Alternatives to Chroma

RaimaDB, an embedded time series database that can be used for Edge and IoT devices, can run in-memory. It is a lightweight, secure, and extremely powerful RDBMS. It has been field tested by more than 20 000 developers around the world and has been deployed in excess of 25 000 000 times.

It is a highly scalable, embedded SQL database that can be accessed from anywhere. It also includes commercial-grade data security, disaster recovery, change synchronization, and data security.

Searching and analyzing structured data is easy; however, over 80% of generated data is unstructured, requiring a different approach. Machine learning converts unstructured data into high-dimensional vectors of numerical values, which makes it possible to find patterns or relationships within that data type. Unfortunately, traditional databases were never meant to store vectors or embeddings and can not meet unstructured data's scalability and performance requirements. Zilliz Cloud is a cloud-native vector database that stores, indexes, and searches for billions of embedding vectors to power enterprise-grade similarity search, recommender systems, anomaly detection, and more. Zilliz Cloud, built on the popular open-source vector database Milvus, allows for easy integration with vectorizers from OpenAI, Cohere, HuggingFace, and other popular models. Purpose-built to solve the challenge of managing billions of embeddings, Zilliz Cloud makes it easy to build applications for scale.

The AI Knowledge Platform. The Pinecone Database, Inference, and Assistant make building high-performance vector search apps easy. Fully managed and developer-friendly, the database is easily scalable without any infrastructure problems. Once you have vector embeddings created, you can search and manage them in Pinecone to power semantic searches, recommenders, or other applications that rely upon relevant information retrieval. Even with billions of items, ultra-low query latency Provide a great user experience. You can add, edit, and delete data via live index updates. Your data is available immediately. For more relevant and quicker results, combine vector search with metadata filters. Our API makes it easy to launch, use, scale, and scale your vector searching service without worrying about infrastructure. It will run smoothly and securely.

MyScale is a cutting-edge AI database that combines vector search with SQL analytics, offering a seamless, fully managed, and high-performance solution. Key features of MyScale include: - Enhanced data capacity and performance: Each standard MyScale pod supports 5 million 768-dimensional data points with exceptional accuracy, delivering over 150 QPS. - Swift data ingestion: Ingest up to 5 million data points in under 30 minutes, minimizing wait times and enabling faster serving of your vector data. - Flexible index support: MyScale allows you to create multiple tables, each with its own unique vector indexes, empowering you to efficiently manage heterogeneous vector data within a single MyScale cluster. - Seamless data import and backup: Effortlessly import and export data from and to S3 or other compatible storage systems, ensuring smooth data management and backup processes. With MyScale, you can harness the power of advanced AI database capabilities for efficient and effective data analysis.

Qdrant is a vector database and similarity engine. It is an API service that allows you to search for the closest high-dimensional vectors. Qdrant allows embeddings and neural network encoders to be transformed into full-fledged apps for matching, searching, recommending, etc. This specification provides the OpenAPI version 3 specification to create a client library for almost any programming language. You can also use a ready-made client for Python, or other programming languages that has additional functionality. For Approximate Nearest Neighbor Search, you can make a custom modification to the HNSW algorithm. Search at a State of the Art speed and use search filters to maximize results. Additional payload can be associated with vectors. Allows you to store payload and filter results based upon payload values.

Weaviate is an open source vector database. It allows you to store vector embeddings and data objects from your favorite ML models, and scale seamlessly into billions upon billions of data objects. You can index billions upon billions of data objects, whether you use the vectorization module or your own vectors. Combining multiple search methods, such as vector search and keyword-based search, can create state-of-the art search experiences. To improve your search results, pipe them through LLM models such as GPT-3 to create next generation search experiences. Weaviate's next generation vector database can be used to power many innovative apps. You can perform a lightning-fast, pure vector similarity search on raw vectors and data objects. Combining keyword-based and vector search techniques will yield state-of the-art results. You can combine any generative model with your data to do Q&A, for example, over your dataset.

Faiss is a library that allows for efficient similarity searches and clustering dense vectors. It has algorithms that can search for vectors of any size. It also includes supporting code for parameter tuning and evaluation. Faiss is written entirely in C++ and includes wrappers for Python. The GPU is home to some of the most powerful algorithms. It was developed by Facebook AI Research.

