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    Recombee

    AI-powered, domain-agnostic recommender system providing real-time personalization for e-commerce, media, and news sectors.

    CZCzech Republic·Founded 2014
    Headquarters
    🇨🇿Czech Republic
    Data Hosting
    Global / multi-region
    Structure
    Independent
    Compliance
    ▣GDPR◆EU HQ▦EU Data

    Recombee key facts

    Last updated
    Legal entity
    Recombee s.r.o.
    Headquarters
    Czech Republic (CZ)
    Ownership
    Independent
    Founded
    2014
    Data hosting
    Global / multi-region
    Infrastructure
    Self-hosted (Private Cloud)
    Open source
    No
    US Cloud Act exposure
    Low (no US parent)
    Replaces
    Amazon Personalize, Algolia Personalization, Dynamic Yield, Salesforce Einstein Personalization, Adobe Target

    About Recombee

    Recombee is a Prague-based technology company that specializes in providing a universal, AI-powered recommender system as a service. Founded by a team of data scientists and engineers from CTU, the platform is designed to be domain-agnostic, serving sectors ranging from e-commerce to streaming services and news media. It allows businesses to integrate sophisticated personalization features into their platforms without the need for extensive in-house machine learning development. The company operates its own global private cloud infrastructure, consisting of nearly a thousand servers. This setup is optimized for high-scale, low-latency data processing, enabling real-time responses to user behavior. Recombee distinguishes itself through its ability to handle complex business logic, such as content aging and category mixing, while maintaining high performance and cost efficiency compared to generic cloud AI solutions.

    Alternative to

    Hosting & Security

    Infrastructure

    Self-hosted (Private Cloud)

    Key Features

    Real-time recommendations
    A/B testing framework
    Scalable algorithms for low-latency
    Business rule support (filtering, boosting)
    Multi-language and multi-country customization
    Diversity control to avoid filter bubbles
    Infinite scroll optimization
    Embedding-based recommendations

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