Alternatives to Astra DB

MongoDB Atlas stands out as the leading cloud database service available, offering unparalleled data distribution and seamless mobility across all major platforms, including AWS, Azure, and Google Cloud. Its built-in automation tools enhance resource management and workload optimization, making it the go-to choice for modern application deployment. As a fully managed service, it ensures best-in-class automation and adheres to established practices that support high availability, scalability, and compliance with stringent data security and privacy regulations. Furthermore, MongoDB Atlas provides robust security controls tailored for your data needs, allowing for the integration of enterprise-grade features that align with existing security protocols and compliance measures. With preconfigured elements for authentication, authorization, and encryption, you can rest assured that your data remains secure and protected at all times. Ultimately, MongoDB Atlas not only simplifies deployment and scaling in the cloud but also fortifies your data with comprehensive security features that adapt to evolving requirements.

Achieve exceptional response quality through a vector database specifically designed for advanced retrieval augmented generation (RAG) and contemporary search functionalities. Emphasize substantial growth with a robust, enterprise-ready vector database that inherently includes security, compliance, and ethical AI methodologies. Create superior applications utilizing advanced retrieval techniques that are underpinned by years of research and proven customer success. Effortlessly launch your generative AI application with integrated platforms and data sources, including seamless connections to AI models and frameworks. Facilitate the automatic data upload from an extensive array of compatible Azure and third-party sources. Enhance vector data processing with comprehensive features for extraction, chunking, enrichment, and vectorization, all streamlined in a single workflow. Offer support for diverse vector types, hybrid models, multilingual capabilities, and metadata filtering. Go beyond simple vector searches by incorporating keyword match scoring, reranking, geospatial search capabilities, and autocomplete features. This holistic approach ensures that your applications can meet a wide range of user needs and adapt to evolving demands.

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.

ScaleGrid is a fully managed Database-as-a-Service (DBaaS) platform that helps you automate your time-consuming database administration tasks both in the cloud and on-premises. ScaleGrid makes it easy to provision, monitor, backup, and scale open-source databases. It offers advanced security, high availability, query analysis, and troubleshooting support to improve your deployments' performance. The following databases are supported: - MySQL - PostgreSQL - Redis™. - MongoDB®, database - Greenplum™ (coming soon) ScaleGrid supports both public and privately-owned clouds such as AWS, Azure and Google Cloud Platform (GCP), DigitalOcean and Linode, Oracle Cloud Infrastructure, (OCI), VMware, and OpenStack. ScaleGrid is used by thousands of developers, startups, as well as enterprise customers such as Accenture, Meteor and Atlassian. It handles all your database operations at any scale, so you can concentrate on your application performance.

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.

Qdrant serves as a sophisticated vector similarity engine and database, functioning as an API service that enables the search for the closest high-dimensional vectors. By utilizing Qdrant, users can transform embeddings or neural network encoders into comprehensive applications designed for matching, searching, recommending, and far more. It also offers an OpenAPI v3 specification, which facilitates the generation of client libraries in virtually any programming language, along with pre-built clients for Python and other languages that come with enhanced features. One of its standout features is a distinct custom adaptation of the HNSW algorithm used for Approximate Nearest Neighbor Search, which allows for lightning-fast searches while enabling the application of search filters without diminishing the quality of the results. Furthermore, Qdrant supports additional payload data tied to vectors, enabling not only the storage of this payload but also the ability to filter search outcomes based on the values contained within that payload. This capability enhances the overall versatility of search operations, making it an invaluable tool for developers and data scientists alike.

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.

Amazon ElastiCache enables users to effortlessly establish, operate, and expand widely-used open-source compatible in-memory data stores in the cloud environment. It empowers the development of data-driven applications or enhances the efficiency of existing databases by allowing quick access to data through high throughput and minimal latency in-memory stores. This service is particularly favored for various real-time applications such as caching, session management, gaming, geospatial services, real-time analytics, and queuing. With fully managed options for Redis and Memcached, Amazon ElastiCache caters to demanding applications that necessitate response times in the sub-millisecond range. Functioning as both an in-memory data store and a cache, it is designed to meet the needs of applications that require rapid data retrieval. Furthermore, by utilizing a fully optimized architecture that operates on dedicated nodes for each customer, Amazon ElastiCache guarantees incredibly fast and secure performance for its users' critical workloads. This makes it an essential tool for businesses looking to enhance their application's responsiveness and scalability.