One tool allows you to operationalize your embeddings. A comprehensive database that provides embedding functionality previously unavailable on multiple platforms is now available to you. Embeddinghub makes it easy to accelerate your machine learning. Embeddings are dense numerical representations of real world objects and relationships. They can be expressed as vectors. They are often created by first defining an unsupervised machine learning problem, also known as a "surrogate issue". Embeddings are intended to capture the semantics from the inputs they were derived. They can then be shared and reused for better learning across machine learning models. This is possible with Embeddinghub in an intuitive and streamlined way.

VectorDB is a lightweight Python program for storing and retrieving texts using chunking techniques, embedding techniques, and vector search. It offers an easy-to use interface for searching, managing, and saving textual data, along with metadata, and is designed to be used in situations where low latency and speed are essential. When working with large language model datasets, vector search and embeddings become essential. They allow for efficient and accurate retrieval relevant information. These techniques enable quick comparisons and search, even with millions of documents. This allows you to find the most relevant search results in a fraction the time of traditional text-based methods. The embeddings also capture the semantic meaning in the text. This helps improve the search results, and allows for more advanced natural-language processing tasks.

LanceDB is an open-source database for AI that is developer-friendly. LanceDB provides the best foundation for AI applications. From hyperscalable vector searches and advanced retrieval of RAG data to streaming training datasets and interactive explorations of large AI datasets. Installs in seconds, and integrates seamlessly with your existing data and AI tools. LanceDB is an embedded database with native object storage integration (think SQLite, DuckDB), which can be deployed anywhere. It scales down to zero when it's not being used. LanceDB is a powerful tool for rapid prototyping and hyper-scale production. It delivers lightning-fast performance in search, analytics, training, and multimodal AI data. Leading AI companies have indexed petabytes and billions of vectors, as well as text, images, videos, and other data, at a fraction the cost of traditional vector databases. More than just embedding. Filter, select and stream training data straight from object storage in order to keep GPU utilization at a high level.

LlamaIndex, a "dataframework", is designed to help you create LLM apps. Connect semi-structured API data like Slack or Salesforce. LlamaIndex provides a flexible and simple data framework to connect custom data sources with large language models. LlamaIndex is a powerful tool to enhance your LLM applications. Connect your existing data formats and sources (APIs, PDFs, documents, SQL etc.). Use with a large-scale language model application. Store and index data for different uses. Integrate downstream vector stores and database providers. LlamaIndex is a query interface which accepts any input prompts over your data, and returns a knowledge augmented response. Connect unstructured data sources, such as PDFs, raw text files and images. Integrate structured data sources such as Excel, SQL etc. It provides ways to structure data (indices, charts) so that it can be used with LLMs.

A vector database designed for scalable similarity searches. Open-source, highly scalable and lightning fast. Massive embedding vectors created by deep neural networks or other machine learning (ML), can be stored, indexed, and managed. Milvus vector database makes it easy to create large-scale similarity search services in under a minute. For a variety languages, there are simple and intuitive SDKs. Milvus is highly efficient on hardware and offers advanced indexing algorithms that provide a 10x speed boost in retrieval speed. Milvus vector database is used in a variety a use cases by more than a thousand enterprises. Milvus is extremely resilient and reliable due to its isolation of individual components. Milvus' distributed and high-throughput nature makes it an ideal choice for large-scale vector data. Milvus vector database uses a systemic approach for cloud-nativity that separates compute and storage.

Marqo is a complete vector search engine. It's more than just a database. A single API handles vector generation, storage and retrieval. No need to embed your own embeddings. Marqo can accelerate your development cycle. In just a few lines, you can index documents and start searching. Create multimodal indexes, and search images and text combinations with ease. You can choose from a variety of open-source models or create your own. Create complex and interesting queries with ease. Marqo allows you to compose queries that include multiple weighted components. Marqo includes input pre-processing and machine learning inference as well as storage. Marqo can be run as a Docker on your laptop, or scaled up to dozens GPU inference nodes. Marqo is scalable to provide low latency searches on multi-terabyte indices. Marqo allows you to configure deep-learning models such as CLIP for semantic meaning extraction from images.

