Compare the Top Retrieval-Augmented Generation (RAG) Software that integrates with Kubernetes as of July 2025

This a list of Retrieval-Augmented Generation (RAG) software that integrates with Kubernetes. Use the filters on the left to add additional filters for products that have integrations with Kubernetes. View the products that work with Kubernetes in the table below.

What is Retrieval-Augmented Generation (RAG) Software for Kubernetes?

Retrieval-Augmented Generation (RAG) tools are advanced AI systems that combine information retrieval with text generation to produce more accurate and contextually relevant outputs. These tools first retrieve relevant data from a vast corpus or database, and then use that information to generate responses or content, enhancing the accuracy and detail of the generated text. RAG tools are particularly useful in applications requiring up-to-date information or specialized knowledge, such as customer support, content creation, and research. By leveraging both retrieval and generation capabilities, RAG tools improve the quality of responses in tasks like question-answering and summarization. This approach bridges the gap between static knowledge bases and dynamic content generation, providing more reliable and context-aware results. Compare and read user reviews of the best Retrieval-Augmented Generation (RAG) software for Kubernetes currently available using the table below. This list is updated regularly.

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    Second State

    Second State

    Second State

    Fast, lightweight, portable, rust-powered, and OpenAI compatible. We work with cloud providers, especially edge cloud/CDN compute providers, to support microservices for web apps. Use cases include AI inference, database access, CRM, ecommerce, workflow management, and server-side rendering. We work with streaming frameworks and databases to support embedded serverless functions for data filtering and analytics. The serverless functions could be database UDFs. They could also be embedded in data ingest or query result streams. Take full advantage of the GPUs, write once, and run anywhere. Get started with the Llama 2 series of models on your own device in 5 minutes. Retrieval-argumented generation (RAG) is a very popular approach to building AI agents with external knowledge bases. Create an HTTP microservice for image classification. It runs YOLO and Mediapipe models at native GPU speed.
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