and feature rollouts, allowing users to define complex targeting rules and experiment variations. Rollouts, on the other hand, focuses on simplicity and ease of use, offering predefined feature flag templates and simple targeting options for faster implementation.
Cost Structure: Full Stack follows a usage-based pricing model where customers are charged based on the number of unique visitors or events tracked in their experiments. Rollouts, on the other hand, offers a free tier for smaller teams with limited feature flagging needs, and a paid plan with additional features for larger teams and enterprise users.
Scale and Performance: Full Stack is built to handle high traffic and complex experimentation scenarios, with features like geo-targeting, experiment grouping, and advanced analytics to support large-scale experiments. Rollouts, on the other hand, is optimized for performance and reliability, ensuring that feature flags can be evaluated quickly and efficiently in real-time environments.

Optimizely Full Stack vs Optimizely Rollouts

Overview