Start building in just minutes. Vectorize provides fast and cost-effective vector storage for your AI Retrieval augmented generation (RAG) & search applications. Vectorize integrates seamlessly with Cloudflare’s AI developer platform & AI gateway to centralize development, monitoring, and control of AI applications at a global level. Vectorize is a globally-distributed vector database that allows you to build AI-powered full-stack applications using Cloudflare Workers AI. Vectorize makes it easier and cheaper to query embeddings - representations of objects or values such as text, images, audio, etc. - that are intended to be consumed by machine intelligence models and semantic search algorithms. Search, similarity and recommendation, classification, anomaly detection, and classification based on your data. Search results are improved and faster. Support for string, number and boolean type.

We've already done the hard work of bundling, pre-configuring and integrating all the components that you need to build solutions and integrate LLMs into your business processes. ConfidentialMind allows you to jump into action. Deploy an endpoint for powerful open-source LLMs such as Llama-2 and turn it into an LLM API. Imagine ChatGPT on your own cloud. This is the most secure option available. Connects the rest with the APIs from the largest hosted LLM provider like Azure OpenAI or AWS Bedrock. ConfidentialMind deploys a Streamlit-based playground UI with a selection LLM-powered productivity tool for your company, such as writing assistants or document analysts. Includes a vector data base, which is critical for most LLM applications to efficiently navigate through large knowledge bases with thousands documents. You can control who has access to your team's solutions and what data they have.

Vespa is forBig Data + AI, online. At any scale, with unbeatable performance. Vespa is a fully featured search engine and vector database. It supports vector search (ANN), lexical search, and search in structured data, all in the same query. Integrated machine-learned model inference allows you to apply AI to make sense of your data in real-time. Users build recommendation applications on Vespa, typically combining fast vector search and filtering with evaluation of machine-learned models over the items. To build production-worthy online applications that combine data and AI, you need more than point solutions: You need a platform that integrates data and compute to achieve true scalability and availability - and which does this without limiting your freedom to innovate. Only Vespa does this. Together with Vespa's proven scaling and high availability, this empowers you to create production-ready search applications at any scale and with any combination of features.

HSQLDB (HyperSQL DataBase), is the most popular SQL relational database system in Java. It is a small, fast, multithreaded, transactional database engine that supports both embedded and server modes. It also includes simple GUI query tools and a powerful command-line SQL tool. HSQLDB supports all the SQL Standard features found in an open-source database engine, including the SQL:2016 core language features as well as a wide range of optional SQL:2016 features. With only two exceptions, it supports Advanced ANSI-92 SQL. Many extensions to the Standard are supported, including syntax compatibility modes, and features of popular database engines.

Metal is a fully-managed, production-ready ML retrieval platform. Metal embeddings can help you find meaning in unstructured data. Metal is a managed services that allows you build AI products without having to worry about managing infrastructure. Integrations with OpenAI and CLIP. Easy processing & chunking of your documents. Profit from our system in production. MetalRetriever is easily pluggable. Simple /search endpoint to run ANN queries. Get started for free. Metal API Keys are required to use our API and SDKs. Authenticate by populating headers with your API Key. Learn how to integrate Metal into your application using our Typescript SDK. You can use this library in JavaScript as well, even though we love TypeScript. Fine-tune spp programmatically. Indexed vector data of your embeddings. Resources that are specific to your ML use case.

Perst, an object-oriented embedded database (ODBMS) from McObject, is open source and dual licensed. It is available as a Java-only embedded database and a C# version (for Microsoft's.NET Framework). Perst allows developers to store, sort and retrieve objects with minimal memory and storage overhead, while leveraging Java and C#'s object-oriented paradigm. Perst's performance advantage over Java or.NET embedded databases is evident in the TestIndex benchmarks and PolePosition benchmarks. Perst stores data in Java and.NET object, eliminating the need for translations required to store in relational or object-relational database. This increases performance at runtime. Perst is a core program that only has five thousand lines. The small footprint places minimal demands on the system resources.

We've been working on Generative AI for 5 years. Deep Lake combines the power and flexibility of vector databases and data lakes to create enterprise-grade LLM-based solutions and refine them over time. Vector search does NOT resolve retrieval. You need a serverless search for multi-modal data including embeddings and metadata to solve this problem. You can filter, search, and more using the cloud, or your laptop. Visualize your data and embeddings to better understand them. Track and compare versions to improve your data and your model. OpenAI APIs are not the foundation of competitive businesses. Your data can be used to fine-tune LLMs. As models are being trained, data can be efficiently streamed from remote storage to GPUs. Deep Lake datasets can be visualized in your browser or Jupyter Notebook. Instantly retrieve different versions and materialize new datasets on the fly via queries. Stream them to PyTorch, TensorFlow, or Jupyter Notebook.

