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Fusion 5.12
    Fusion 5.12

    Managed Fusion

    Lucidworks Managed Fusion is a search and machine learning platform that lets you deploy AI-powered data discovery and search applications in a modern, containerized, cloud-native architecture. It can index billions of documents from any data source and process thousands of queries per second from thousands of concurrent users.

    Managed Fusion integrates with popular machine learning frameworks, making it easy to build and deploy AI-powered applications.

    How is Managed Fusion different from Fusion?

    Lucidworks performs deployments, upgrades, and high availability for your Managed Fusion instance. Additionally, some features and capabilities may be available in Managed Fusion only or the other way around.

    For more information on the key differences between Managed Fusion and Fusion, see Managed Fusion versus Fusion.



    Managed Fusion offers dozens of connectors so you can access your data from a large number of sources, including Amazon AWS S3, SharePoint, or almost any website. Your data moves through the indexing Data Flow, where it is transformed to meet the specific needs of your use case.

    To learn more about Managed Fusion connectors, see Connectors.


    Signals are recorded user events that provide insights on user behavior. Almost any user event can be captured as a signal, including when a user clicks a search result, adds an item to their cart, or makes a purchase. Managed Fusion uses signals to improve search result relevancy and deliver the right results to the right users.

    For more information on signals, see Signals.


    Rules are manually created formulas for rewriting queries. This is the most versatile strategy for creating custom query rewrites. It supports a variety of conditions and actions to address a wide range of use cases.

    Use rules to boost, bury, or block results to customize your search experience, or take it further with other rule actions.

    For more information on rules, see Business rules.


    Create personalized recommendations for users based on signals data, even if they have not performed a search. Use these recommendations with search-driven assets to drive users to the most relevant content.

    • Items-for-item. Customers shopping for bicycles may be interested in more items, such as a helmet and bike lock. Use items-for-items recommendations to surface those items for them.

    • Items-for-query. Sometimes, semantic relevance is not the same as user relevance. Items-for-query recommendations show items commonly associated with a query, based on user behavior.

    • Items-for-user. Winter clothing needs are different for customers in Arizona versus Alaska. Items-for-user recommendations help each customer find what works best for them.

    • Queries-for-query. Help users who are not finding the right words. Queries-for-query recommendations show queries that are related to the current query.

    • Trending items and queries. Show items that have been popular lately, so customers can see the latest and greatest.

    • Users-for-item. Find which users have interacted with specific items, and reach out with your marketing campaign.

    For more information on recommendations, see Recommendations.

    Search curation

    Predictive Merchandiser and Experience Optimizer are AI-powered tools that provide insights, customer recommendations, and search optimization.

    Predictive Merchandiser is designed for product discovery, such as ecommerce. Experience Optimizer is designed for knowledge management. For example, a site providing news, information, or documentation.

    For more information on these tools, see Curate your search experience.


    Managed Fusion builds upon trusted technology that makes it reliable, secure, fast, and effective. Major components of its technology stack include Kubernetes, Solr, Spark, and ZooKeeper.


    Kubernetes is a container orchestration service used for deploying, scaling, and managing distributed applications. Managed Fusion uses Google Kubernetes Engine (GKE) and benefits from Google’s global infrastructure, security, and support.

    Apache Solr

    Solr is a highly reliable and scalable search platform. Managed Fusion uses Solr to index and query billions of documents and serve millions of users.

    Apache Spark

    Spark is an analytics engine used by Managed Fusion for large-scale data processing. This includes processing millions of signals and generating recommendations.

    Apache ZooKeeper

    ZooKeeper is a distributed configuration service that Managed Fusion uses to configure your components and keep them consistent within your deployment.

    Additional information

    About this documentation

    Lucidworks' documentation is written to inform and instruct all users on product features and capabilities. If you want to leave feedback for the Documentation team, use the feedback widget or send an email to

    Document types

    Lucidworks documentation is categorized by document type, which is indicated under the title and on each search result. The following are descriptions of each document type:

    • Concept: A concept article provides a high-level, less technical overview of a topic.

    • Reference: A reference article provides technical details, such as configuration specifications.

    • How-to: A how-to article provides step-by-step instructions for completing a task.

    • Release note: Release notes describe the latest developments in Managed Fusion.


    Lucidworks provides technical support to customers 24 hours a day, 7 days a week, with unlimited incidents. You can open a support case at any time. Before doing so, complete the instructions found in the Technical Support policy.


    LucidAcademy Fusion Foundations Training modules

    Lucidworks offers free training to help you get started with Managed Fusion. Visit the LucidAcademy to learn how Managed Fusion indexes your data and delivers highly relevant search results to thousands of concurrent users.