
Couchbase’s operational data platform for AI is a scalable foundation for enterprise operational, analytical, mobile and AI workloads that replaces legacy infrastructure and data services.
Bring your data to life in new ways with Couchbase’s enterprise data partnership: launch game-changing customer experiences, explore the infinite possibilities of AI, scale your global operations, and move your data from the cloud to the edge, and beyond.
Couchbase’s operational data platform for AI eliminates fragmented tech stacks, so teams can stay innovative and agile, with less risk and lower cost of ownership. With enterprise partnership and scalable, AI-ready technology, Couchbase turns your data into the foundation for your next breakthrough.
- Power your Performance. Expect peak performance from your digital experiences—even at peak demand.
- Accelerate Your Innovation. Get to market faster and stay one step ahead of competitors with a unified data platform.
- Simplify Your Operations. Cut complexity and drive visibility by consolidating your legacy infrastructure and services.
- Control Your Costs. Optimize your infrastructure spending with a unified database that significantly reduces your TCO.
- Sync Your Experience. Take your data wherever it needs to go—across regions and data centers, from cloud to edge.
Learn more

LM-Kit.NET serves as a comprehensive toolkit tailored for the seamless incorporation of generative AI into .NET applications, fully compatible with Windows, Linux, and macOS systems. This versatile platform empowers your C# and VB.NET projects, facilitating the development and management of dynamic AI agents with ease.
Utilize efficient Small Language Models for on-device inference, which effectively lowers computational demands, minimizes latency, and enhances security by processing information locally. Discover the advantages of Retrieval-Augmented Generation (RAG) that improve both accuracy and relevance, while sophisticated AI agents streamline complex tasks and expedite the development process.
With native SDKs that guarantee smooth integration and optimal performance across various platforms, LM-Kit.NET also offers extensive support for custom AI agent creation and multi-agent orchestration. This toolkit simplifies the stages of prototyping, deployment, and scaling, enabling you to create intelligent, rapid, and secure solutions that are relied upon by industry professionals globally, fostering innovation and efficiency in every project.
Learn more
Amazon S3 Vectors
Amazon S3 Vectors stands out as a groundbreaking cloud object storage solution designed specifically for the large-scale storage and querying of vector embeddings, offering an efficient and economical option for applications like semantic search, AI-based agents, retrieval-augmented generation, and similarity searches. It introduces a unique “vector bucket” category within S3, allowing users to organize vectors into “vector indexes” and store high-dimensional embeddings that represent diverse forms of unstructured data, including text, images, and audio, while facilitating similarity queries through specialized APIs, all without requiring any infrastructure setup. Additionally, each vector can incorporate metadata such as tags, timestamps, and categories, which supports attribute-based filtered queries. One of the standout features of S3 Vectors is its remarkable scalability; it can manage up to 2 billion vectors per index and as many as 10,000 vector indexes within a single bucket, while ensuring elastic and durable storage accompanied by server-side encryption options through SSE-S3 or KMS. This innovative solution not only streamlines the management of extensive datasets but also significantly boosts the efficiency and effectiveness of data retrieval for developers and businesses, ultimately transforming the way organizations handle large volumes of unstructured data. With its advanced capabilities, Amazon S3 Vectors is positioned to redefine data storage and retrieval methodologies in the cloud.
Learn more
Qdrant
Qdrant operates as an advanced vector similarity engine and database, providing an API service that allows users to locate the nearest high-dimensional vectors efficiently. By leveraging Qdrant, individuals can convert embeddings or neural network encoders into robust applications aimed at matching, searching, recommending, and much more. It also includes an OpenAPI v3 specification, which streamlines the creation of client libraries across nearly all programming languages, and it features pre-built clients for Python and other languages, equipped with additional functionalities. A key highlight of Qdrant is its unique custom version of the HNSW algorithm for Approximate Nearest Neighbor Search, which ensures rapid search capabilities while permitting the use of search filters without compromising result quality. Additionally, Qdrant enables the attachment of extra payload data to vectors, allowing not just storage but also filtration of search results based on the contained payload values. This functionality significantly boosts the flexibility of search operations, proving essential for developers and data scientists. Its capacity to handle complex data queries further cements Qdrant's status as a powerful resource in the realm of data management.
Learn more