Astra DB from DataStax is a real-time vector database as a service for developers that need to get accurate Generative AI applications into production, fast. Astra DB gives you a set of elegant APIs supporting multiple languages and standards, powerful data pipelines and complete ecosystem integrations. Astra DB enables you to quickly build Gen AI applications on your real-time data for more accurate AI that you can deploy in production. Built on Apache Cassandra, Astra DB is the only vector database that can make vector updates immediately available to applications and scale to the largest real-time data and streaming workloads, securely on any cloud. Astra DB offers unprecedented serverless, pay as you go pricing and the flexibility of multi-cloud and open-source. You can store up to 80GB and/or perform 20 million operations per month. Securely connect to VPC peering and private links. Manage your encryption keys with your own key management. SAML SSO secure account accessibility. You can deploy on Amazon, Google Cloud, or Microsoft Azure while still compatible with open-source Apache Cassandra.

Azure Managed Redis offers the latest Redis innovations and industry-leading availability. It also has a cost-effective Total Cost Of Ownership (TCO) that is designed for hyperscale clouds. Azure Managed Redis provides these capabilities on a trusted platform, empowering businesses with the ability to scale and optimize generative AI applications in a seamless manner. Azure Managed Redis uses the latest Redis innovations for high-performance and scalable AI applications. Its features, such as in-memory storage, vector similarity searches, and real-time computing, allow developers to handle large datasets, accelerate machine-learning, and build faster AI applications. Its interoperability to Azure OpenAI Service allows AI workloads that are ready for mission-critical applications to be faster, more scalable and more reliable.

Actian Zen is a low-maintenance, embedded database management system that is high-performance and designed for edge applications, IoT, mobile devices and IoT environments. It provides developers with flexibility when working with unstructured and structured data. Actian Zen's small footprint, scalability and high reliability make it ideal for resource-constrained settings where consistent performance and minimal administration overhead are essential. It supports real-time analytics and data processing with built-in security and a self tuning architecture without the need for constant maintenance or monitoring. Actian Zen is used by many industries, including healthcare, retail and manufacturing, in which edge computing and distributed data environments play a critical role for business operations.

RocksDB uses a log-structured database engine written entirely in C++ for maximum performance. Keys and values can be stored in arbitrarily-sized byte streams. RocksDB is optimized to store flash drives and high speed disk drives in fast, low latency storage. RocksDB makes the most of flash and RAM's high read/write speeds. RocksDB can perform basic operations like opening and closing a table, reading and writing, and more complex operations such as merging or compaction filters. RocksDB can adapt to different workloads. RocksDB can be used to meet a wide range of data needs, including database storage engines like MyRocks and application data caching.

MySQL is the most widely used open-source database in the world. MySQL is the most popular open source database for web-based applications. It has been proven to be reliable, performant, and easy-to-use. This database is used by many high-profile web properties, including Facebook, Twitter and YouTube. It is also a popular choice for embedded databases, distributed by thousands ISVs and OEMs.

ArcadeDB allows you to manage complex models without any compromises. Polyglot Persistence is gone. There is no need to have multiple databases. ArcadeDB Multi-Model databases can store graphs and documents, key values, time series, and key values. Each model is native to the database engine so you don't need to worry about translations slowing down your computer. ArcadeDB's engine was developed with Alien Technology. It can crunch millions upon millions of records per second. ArcadeDB's traversing speed does not depend on the size of the database. It doesn't matter if your database contains a few records or a billion. ArcadeDB can be used as an embedded database on a single server. It can scale up by using Kubernetes to connect multiple servers. It is flexible enough to run on any platform that has a small footprint. Your data is protected. Our unbreakable fully transactional engine ensures durability for mission-critical production database databases. ArcadeDB uses the Raft Consensus Algorithm in order to maintain consistency across multiple servers.

