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    Split: Feature Management and Experimentation

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    Switch on the Split Feature Data Platform and deliver software features that matter, fast.
    4.3

    Overview

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    Why Choose Split?

    In a world where product development teams are pressured to do more with less, Split's Feature Data Platform gives you the confidence to move fast without breaking things.

    Set up feature flags and safely deploy to production, controlling who sees which features and when. Connect every flag to contextual data, so you know if your features are making things better or worse, and act without hesitation. Split's Amazon S3 integration makes it easy to bring high-volume customer data and feature flags together - enabling users to seamlessly calculate key metrics, increase the reliability of each release, and run experiments while creating customer feedback loops. Split is a feature management and experimentation partner that takes the extra step with experts to support you, offering online courses to help you learn as you go, and providing a developer-oriented culture that puts our customers at the center.

    Whether you're looking to increase your releases, to decrease your MTTR, or to ignite your dev team without burning them out - Change the way the work gets done with Split.

    Common use cases include continuous integration/continuous delivery, targeted rollouts, dark launches, canary releases, A/B testing, and ongoing experimentation.

    For custom pricing, EULA, or a private contract, please contact aws-marketplace@split.io .

    Highlights

    • A unified feature flagging and experimentation platform enabling product and engineering teams to reduce cycle times, mitigate release risk, and maximize business impact.
    • Split provides enterprises with the speed, control, and data-driven insights they need to get ahead of the competition and get the right features in front of customers with zero downtime.
    • Split's Feature Data Platform serves feature flags to more than 6 billion devices worldwide

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    Pricing

    Split: Feature Management and Experimentation

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    Pricing is based on the duration and terms of your contract with the vendor. This entitles you to a specified quantity of use for the contract duration. If you choose not to renew or replace your contract before it ends, access to these entitlements will expire.
    Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator  to estimate your infrastructure costs.

    12-month contract (1)

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    Dimension
    Description
    Cost/12 months
    Business
    Starting at 10 seats and 50,000 Monthly Tracked Keys
    $7,200.00

    Vendor refund policy

    All fees are non-cancellable and non-refundable except as required by law.

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    Usage information

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    Delivery details

    Software as a Service (SaaS)

    SaaS delivers cloud-based software applications directly to customers over the internet. You can access these applications through a subscription model. You will pay recurring monthly usage fees through your AWS bill, while AWS handles deployment and infrastructure management, ensuring scalability, reliability, and seamless integration with other AWS services.

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    AWS infrastructure support

    AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.

    Product comparison

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    Updated weekly

    Accolades

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    Top
    50
    In Agile Lifecycle Management
    Top
    10
    In Business Intelligence & Advanced Analytics, Generative AI, Continuous Integration and Continuous Delivery
    Top
    100
    In Testing

    Customer reviews

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    Sentiment is AI generated from actual customer reviews on AWS and G2
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    Overview

     Info
    AI generated from product descriptions
    Feature Flagging and Deployment Control
    Ability to set up feature flags and safely deploy to production, controlling which users see which features and when with zero downtime deployment capability.
    Experimentation and A/B Testing
    Support for A/B testing, canary releases, dark launches, and targeted rollouts to enable data-driven experimentation and feature validation.
    Contextual Data Integration
    Connection of feature flags to contextual customer data through Amazon S3 integration to enable seamless metric calculation and feature impact analysis.
    Release Risk Mitigation
    Reduction of cycle times and release risk through continuous integration/continuous delivery workflows and mean time to recovery optimization.
    High-Volume Data Processing
    Capability to serve feature flags to high-volume distributed systems, supporting more than 6 billion devices with reliable feature delivery at scale.
    Feature Flag Management
    Industry-leading feature flags enabling runtime control for code and application behavior management
    Progressive Deployment and Rollout
    Progressive delivery capabilities with automatic rollback functionality for controlled software releases
    Real-time Observability and Monitoring
    Real-time observability and monitoring of production behavior for AI-generated code and agent performance
    AI Agent Control and Governance
    Runtime control for AI agents including prompt and model configuration, behavior monitoring, and automatic corrective actions without redeployment
    Production Experimentation and Testing
    Production testing capabilities with continuous experimentation and measurement of variations to optimize outcomes
    Feature Flag Management
    Open-source feature flag platform enabling controlled feature releases and rollouts to manage deployment risk
    Data Governance and Compliance Controls
    Market-leading data governance, security, and compliance controls designed for enterprise-grade requirements including FedRamp and air-gapped deployment scenarios
    Deployment Flexibility
    Support for multiple deployment options including cloud-hosted private instances and self-hosted solutions
    Developer Tools and Workflow Integration
    Developer-focused tools for testing and deploying new features to production environments with streamlined release process capabilities

    Contract

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    Standard contract
    No
    No
    No

    Customer reviews

    Ratings and reviews

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    4.3
    2 ratings
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    2 AWS reviews
    Ankit-Mishra

    Dynamic configuration has simplified multi-environment rollout and now controls deployments on demand

    Reviewed on Jun 05, 2026
    Review from a verified AWS customer

    What is our primary use case?