We have taken the initiative to bundle and set up all necessary components for crafting solutions and seamlessly integrating LLMs into your organizational workflows. With ConfidentialMind, you can immediately get started. It provides an endpoint for the most advanced open-source LLMs, such as Llama-2, effectively transforming it into an internal LLM API. Envision having ChatGPT operating within your personal cloud environment. This represents the utmost in security solutions available. It connects with the APIs of leading hosted LLM providers, including Azure OpenAI, AWS Bedrock, and IBM, ensuring comprehensive integration. Additionally, ConfidentialMind features a playground UI built on Streamlit, which offers a variety of LLM-driven productivity tools tailored for your organization, including writing assistants and document analysis tools. It also comes with a vector database, essential for efficiently sifting through extensive knowledge repositories containing thousands of documents. Furthermore, it empowers you to manage access to the solutions developed by your team and regulate what information the LLMs can access, enhancing data security and control. With these capabilities, you can drive innovation while ensuring compliance and safety within your business operations.

Introducing a versatile, open-source multi-cloud platform for contemporary data applications, built on Apache Cassandra™. Achieve global-scale performance with guaranteed 100% uptime while avoiding vendor lock-in. You have the flexibility to deploy on multi-cloud environments, on-premises infrastructures, or use Kubernetes. The platform is designed to be elastic and offers a pay-as-you-go pricing model to enhance total cost of ownership. Accelerate your development process with Stargate APIs, which support NoSQL, real-time interactions, reactive programming, as well as JSON, REST, and GraphQL formats. Bypass the difficulties associated with managing numerous open-source projects and APIs that lack scalability. This solution is perfect for various sectors including e-commerce, mobile applications, AI/ML, IoT, microservices, social networking, gaming, and other highly interactive applications that require dynamic scaling based on demand. Start your journey of creating modern data applications with Astra, a database-as-a-service powered by Apache Cassandra™. Leverage REST, GraphQL, and JSON alongside your preferred full-stack framework. This platform ensures that your richly interactive applications are not only elastic but also ready to gain traction from the very first day, all while offering a cost-effective Apache Cassandra DBaaS that scales seamlessly and affordably as your needs evolve. With this innovative approach, developers can focus on building rather than managing infrastructure.

Oracle Autonomous Database is a cloud-based database solution that automates various management tasks, such as tuning, security, backups, and updates, through the use of machine learning, thereby minimizing the reliance on database administrators. It accommodates an extensive variety of data types and models, like SQL, JSON, graph, geospatial, text, and vectors, which empowers developers to create applications across diverse workloads without the necessity of multiple specialized databases. The inclusion of AI and machine learning features facilitates natural language queries, automatic data insights, and supports the creation of applications that leverage artificial intelligence. Additionally, it provides user-friendly tools for data loading, transformation, analysis, and governance, significantly decreasing the need for intervention from IT staff. Furthermore, it offers versatile deployment options, which range from serverless to dedicated setups on Oracle Cloud Infrastructure (OCI), along with the alternative of on-premises deployment using Exadata Cloud@Customer, ensuring flexibility to meet varying business needs. This comprehensive approach streamlines database management and empowers organizations to focus more on innovation rather than routine maintenance.

Engaging applications captivate users while motivating developers to innovate. To meet the growing demands of the digital landscape, consider utilizing the DataStax Astra Streaming service platform. This cloud-native platform for messaging and event streaming is built on the robust foundation of Apache Pulsar. With Astra Streaming, developers can create streaming applications that leverage a multi-cloud, elastically scalable architecture. Powered by the advanced capabilities of Apache Pulsar, this platform offers a comprehensive solution that encompasses streaming, queuing, pub/sub, and stream processing. Astra Streaming serves as an ideal partner for Astra DB, enabling current users to construct real-time data pipelines seamlessly connected to their Astra DB instances. Additionally, the platform's flexibility allows for deployment across major public cloud providers, including AWS, GCP, and Azure, thereby preventing vendor lock-in. Ultimately, Astra Streaming empowers developers to harness the full potential of their data in real-time environments.