Create and manage AI applications without the need to move data to complex vector databases and pipelines. Integrate AI, vector search and real-time inference directly with your database. Python is all you need. All your AI models can be deployed in a single, scalable deployment. The AI models and APIs are automatically updated as new data is processed. You don't need to duplicate your data or create an additional database to use vector searching and build on it. SuperDuperDB allows vector search within your existing database. Integrate and combine models such as those from Sklearn PyTorch HuggingFace, with AI APIs like OpenAI, to build even the most complicated AI applications and workflows. With simple Python commands, deploy all your AI models in one environment to automatically compute outputs in your datastore (inference).

POS system offers the most advanced technology in a full-featured suite for distributors and retailers. OneStep-JV™, a Point of Sale system, combines the power of Java and Oracle. OneStep-JV™ point-of-sale systems are written in Java and have Oracle as their embedded database. This allows them to provide the best technology and inventory management software for retailers and distributors. OneStep-JV™ POS systems can be operated on single-user computers as well as small and very large networks. They can also be used on portable devices such Palm Tops that run on a variety of operating systems, including Windows, Windows Networks, Novell Unix, Linux, Unix, Unix, and Linux. OneStep-JV™ POS systems are built with Oracle's stability and include auto-recovery features that ensure database and inventory control software integrity.

IBM Informix®, a fast and flexible database that can seamlessly integrate SQL, NoSQL/JSON and time series data, is available. Informix's versatility and ease-of-use make it a popular choice for a wide variety of environments, including enterprise data warehouses and individual application development. Informix is also well-suited for embedded data management solutions due to its small footprint and self-managing capabilities.

A modern Postgres dataplatform for operators, developers and data engineers. AI builders can also use it to power mission-critical workloads. Flexible deployment across hybrid cloud and multi-cloud. EDB Postgres is the first intelligent data-platform for transactional, analytic, and new AI workloads, powered by a Postgres engine enhanced. It can be deployed either as a cloud managed service, as self-managed software or as a physical device. It provides built-in observability and AI-driven assistance. It also includes migration tooling and a single pane-of-glass for managing hybrid data estates. EDB Postgres AI elevates data infrastructure into a strategic technology asset, bringing analytical and AI systems close to customers' core transactional and operational data. All managed through Postgres, the world's most popular database. Modernize legacy systems with the most comprehensive Oracle compatibility and a suite migration tools to get customers onboard.

Berkeley DB is a set of embedded key-value databases libraries that provide high-performance data management services for applications.

Couchbase, unlike other NoSQL database, provides a multicloud to edge enterprise-class database that offers robust capabilities for business-critical apps on a highly available and scalable platform. Couchbase is a distributed cloud native database that runs on any cloud. It can be managed by the customer or fully managed. Couchbase is built using open standards and combines the best of NoSQL and SQL with the power and familiarity that mainframes and relational databases provide. Couchbase Server is an open-source, multipurpose distributed database. It combines the best of relational databases, such as SQL, ACID transactions, and JSON, with a foundation which is fast and scalable. It is used in many industries for things such as user profiles, dynamic catalogs, GenAI applications, vector search, caching at high speed, and more.

SolidDB is renowned for its ability to deliver data at lightning speed. SolidDB is used in millions of applications and telecommunications networks worldwide. It is used by market leaders like Cisco, HP and Alcatel. SolidDB is able to perform faster than traditional databases by storing critical data in memory and not on disk. It allows applications to run hundreds of thousands to millions upon transactions per second, with response times of just a few microseconds. SolidDB provides game-changing performance and built-in data availability features to help maintain uptime, prevent data losses, and accelerate recovery. SolidDB also gives administrators the ability to customize the software to meet their specific application requirements. It also includes features that simplify administration and deployment, which can help reduce total cost of ownership (TCO).

Empress Embedded Database engine, a relational database management software that specializes in embedded database technology, is the heartbeat behind EMPRESS RDBMS. It's a relational database management tool that focuses on embedded database technology. From car navigation systems to mission-critical military command and control systems, to complex medical systems and Internet routers, EMPRESS keeps a steady beat, 24 hours / 7, at the core of embedded system applications all over the world. Empress kernel level mr API gives users access the Empress Database kernel libraries. This is a unique feature in Empress. This Empress API is the fastest way to access Empress databases. MR Routines allow developers to have complete control over space and time when developing embedded database applications. Empress ODBC APIs and JDBC APIs allow Empress databases to be accessed in standalone or client/server modes. Empress ODBC APIs and JDBC APIs allow many 3rd-party ODBC/JDBC capable software packages access to Empress databases via Empress Connectivity Server or local Empress databases.

