Best Web-Based Embedded Database Systems of 2025

MySQL stands out as the most widely used open source database globally. Thanks to its established track record in performance, dependability, and user-friendliness, it has emerged as the preferred database for web applications, powering notable platforms such as Facebook, Twitter, and YouTube, alongside the top five websites. Furthermore, MySQL is also highly favored as an embedded database solution, being distributed by numerous independent software vendors and original equipment manufacturers. Its versatility and robust features contribute to its widespread adoption across various industries.

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.

VelocityDB stands out as a unique database engine, renowned for its ability to store data more quickly and efficiently than any competing solutions while being significantly more affordable. Unlike traditional databases that rely on mapping to tables, JSON, or XML, it retains .NET objects in their native format. Complementing this is VelocityGraph, an open-source property graph database that seamlessly integrates with the VelocityDB object database. As a C# .NET noSQL object database, VelocityDB is enhanced by VelocityGraph, making it one of the fastest, most scalable, and flexible database options available today. Bugs that are reported along with reproducible test cases are typically resolved within a week, showcasing the responsiveness of the development team. The primary advantage of using this database system lies in its remarkable flexibility, enabling developers to tailor their applications to meet even the most specific requirements. With VelocityDB, you have the freedom to select the most suitable data structures for your needs, granting you control over the persistent placement of data, as well as how it is indexed and accessed. This level of customization sets VelocityDB apart in the realm of database technology.

Chroma is an open-source embedding database that is designed specifically for AI applications. It provides a comprehensive set of tools for working with embeddings, making it easier for developers to integrate this technology into their projects. Chroma is focused on developing a database that continually learns and evolves. You can contribute by addressing an issue, submitting a pull request, or joining our Discord community to share your feature suggestions and engage with other users. Your input is valuable as we strive to enhance Chroma's functionality and usability.

LanceDB is an accessible, open-source database specifically designed for AI development. It offers features such as hyperscalable vector search and sophisticated retrieval capabilities for Retrieval-Augmented Generation (RAG), along with support for streaming training data and the interactive analysis of extensive AI datasets, making it an ideal foundation for AI applications. The installation process takes only seconds, and it integrates effortlessly into your current data and AI toolchain. As an embedded database—similar to SQLite or DuckDB—LanceDB supports native object storage integration, allowing it to be deployed in various environments and efficiently scale to zero when inactive. Whether for quick prototyping or large-scale production, LanceDB provides exceptional speed for search, analytics, and training involving multimodal AI data. Notably, prominent AI companies have indexed vast numbers of vectors and extensive volumes of text, images, and videos at a significantly lower cost compared to other vector databases. Beyond mere embedding, it allows for filtering, selection, and streaming of training data directly from object storage, thereby ensuring optimal GPU utilization for enhanced performance. This versatility makes LanceDB a powerful tool in the evolving landscape of artificial intelligence.

Microsoft has invested significant effort in creating a robust synergy between .NET and SQL Server, and VistaDB encapsulates this strength in a compact and easy-to-deploy format. The VistaDB engine consists of a singular, lightweight managed assembly that can be integrated with your application, with each database represented as a standalone file. By leveraging Xamarin, it is possible to have a complete relational database management system (RDBMS) right on your mobile device. VistaDB serves as a fully compliant ADO.NET data provider, offering support for Entity Framework, ADO.NET, and Typed Datasets, alongside its own direct access API tailored for efficient cursor operations. Thanks to ADO.NET, VistaDB seamlessly integrates with a broad spectrum of third-party object-relational mappers (ORMs), reporting tools, and additional resources. It also fully embraces the ADO.NET provider factory model, enabling developers to create a unified codebase that can adapt to either VistaDB or SQL Server during runtime. This versatility extends to a multitude of applications built on ADO.NET, encompassing not just your own software but also various reporting systems, ORMs, and other data-centric libraries within the .NET ecosystem. With VistaDB’s capabilities, developers can harness the power of a full-fledged database system while enjoying the simplicity of deployment and use.