Azure Managed Redis incorporates cutting-edge Redis features, exceptional reliability, and a budget-friendly Total Cost of Ownership (TCO), all tailored for the demands of hyperscale cloud environments. This service operates on a dependable cloud platform, allowing organizations to effortlessly expand and enhance their generative AI applications. By integrating the most recent Redis advancements, Azure Managed Redis is optimized for high-performance, scalable AI solutions. It offers a variety of functionalities, including in-memory data storage, vector similarity search, and real-time data processing, which empower developers to efficiently manage extensive datasets, expedite machine learning processes, and create quicker AI applications. Furthermore, its seamless integration with the Azure OpenAI Service ensures that AI tasks are optimized for speed, scalability, and critical mission applications, positioning it as a premier option for developing advanced, intelligent systems. This combination of features not only supports current technology needs but also prepares businesses for future innovations in artificial intelligence.

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.

Weaviate serves as an open-source vector database that empowers users to effectively store data objects and vector embeddings derived from preferred ML models, effortlessly scaling to accommodate billions of such objects. Users can either import their own vectors or utilize the available vectorization modules, enabling them to index vast amounts of data for efficient searching. By integrating various search methods, including both keyword-based and vector-based approaches, Weaviate offers cutting-edge search experiences. Enhancing search outcomes can be achieved by integrating LLM models like GPT-3, which contribute to the development of next-generation search functionalities. Beyond its search capabilities, Weaviate's advanced vector database supports a diverse array of innovative applications. Users can conduct rapid pure vector similarity searches over both raw vectors and data objects, even when applying filters. The flexibility to merge keyword-based search with vector techniques ensures top-tier results while leveraging any generative model in conjunction with their data allows users to perform complex tasks, such as conducting Q&A sessions over the dataset, further expanding the potential of the platform. In essence, Weaviate not only enhances search capabilities but also inspires creativity in app development.

Introducing a cutting-edge Postgres data platform designed for operators, developers, data engineers, and AI innovators, it supports vital workloads from the edge to the core. The platform offers versatile deployment options across hybrid and multi-cloud environments. EDB Postgres AI stands out as the pioneering intelligent data platform capable of handling transactional, analytical, and emerging AI workloads, all driven by an advanced Postgres engine. Whether deployed as a managed cloud service, self-managed software, or a dedicated physical appliance, it provides essential features such as integrated observability, AI-powered assistance, and migration tools, allowing for streamlined management of hybrid data environments. By connecting analytical and AI systems more closely with essential operational and transactional data, EDB Postgres AI transforms data infrastructure into a key technological asset, all while utilizing the widely embraced open-source database, Postgres. Customers can seamlessly transition from outdated systems thanks to robust Oracle compatibility and a comprehensive set of migration tools designed to facilitate their onboarding process. This innovative platform not only enhances operational efficiency but also empowers organizations to leverage their data strategically for future growth.

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.

Amazon Keyspaces, an advanced database service compatible with Apache Cassandra, offers a scalable and highly available solution that is fully managed. This service allows you to seamlessly execute your Cassandra workloads on AWS using the same application code and developer tools that are already in your toolkit. There is no need for you to provision, patch, or oversee servers, nor to install or manage any software. Operating on a serverless model, Amazon Keyspaces ensures that you only pay for the resources you utilize, with the ability to automatically adjust table capacity in alignment with application demand. You can develop applications capable of handling thousands of requests every second while benefiting from almost limitless throughput and storage options. Amazon Keyspaces empowers you with the performance, flexibility, and essential enterprise features necessary for managing critical Cassandra workloads effectively at scale. Additionally, it supports rapid data processing for applications that demand extremely low latency, making it ideal for scenarios such as industrial equipment maintenance and trade monitoring, ensuring that your business operations remain efficient and responsive.

TopK is a cloud-native document database that runs on a serverless architecture. It's designed to power search applications. It supports both vector search (vectors being just another data type) as well as keyword search (BM25 style) in a single unified system. TopK's powerful query expression language allows you to build reliable applications (semantic, RAG, Multi-Modal, you name them) without having to juggle multiple databases or services. The unified retrieval engine we are developing will support document transformation (automatically create embeddings), query comprehension (parse the metadata filters from the user query), adaptive ranking (provide relevant results by sending back "relevance-feedback" to TopK), all under one roof.

txtai is a comprehensive open-source embeddings database that facilitates semantic search, orchestrates large language models, and streamlines language model workflows. It integrates sparse and dense vector indexes, graph networks, and relational databases, creating a solid infrastructure for vector search while serving as a valuable knowledge base for applications involving LLMs. Users can leverage txtai to design autonomous agents, execute retrieval-augmented generation strategies, and create multi-modal workflows. Among its standout features are support for vector search via SQL, integration with object storage, capabilities for topic modeling, graph analysis, and the ability to index multiple modalities. It enables the generation of embeddings from a diverse range of data types including text, documents, audio, images, and video. Furthermore, txtai provides pipelines driven by language models to manage various tasks like LLM prompting, question-answering, labeling, transcription, translation, and summarization, thereby enhancing the efficiency of these processes. This innovative platform not only simplifies complex workflows but also empowers developers to harness the full potential of AI technologies.