VelocityDB is a database platform unlike any other. It stores data faster and more efficiently than other databases engines at a fraction the cost. It stores.NET objects in their original form without any mapping to tables, JSON, or XML. VelocityGraph, an open-source property graph database, can be used in conjunction the VelocityDB object data base. Object and graph database engine VelocityDB, a C#.NET NoSQL object database, can be extended to VelocityGraph. World's fastest most scalable & flexible database. A bug reported with a reproducible case is usually fixed within one week. This database system offers the greatest benefit, flexibility. You can fine-tune your application like no other database system. You can choose the most suitable data structure for your application with VelocityDB. You can choose where and how the data is indexed and accessed.

Postgres: Open-source vector similarity search Supports exact and approximate closest neighbor search for L2 distances, inner product and cosine distances.

H2, the Java SQL database, is your welcome. An embedded mode allows an application to open a database within the same JVM by using JDBC. This connection mode is the fastest and most convenient. However, a database can only be opened in one virtual machine (and a class loader) at a time. Both in-memory and persistent databases are supported, as in all modes. There is no limit to the number of databases that can be opened simultaneously or the number of connections. Mixed mode is a combination between the server and embedded modes. The first application to connect to a database uses embedded mode. However, it also starts a server so other applications (running in different processes and virtual machines) can simultaneously access the same data. The local connections are just as fast as if the data were used in embedded mode. Remote connections are slightly slower.

What makes eXtremeDB platform independent? - Hybrid storage of data. Unlike other IMDS databases, eXtremeDB databases are all-in-memory or all-persistent. They can also have a mix between persistent tables and in-memory table. eXtremeDB's Active Replication Fabric™, which is unique to eXtremeDB, offers bidirectional replication and multi-tier replication (e.g. edge-to-gateway-to-gateway-to-cloud), compression to maximize limited bandwidth networks and more. - Row and columnar flexibility for time series data. eXtremeDB supports database designs which combine column-based and row-based layouts in order to maximize the CPU cache speed. - Client/Server and embedded. eXtremeDB provides data management that is fast and flexible wherever you need it. It can be deployed as an embedded system and/or as a clients/server database system. eXtremeDB was designed for use in resource-constrained, mission-critical embedded systems. Found in over 30,000,000 deployments, from routers to satellites and trains to stock market world-wide.

The enterprise database for time series, documents, and vectors. Store any type data and combine the simplicity and scalability NoSQL with SQL. CrateDB is a distributed database that runs queries in milliseconds regardless of the complexity, volume, and velocity.

In less than 2 minutes, you can create a backend. Get a Postgres database, authentication and instant APIs to start your project. Real-time subscriptions are also available. You can build faster and concentrate on your products. Every project is a Postgres database, the most trusted relational database in the world. You can add user sign-ups or logins to secure your data with Row Level Security. Large files can be stored, organized and served. Any media, including images and videos. Without the need to deploy or scale servers, you can write custom code and cron jobs. There are many starter projects and example apps to help you get started. We will instantly inspect your database and provide APIs. Stop creating repetitive CRUD endpoints. Instead, focus on your product. Type definitions directly from your database schema. Supabase can be used in the browser without a build. You can develop locally and push to production as soon as you are ready. You can manage Supabase projects on your local machine.

KDB.AI, a powerful knowledge based vector database, is a powerful search engine and knowledge-based vector data base that allows developers to create scalable, reliable, and real-time AI applications. It provides advanced search, recommendation, and personalization. Vector databases are the next generation of data management, designed for applications such as generative AI, IoT or time series. Here's what makes them unique, how they work and the new applications they're designed to serve.

The Databricks Data Intelligence Platform enables your entire organization to utilize data and AI. It is built on a lakehouse that provides an open, unified platform for all data and governance. It's powered by a Data Intelligence Engine, which understands the uniqueness in your data. Data and AI companies will win in every industry. Databricks can help you achieve your data and AI goals faster and easier. Databricks combines the benefits of a lakehouse with generative AI to power a Data Intelligence Engine which understands the unique semantics in your data. The Databricks Platform can then optimize performance and manage infrastructure according to the unique needs of your business. The Data Intelligence Engine speaks your organization's native language, making it easy to search for and discover new data. It is just like asking a colleague a question.