    The main objective of our project was to have a dynamic config with Split  without doing a deployment. Split  helped us by allowing us to call the Split APIs and fetch the configs on the fly.

    What is most valuable?

    Working with dynamic configs is very good and was very easy because we have an environment where we can set up staging, prod, and dev. We can have multiple checks based on which field and which checks we want to apply to a particular config. All these options make us more flexible to change or update the config on a dynamic basis.

    The dynamic config updates with Split work without requiring any deployment of the service or software. The APIs we used were very fast and we get the response within 10 milliseconds or so. It is very fast to use an API call for all the config fetching from Split.

    Split offers multiple environments, and we have so many fields and checks which we can apply. We can specify countries and apply different checks such as if, else, not in, and in. This provides more flexibility in applying the config based on the checks.

    We first try in a dev environment while working on a project. Then we do all the experiments with it and finalize it and move it to the staging environment. There we also do a final round of checks to ensure that all the configs we have defined are working as intended. Then finally  we go to production, and there also if we want to have any changes, we can do it on the fly.

    The environment feature allows us to approve from dev to staging and then finalize the same thing to prod. The multiple checks are really important features.

    The environment feature and the workflow have become very smooth because for a small change in the config, we do not have to deploy the software again. We only have to go and change the config in Split. This gives us more flexibility and a dynamic way to handle and manage the prod config. For example, if I am launching something in India first and then want to launch it for other countries, I only have to go to Split and update the config.

    What needs improvement?

    The UI of split.io can be improved.

    For how long have I used the solution?

    I have used it while I was working on a project for approximately two or three months.

    What other advice do I have?

    Split is a great way to handle the config based on checks and in a dynamic way if you want to control your software without doing any deployment. Split can help you there. Split is a great way to dynamically handle config without requiring the deployment of the services or software. This is what I primarily associate with Split. I would rate this review a 9 out of 10.

    AmitGupta1

    Experimentation has boosted conversion rates and guides faster, safer feature decisions

    Reviewed on Jun 03, 2026
    Review from a verified AWS customer

    What is our primary use case?

    The main use case for Split  would be the experimentation and analytics part. For example, we have two different kinds of components or two different types of pages that we need to test to see which page performs better in terms of users clicking on it via a CTA  button. We track them via Mixpanel , making Split  an actual use case for determining which variant would be successful in terms of CVR increase.

    Recently, we have used Split for an experiment that determined if the user can provide his PII details, such as first name, last name, DOB, etc. Variant A involves entering personal details, while variant B involves entering the employee name to check eligibility. We aimed to find out which would increase the CVR: entering personal details or entering employee details, and we ran this experiment using Split.

    What is most valuable?

    The best features Split offers include A/B experimentation itself, multivariant experiments, and a limit exposure feature that allows us to manipulate the traffic going through the split. We can decide to send only 10% of the traffic or 100%, depending on our limit exposure design. Additionally, we have a tracking mechanism and live trail monitoring, making these tools really good.

    The limit exposure feature in Split decides if the experiment is healthy or not. For example, we enroll the experiment for 5% of users and check the experiment's healthiness from that. We verify if the on variant is working properly and if the off variant is functioning well to ensure that extra users are not affected if something goes wrong. That is the main feature of limit exposure.

    I would say Split has impacted us a lot because we use it for gating new features while going into production, ensuring that those features are not rolled out prematurely. Overall, our organization is positive about using Split.

    What needs improvement?

    Split can definitely be improved by introducing something like a library, as we are currently making external API calls that contribute to latency and extra costs for our services. A library would have been better than relying on HTTP calls to improve the speed of Split.

    I chose 8 out of 10 for Split because, firstly, it can definitely improve itself by removing the HTTP call and using a library, which would really help our team. Additionally, we tend to move towards other issues related to Split due to flickering issues, which we observed because of latency.

    For how long have I used the solution?

    I have been using Split for about two years now.

    What do I think about the stability of the solution?

    Split is stable.

    What do I think about the scalability of the solution?

    Split's scalability is quite good, as it can scale up easily, and the exposure is also fine with a nearly 50-50 ratio. So, it's scalable.

    How are customer service and support?

    We haven't encountered any significant issues with customer support, but we did have one or two minor issues, and they were pretty reachable.

    What other advice do I have?

    Split itself has many properties like attributes and split impressions, which are really good tools for Mixpanel  tracking of the experimentation. I would suggest that this is a really good product for the use case of experimentation.

    Split has increased some of our CVR; for example, in a recent pop-up model experiment, our conversion was around 15%. Drastically, once we started the experiment, the on-variant won by increasing the CVR up to 35%. This makes it a really good tool for determining which particular feature would deliver the expected CVR. It has also reduced the time it takes to determine such outcomes; in approximately two weeks, we decide whether a feature should be rolled out permanently or not. Moreover, it has minimized errors as well.

    I would definitely suggest that others use Split for experimentation purposes, not just for gating features, as that is the only benefit we have experienced from using Split. I gave Split a rating of 8 out of 10.

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