While generative AI is a relatively recent development, our efforts over the last five years have paved the way for this moment. Deep Lake merges the strengths of data lakes and vector databases to craft and enhance enterprise-level solutions powered by large language models, allowing for continual refinement. However, vector search alone does not address retrieval challenges; a serverless query system is necessary for handling multi-modal data that includes embeddings and metadata. You can perform filtering, searching, and much more from either the cloud or your local machine. This platform enables you to visualize and comprehend your data alongside its embeddings, while also allowing you to monitor and compare different versions over time to enhance both your dataset and model. Successful enterprises are not solely reliant on OpenAI APIs, as it is essential to fine-tune your large language models using your own data. Streamlining data efficiently from remote storage to GPUs during model training is crucial. Additionally, Deep Lake datasets can be visualized directly in your web browser or within a Jupyter Notebook interface. You can quickly access various versions of your data, create new datasets through on-the-fly queries, and seamlessly stream them into frameworks like PyTorch or TensorFlow, thus enriching your data processing capabilities. This ensures that users have the flexibility and tools needed to optimize their AI-driven projects effectively.

SkySQL is a MySQL-compatible and MariaDB serverless DBaaS platform offering businesses a seamless, scalable, and cost-efficient solution for database management. Its serverless architecture automatically adjusts resources to demand, allowing for instant scaling and zero waste during idle times. With integrated SkyAI DBA And Developer Copilot agents for performance optimization and SkyDBA fractional DBA services for expert database maintenance, businesses can achieve operational efficiency while focusing on innovation. Additionally, SkySQL provides integrated AI Agents for text-to-SQL queries and improved security, ensuring that data access remains streamlined and secure. Perfect for developers, it offers a forever-free tier to get started without upfront costs.

ScyllaDB serves as an ideal database solution for applications that demand high performance and minimal latency, catering specifically to data-intensive needs. It empowers teams to fully utilize the growing computing capabilities of modern infrastructures, effectively removing obstacles to scaling as data volumes expand. Distinct from other database systems, ScyllaDB stands out as a distributed NoSQL database that is completely compatible with both Apache Cassandra and Amazon DynamoDB, while incorporating significant architectural innovations that deliver outstanding user experiences at significantly reduced costs. Over 400 transformative companies, including Disney+ Hotstar, Expedia, FireEye, Discord, Zillow, Starbucks, Comcast, and Samsung, rely on ScyllaDB to tackle their most challenging database requirements. Furthermore, ScyllaDB is offered in various formats, including a free open-source version, a fully-supported enterprise solution, and a fully managed database-as-a-service (DBaaS) available across multiple cloud platforms, ensuring flexibility for diverse user needs. This versatility makes it an attractive choice for organizations looking to optimize their database performance.

Marqo stands out not just as a vector database, but as a comprehensive vector search engine. It simplifies the entire process of vector generation, storage, and retrieval through a unified API, eliminating the necessity of providing your own embeddings. By utilizing Marqo, you can expedite your development timeline significantly, as indexing documents and initiating searches can be accomplished with just a few lines of code. Additionally, it enables the creation of multimodal indexes, allowing for the seamless combination of image and text searches. Users can select from an array of open-source models or implement their own, making it flexible and customizable. Marqo also allows for the construction of intricate queries with multiple weighted elements, enhancing its versatility. With features that incorporate input pre-processing, machine learning inference, and storage effortlessly, Marqo is designed for convenience. You can easily run Marqo in a Docker container on your personal machine or scale it to accommodate numerous GPU inference nodes in the cloud. Notably, it is capable of handling low-latency searches across multi-terabyte indexes, ensuring efficient data retrieval. Furthermore, Marqo assists in configuring advanced deep-learning models like CLIP to extract semantic meanings from images, making it a powerful tool for developers and data scientists alike. Its user-friendly nature and scalability make Marqo an excellent choice for those looking to leverage vector search capabilities effectively.