Firebird is a relational database that incorporates numerous features compliant with ANSI SQL standards and is compatible with Linux, Windows, and multiple Unix systems. It boasts remarkable concurrency, impressive performance, and robust support for stored procedures and triggers, making it a popular choice for various applications. Since its inception in 1981, Firebird has been utilized in production environments under different names. The Firebird Project operates independently, consisting of C and C++ developers, technical advisors, and supporters who contribute to the ongoing development of this multi-platform relational database management system, which is based on the source code released by Inprise Corp (now Borland Software Corp) on July 25, 2000. Moreover, the Firebird Project provides a wealth of documentation for users, developers, and administrators, ranging from beginner Quick Start guides to in-depth expert articles that explore numerous aspects of the Firebird system. This extensive resource ensures that anyone working with Firebird can find the information they need to optimize their experience.

CUBRID is a relational database management system (DBMS) specifically designed for online transaction processing (OLTP) that adheres to ANSI SQL standards, offering features such as MVCC support, high-availability capabilities, and user-friendly GUI tools for database management and migration. Additionally, it boasts compatibility with Oracle and MySQL, and supports a range of interfaces, including JDBC. Installation is straightforward, and it includes native GUI-based administration tools to streamline the development process. CUBRID's architecture is multi-threaded and multi-server, equipped with a native broker middleware, a cost-based optimizer, and advanced caching methods to enhance OLTP performance. It also incorporates highly reliable automatic failover technology via the CUBRID Heartbeat engine core, ensuring minimal downtime. With multi-volume support and automatic volume expansion, it can handle an extensive number of databases, tables, and indexes without size limitations. Furthermore, its robust features make it suitable for dynamic and scalable applications in various industries.

InterSystems Cache®, a high-performance database, powers transaction processing applications all over the globe. It's used for everything, from mapping a million stars in the Milky Way to processing a trillion equity trades per day to managing smart energy grids. InterSystems has developed Cache, a multi-model (object-relational, key-value), DBMS and application server. InterSystems Cache offers multiple APIs that allow you to work with the same data simultaneously: key/value, relational/object, document, multidimensional, object, object, and object. Data can be managed using SQL, Java, node.js.NET, C++ and Python. Cache also offers an application server that hosts web apps (CSP, REST, SOAP and other types TCP access for Cache data).

Berkeley DB encompasses a suite of embedded key-value database libraries that deliver scalable and high-performance data management functionalities for various applications. Its products utilize straightforward function-call APIs for accessing and managing data efficiently. With Berkeley DB, developers can create tailored data management solutions that bypass the typical complexities linked with custom projects. The library offers a range of reliable building-block technologies that can be adapted to meet diverse application requirements, whether for handheld devices or extensive data centers, catering to both local storage needs and global distribution, handling data volumes that range from kilobytes to petabytes. This versatility makes Berkeley DB a preferred choice for developers looking to implement efficient data solutions.

IBM Informix® is a highly adaptable and efficient database that can effortlessly combine SQL, NoSQL/JSON, as well as time series and spatial data. Its flexibility and user-friendly design position Informix as a top choice for diverse settings, ranging from large-scale enterprise data warehouses to smaller individual application development projects. Moreover, due to its compact footprint and self-managing features, Informix is particularly advantageous for embedded data management applications. The rising demand for IoT data processing necessitates strong integration and processing capabilities, which Informix fulfills with its hybrid database architecture that requires minimal administrative effort and has a small memory footprint while delivering robust functionality. Notably, Informix is well-equipped for multi-tiered architectures that necessitate processing at various levels, including devices, gateway layers, and cloud environments. Furthermore, it incorporates native encryption to safeguard data both at rest and in transit. Additionally, Informix supports a flexible schema alongside multiple APIs and configurations, making it a versatile choice for modern data management challenges.