Deliver high-quality answers with a database that is built for advanced retrieval, augmented generation (RAG), and modern search. Focus on exponential growth using a vector database built for enterprise that includes security, compliance and responsible AI practices. With sophisticated retrieval strategies that are backed by decades worth of research and validation from customers, you can build better applications. Rapidly deploy your generative AI application with seamless platform and integrations of data sources, AI models and frameworks. Upload data automatically from a variety of supported Azure and 3rd-party sources. Streamline vector data with integrated extraction, chunking and enrichment. Support for multivectors, hybrids, multilinguals, and metadata filters. You can go beyond vector-only searching with keyword match scoring and reranking. Also, you can use geospatial searches, autocomplete, and geospatial search.

Neo4j's graph platform is designed to help you leverage data and data relationships. Developers can create intelligent applications that use Neo4j to traverse today's interconnected, large datasets in real-time. Neo4j's graph database is powered by a native graph storage engine and processing engine. It provides unique, actionable insights through an intuitive, flexible, and secure database.

Vald is a distributed, fast, dense and highly scalable vector search engine that approximates nearest neighbors. Vald was designed and implemented using the Cloud-Native architecture. It uses the fastest ANN Algorithm NGT for searching neighbors. Vald supports automatic vector indexing, index backup, horizontal scaling, which allows you to search from billions upon billions of feature vector data. Vald is simple to use, rich in features, and highly customizable. Usually, the graph must be locked during indexing. This can cause stop-the world. Vald uses distributed index graphs so that it continues to work while indexing. Vald has its own highly customizable Ingress/Egress filter. This can be configured to work with the gRPC interface. Horizontal scaling is available on memory and cpu according to your needs. Vald supports disaster recovery by enabling auto backup using Persistent Volume or Object Storage.

SAP HANA is an in-memory database with high performance that accelerates data-driven decision-making and actions. It supports all workloads and provides the most advanced analytics on multi-model data on premise and in cloud.

The superfast nosql database for mobile devices and iot, with integrated data sync. High-performance Objectbox runs 10x faster than other databases, improving response times and enabling real time applications. Check out our benchmarks. From sensor to server, and everything in between. We support windows, mac/ios and android. Containerized or embedded. Sync data seamlessly. Objectbox's out-of-the box synchronization makes data readily available so your app can go live faster. Offline first Create applications that can work offline and online, without the need for an internet connection. This gives you an "always on"-feeling. Save time and dev. Save time and dev. Objectbox can help you reduce time-to-market, development and lifecycle costs, and free up valuable developer time to do tasks that add value. Objectbox helps reduce cloud costs by persisting data locally (on-the edge) and syncing data faster and more efficiently.

Symas LMDB, an extremely fast and memory-efficient database that we created for the OpenLDAP Project, is Symas LMDB. It uses memory-mapped files to provide the same read performance as an in-memory database but retains the persistence of standard disk databases. LMDB is small at 32KB in object code. It's still the right 32KB. LMDB is both compact and efficient. That's why LMDB is so powerful. Symas provides fixed-price commercial support for those who use LMDB in their applications. Development takes place in the OpenLDAP Project's Git repo in mdb.master branch. Symas LMDB has been featured in numerous publications and publications.

CUBRID is a relational DBMS optimized for online transaction processing (OLTP) that complies with ANSI SQL standards and provides MVCC support, High-Availability (HA) capabilities, and GUI-based tools for DB management/migration. It also provides Oracle/MySQL compatibility and supports a variety of interfaces, including JDBC. * [Major RDBMS Features]: - ANSI SQL standard and support extended SQL syntaxes - Support VIEW/TRIGGER/PRIMARY KEY/FOREIGN KEY/SERIAL - Support Stored Procedure/Function - Seamless transactions: COMMIT/ROLLBACK/SAVEPOINT - Support automatic recovery in the event of failure CUBRID consists of a 3-tier structure of applications/interfaces, brokers, and servers, and the flexibility to build systems is ideal for data-intensive online transaction processing (OLTP) services. CUBRID provides ease of installation and native GUI-based administration tools for developers' convenience. Multi-threaded, multi-server architecture, native broker middleware, cost-based optimizer, and intensive caching techniques for your OLTP services. Very accurate predictable automatic fail-over built-in technology, based on the CUBRID Heartbeat native engine core.