StackShareStackShare
Follow on
StackShare

Discover and share technology stacks from companies around the world.

Follow on

© 2025 StackShare. All rights reserved.

Product

  • Stacks
  • Tools
  • Feed

Company

  • About
  • Contact

Legal

  • Privacy Policy
  • Terms of Service
  1. Stackups
  2. DevOps
  3. Build Automation
  4. Feature Flags Management
  5. Optimizely Rollouts vs Split

Optimizely Rollouts vs Split

OverviewComparisonAlternatives

Overview

Split
Split
Stacks119
Followers121
Votes2
Optimizely Rollouts
Optimizely Rollouts
Stacks4
Followers28
Votes0

Optimizely Rollouts vs Split: What are the differences?

Introduction

Optimizely Rollouts and Split are two popular feature flagging and experimentation platforms that can be used to manage feature releases and A/B testing in software development. While they both serve similar purposes, there are some key differences between the two.

  1. Feature Flagging Flexibility: Optimizely Rollouts offers more flexible feature flagging capabilities compared to Split. With Optimizely Rollouts, you can create simple feature flags as well as more advanced variations like percentage rollouts and user segmentation. Split, on the other hand, primarily focuses on feature flagging and doesn't provide as many options for complex rollout strategies.

  2. Experimentation Capabilities: Split provides more extensive experimentation capabilities compared to Optimizely Rollouts. Split allows you to conduct various types of experiments such as A/B tests, multivariate tests, and canary releases. It offers advanced statistical analysis and personalized targeting options to help you make data-driven decisions. Optimizely Rollouts, on the other hand, is more focused on feature flagging and doesn't provide the same level of experimentation features.

  3. Integration with Analytics Tools: Optimizely Rollouts integrates seamlessly with Optimizely's broader experimentation platform, which includes advanced analytics and reporting features. If you are already using Optimizely for other experimentation purposes, Rollouts can fit well into your existing workflow. Split, on the other hand, provides integrations with various analytics and monitoring tools, giving you the flexibility to choose the analytics solution that best suits your needs.

  4. Client SDK Support: Split offers a wide range of client SDKs for different programming languages, including JavaScript, Python, Java, Ruby, and many more. This allows you to easily implement feature flags and experiments in your codebase regardless of the language you are using. Optimizely Rollouts also provides SDKs for popular programming languages, but they may not have the same level of language coverage as Split.

  5. Ease of Use: Optimizely Rollouts is known for its user-friendly and intuitive interface, making it easy for non-technical team members to create and manage feature flags. Split, on the other hand, has a more developer-centric approach and may require some technical expertise to fully utilize its capabilities.

  6. Pricing Model: Optimizely Rollouts follows a free-tier model, allowing you to use its feature flagging capabilities without any cost. However, if you want to access additional experimentation features and advanced analytics, you need to upgrade to Optimizely's paid plans. Split, on the other hand, offers both free and paid plans, with different tiers based on your organization's needs and usage.

In Summary, Optimizely Rollouts provides flexible feature flagging capabilities with seamless integration with Optimizely's broader experimentation platform, while Split offers extensive experimentation features with a wide range of client SDK support. The choice between the two depends on your specific requirements and preferences.

Share your Stack

Help developers discover the tools you use. Get visibility for your team's tech choices and contribute to the community's knowledge.

View Docs
CLI (Node.js)
or
Manual

Detailed Comparison

Split
Split
Optimizely Rollouts
Optimizely Rollouts

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.

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.

Targeted Feature Release - Easily target any feature, anywhere in the stack, to the right users based on any attribute you have access to, from demographic data to in-the-browser metrics; Foster a culture of continuous improvement: Analyze the impact of every feature on hundreds of business, product, and operational metrics in real time; Rigorous statistical analysis: Split’s statistics engine provides causal analysis and guards against misleading results; Easy-to-use Web Console - Split's UI gives anyone on the team the power to target feature releases, ramp up features to your customers, and instantly kill problem features
Unlimited feature flags; Staged rollouts; REST API; Roll out everywhere; Change history; Audience targeting; Cloud dashboard; Advanced user permissions; Enterprise security and compliance
Statistics
Stacks
119
Stacks
4
Followers
121
Followers
28
Votes
2
Votes
0
Pros & Cons
Pros
  • 1
    Affordable
  • 1
    Fast
No community feedback yet
Integrations
Datadog
Datadog
Librato
Librato
Slim Lang
Slim Lang
HipChat
HipChat
Sumo Logic
Sumo Logic
Rollbar
Rollbar
Papertrail
Papertrail
AppDynamics
AppDynamics
New Relic
New Relic
Slack
Slack
React
React
Python
Python
C#
C#
Jira
Jira
Node.js
Node.js
Java
Java
JavaScript
JavaScript
Golang
Golang
Ruby
Ruby
PHP
PHP

What are some alternatives to Split, Optimizely Rollouts?

ConfigCat

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

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

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

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

Flagr

Flagr

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

Rollout

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

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.

Optimizely Full Stack

Optimizely Full Stack

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.

Bullet Train

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.

FF4J

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.

Related Comparisons

GitHub
Bitbucket

Bitbucket vs GitHub vs GitLab

GitHub
Bitbucket

AWS CodeCommit vs Bitbucket vs GitHub

Kubernetes
Rancher

Docker Swarm vs Kubernetes vs Rancher

gulp
Grunt

Grunt vs Webpack vs gulp

Graphite
Kibana

Grafana vs Graphite vs Kibana