Experience the lightning-fast NoSQL database tailored for mobile and IoT applications, complete with built-in data synchronization. ObjectBox boasts a performance that is ten times superior to its competitors, significantly enhancing response times and facilitating real-time functionality. Our benchmarks speak for themselves, supporting a comprehensive range of systems from sensors to servers. Compatibility extends across various platforms, including Linux, Windows, macOS/iOS, Android, and Raspbian, whether you choose embedded solutions or containerized setups. Enjoy seamless data synchronization with ObjectBox's ready-to-use features, ensuring that your data is accessible precisely when and where it’s needed, allowing you to launch your application more swiftly. Develop applications that operate both online and offline, providing a reliable experience without the dependency on a continuous internet connection, creating an “always-on” atmosphere for users. Save valuable time and development resources by expediting your time-to-market, reducing both development costs and lifecycle expenses, while allowing developers to focus on high-value tasks, as ObjectBox mitigates potential risks. Moreover, ObjectBox can decrease cloud expenses by up to 60% by storing data locally at the edge and efficiently synchronizing only the necessary information. This approach not only optimizes performance but also enhances data management and accessibility across your applications.

Actian Zen is a compact, efficient database management system tailored for embedded use in edge applications, mobile technologies, and IoT settings. This system uniquely combines SQL and NoSQL data structures, offering developers the versatility needed to handle both structured and unstructured information. Renowned for its minimal resource requirements, scalability, and dependable performance, Actian Zen is particularly suited for environments that have limited resources and demand consistent output with low maintenance. It boasts integrated security measures and an architecture that automatically adjusts, allowing for real-time data processing and analytics while minimizing the need for continuous oversight. Its application spans various sectors, including healthcare, retail, and manufacturing, where the capacity for edge computing and managing distributed datasets is vital for operational success. As businesses increasingly rely on technology, the significance of Actian Zen in facilitating efficient data management will only grow.

Transform your embeddings effortlessly with a single, powerful tool. Discover an extensive database crafted to deliver embedding capabilities that previously necessitated several different platforms, making it easier than ever to enhance your machine learning endeavors swiftly and seamlessly with Embeddinghub. Embeddings serve as compact, numerical representations of various real-world entities and their interrelations, represented as vectors. Typically, they are generated by first establishing a supervised machine learning task, often referred to as a "surrogate problem." The primary goal of embeddings is to encapsulate the underlying semantics of their originating inputs, allowing them to be shared and repurposed for enhanced learning across multiple machine learning models. With Embeddinghub, achieving this process becomes not only streamlined but also incredibly user-friendly, ensuring that users can focus on their core functions without unnecessary complexity.

Ditto is the only mobile database with built-in edge device connectivity and resiliency, enabling apps to synchronize without relying on a central server or constant cloud connectivity. With billions of edge devices and deskless workers driving operations and revenue, businesses are hitting the limits of what traditional cloud architectures can offer. Trusted by Chick-fil-A, Delta, Lufthansa, Japan Airlines, and more, Ditto is pioneering the edge-native revolution, transforming how businesses connect, sync, and operate at the edge. By eliminating hardware dependencies, Ditto’s software-driven networking is enabling businesses to build faster, more resilient systems that thrive at the edge – no Wi-Fi, servers, or cloud required. Through the use of CRDTs and P2P mesh replication, Ditto's technology enables you to build collaborative, resilient applications where data is always available and up-to-date for every user, and can even be synced in completely offline situations. This allows you to keep mission-critical systems online when it matters most. Ditto uses an edge-native architecture. Edge-native solutions are built specifically to thrive on mobile and edge devices, without relying solely on cloud-based services. Devices running Ditto apps can discover and communicate with each other directly, forming an ad-hoc mesh network rather than routing everything through a cloud server. The platform automatically handles the complexity of discovery and connectivity using both online and offline channels – Bluetooth, peer-to-peer Wi-Fi, local LAN, WiFi, Cellular – to find nearby devices and sync data changes in real-time.

Businesses require a solution to maintain seamless operations of their systems, enhance data processing speed, and ensure storage reliability. In-memory technologies have emerged as effective tools for addressing these challenges. For over a decade, Tarantool has been assisting organizations globally in creating intelligent caches, data marts, and comprehensive client profiles while optimizing server utilization. This approach not only reduces the expenses associated with storing credentials compared to isolated solutions but also enhances both the service and security of client applications. Furthermore, it lowers the costs of data management by minimizing the number of separate systems that hold customer identities. By analyzing user behavior and data, companies can boost sales through improved speed and accuracy in recommending products or services. Additionally, enhancing the performance of mobile and web channels can significantly reduce user attrition. In the context of large organizations, IT systems often operate within a closed network loop, which poses risks as data circulates without adequate protection. Consequently, it becomes imperative for corporations to adopt robust strategies that not only safeguard their data but also ensure optimal system functionality.

The Mimer SQL codebase is recognized as one of the most advanced in the industry. It boasts a modular architecture that ensures exceptional maintainability, effortless scalability, and a focus on portability. Our expertise in maximizing the capabilities of computers and operating systems is reflected in Mimer SQL. Featuring a cutting-edge SQL compiler that employs the latest optimization techniques, alongside a top-tier storage engine, it establishes a foundation for unprecedented speed and efficiency, limited only by hardware constraints. The security features of Mimer SQL are continuously enhanced, leaving no aspect overlooked. It effectively safeguards data in use, in motion, and at rest, utilizing proven, reliable, and well-documented algorithms. As a perfect partner for contemporary in-vehicle computing solutions, Mimer SQL delivers performance and reliability that meet the rigorous demands of secure, adaptable, and dependable data management in the realm of modern autonomous and connected vehicles. With its innovative design, Mimer SQL ensures that users can confidently rely on it for all their data needs.

SAP HANA is an in-memory database designed to handle both transactional and analytical workloads using a single copy of data, regardless of type. It effectively dissolves the barriers between transactional and analytical processes within organizations, facilitating rapid decision-making whether deployed on-premises or in the cloud. This innovative database management system empowers users to create intelligent, real-time solutions, enabling swift decision-making from a unified data source. By incorporating advanced analytics, it enhances the capabilities of next-generation transaction processing. Organizations can build data solutions that capitalize on cloud-native attributes such as scalability, speed, and performance. With SAP HANA Cloud, businesses can access reliable, actionable information from one cohesive platform while ensuring robust security, privacy, and data anonymization, reflecting proven enterprise standards. In today's fast-paced environment, an intelligent enterprise relies on timely insights derived from data, emphasizing the need for real-time delivery of such valuable information. As the demand for immediate access to insights grows, leveraging an efficient database like SAP HANA becomes increasingly critical for organizations aiming to stay competitive.

Couchbase distinguishes itself from other NoSQL databases by delivering an enterprise-grade, multicloud to edge solution that is equipped with the powerful features essential for mission-critical applications on a platform that is both highly scalable and reliable. This distributed cloud-native database operates seamlessly in contemporary dynamic settings, accommodating any cloud environment, whether it be customer-managed or a fully managed service. Leveraging open standards, Couchbase merges the advantages of NoSQL with the familiar structure of SQL, thereby facilitating a smoother transition from traditional mainframe and relational databases. Couchbase Server serves as a versatile, distributed database that integrates the benefits of relational database capabilities, including SQL and ACID transactions, with the adaptability of JSON, all built on a foundation that is remarkably fast and scalable. Its applications span various industries, catering to needs such as user profiles, dynamic product catalogs, generative AI applications, vector search, high-speed caching, and much more, making it an invaluable asset for organizations seeking efficiency and innovation.

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.

Symas LMDB is an incredibly swift and memory-efficient database that we created specifically for the OpenLDAP Project. Utilizing memory-mapped files, it achieves the read speed typical of purely in-memory databases while also providing the durability associated with traditional disk-based systems. In essence, despite its modest size of just 32KB of object code, LMDB packs a significant punch; it is indeed the perfect 32KB. The compact nature and efficiency of LMDB are integral to its remarkable capabilities. For those integrating LMDB into their applications, Symas provides fixed-price commercial support. Development is actively carried out in the mdb.master branch of the OpenLDAP Project’s git repository. Moreover, LMDB has garnered attention across numerous impressive products and publications, highlighting its versatility and effectiveness in various contexts. Its widespread recognition further cements its status as a vital tool for developers.

Oracle TimesTen In-Memory Database (TimesTen) enhances real-time application performance by rethinking the runtime data storage approach, resulting in reduced response times and increased throughput. By utilizing in-memory data management and refining data structures alongside access algorithms, TimesTen maximizes the efficiency of database operations, leading to significant improvements in both responsiveness and transaction throughput. The launch of TimesTen Scaleout introduces a shared-nothing architecture that builds on the existing in-memory capabilities, enabling seamless scaling across numerous hosts, accommodating vast data volumes of hundreds of terabytes, and processing hundreds of millions of transactions per second, all without requiring manual sharding or workload distribution. This innovative approach not only streamlines performance but also simplifies the overall database management experience for users.

LevelDB is a high-performance key-value storage library developed by Google, designed to maintain an ordered mapping between string keys and string values. The keys and values are treated as arbitrary byte arrays, and the stored data is organized in a sorted manner based on the keys. Users have the option to supply a custom comparison function to modify the default sorting behavior. The library allows for multiple changes to be grouped into a single atomic batch, ensuring data integrity during updates. Additionally, users can create a temporary snapshot for a consistent view of the data at any given moment. The library supports both forward and backward iteration through the stored data, enhancing flexibility during data access. Data is automatically compressed using the Snappy compression algorithm to optimize storage efficiency. Moreover, interactions with the external environment, such as file system operations, are managed through a virtual interface, giving users the ability to customize how the library interacts with the operating system. In practical applications, we utilize a database containing one million entries, where each entry consists of a 16-byte key and a 100-byte value. Notably, the values used in benchmarking compress to approximately half of their original size, allowing for significant space savings. We provide detailed performance metrics for sequential reads in both forward and reverse directions, as well as the effectiveness of random lookups, to showcase the library's capabilities. This comprehensive performance analysis aids developers in understanding how to optimize their use of LevelDB in various applications.

Handling and storing tabular data, such as that found in CSV or Parquet formats, is essential for data management. Transferring large result sets to clients is a common requirement, especially in extensive client/server frameworks designed for centralized enterprise data warehousing. Additionally, writing to a single database from various simultaneous processes poses its own set of challenges. DuckDB serves as a relational database management system (RDBMS), which is a specialized system for overseeing data organized into relations. In this context, a relation refers to a table, characterized by a named collection of rows. Each row within a table maintains a consistent structure of named columns, with each column designated to hold a specific data type. Furthermore, tables are organized within schemas, and a complete database comprises a collection of these schemas, providing structured access to the stored data. This organization not only enhances data integrity but also facilitates efficient querying and reporting across diverse datasets.

HSQLDB, or HyperSQL DataBase, stands out as a premier SQL relational database system developed in Java. It boasts a compact, efficient multithreaded transactional engine that accommodates both in-memory and disk-based tables, functioning effectively in embedded and server configurations. Users can take advantage of a robust command-line SQL interface along with straightforward GUI query tools. HSQLDB is distinguished by its comprehensive support for a vast array of SQL Standard features, including the core language components from SQL:2016 and an impressive collection of optional features from the same standard. It provides full support for Advanced ANSI-92 SQL, with only two notable exceptions. Additionally, HSQLDB includes numerous enhancements beyond the Standard, featuring compatibility modes and functionalities that align with other widely used database systems. Its versatility and extensive feature set make it a highly adaptable choice for developers and organizations alike.