ClusterControl is a multi-cloud hybrid database ops orchestration platform that supports MongoDB and Elasticsearch as well as Redis, TimescaleDB and SQL Server on Linux. It also supports Galera Cluster, PostgreSQL and MySQL in cloud and on-premises environments. It can handle all aspects of the lifecycle, including deployment, failover, backup, and more. It allows organizations to implement the Sovereign DBaaS model with its full suite of databases and ops features. ClusterControl is the ideal solution for organizations who need to run large-scale, open source database operations reliably but don't want to be restricted by traditional DBaaS providers. It allows you to choose your environment, stability of licenses, and gives you DB access.

Postgres now features open-source vector similarity search capabilities. This allows for both exact and approximate nearest neighbor searches utilizing L2 distance, inner product, and cosine distance metrics. Additionally, this functionality enhances the database's ability to manage and analyze complex data efficiently.

IBM Cloud Databases serve as open source data repositories tailored for the development of enterprise applications. Leveraging a Kubernetes-based architecture, these databases support serverless application development. They are engineered to enhance storage and computing capabilities effortlessly, free from the restrictions typically imposed by single server environments. Fully integrated within the IBM Cloud console, they offer a unified approach to consumption, pricing, and user interaction. The goal is to deliver a streamlined experience for developers, encompassing features such as access control, backup orchestration, encryption key management, and comprehensive auditing, monitoring, and logging functionalities. This cohesive framework not only enhances usability but also ensures that developers can focus on building innovative solutions without worrying about underlying infrastructure constraints.

Effortlessly create and oversee AI applications without transferring your data through intricate pipelines or specialized vector databases. You can seamlessly connect AI and vector search directly with your existing database, allowing for real-time inference and model training. With a single, scalable deployment of all your AI models and APIs, you will benefit from automatic updates as new data flows in without the hassle of managing an additional database or duplicating your data for vector search. SuperDuperDB facilitates vector search within your current database infrastructure. You can easily integrate and merge models from Sklearn, PyTorch, and HuggingFace alongside AI APIs like OpenAI, enabling the development of sophisticated AI applications and workflows. Moreover, all your AI models can be deployed to compute outputs (inference) directly in your datastore using straightforward Python commands, streamlining the entire process. This approach not only enhances efficiency but also reduces the complexity usually involved in managing multiple data sources.

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.

Stratoscale empowers IT departments to function as internal cloud service providers, facilitating the on-demand and single-click acquisition of resources along with managed open-source technologies, thus enabling a response time comparable to that of public cloud services. Their multi-cloud hybrid platform offers a genuine infrastructure-as-a-service experience, making it particularly beneficial for DevOps practices and Infrastructure-as-Code automation. With a unified panel for monitoring and scaling all resources and services, as well as automating the complete lifecycle of open-source technologies, IT teams can concentrate more on innovation tailored to business needs, as well as on governance and compliance. APIs are the most effective method for utilizing open-source technologies; however, the varying APIs across different cloud environments create added complexity, which hinders DevOps professionals from achieving their full capabilities. Stratoscale addresses this challenge by providing both Infrastructure-as-a-Service (IaaS) and Database-as-a-Service (DBaaS), simplifying the management and deployment of applications across various platforms. This innovative approach allows organizations to harness the power of cloud technologies while streamlining their operational efficiency.

Aiven takes the reins on your open-source data infrastructure hosted in the cloud, allowing you to focus on what you excel at: developing applications. While you channel your energy into innovation, we expertly handle the complexities of managing cloud data infrastructure. Our solutions are entirely open source, providing the flexibility to transfer data between various clouds or establish multi-cloud setups. You will have complete visibility into your expenses, with a clear understanding of costs as we consolidate networking, storage, and basic support fees. Our dedication to ensuring your Aiven software remains operational is unwavering; should any challenges arise, you can count on us to resolve them promptly. You can launch a service on the Aiven platform in just 10 minutes and sign up without needing to provide credit card information. Simply select your desired open-source service along with the cloud and region for deployment, pick a suitable plan—which includes $300 in free credits—and hit "Create service" to begin configuring your data sources. Enjoy the benefits of maintaining control over your data while leveraging robust open-source services tailored to your needs. With Aiven, you can streamline your cloud operations and focus on driving your projects forward.

Accelerate your application development and ease database management with the premier hybrid multi-cloud DBaaS that supports Microsoft SQL Server, Oracle, EDB, PostgreSQL, MySQL, MariaDB, and MongoDB. Integrate databases seamlessly into your software development process while minimizing administrative tasks and ensuring optimal performance, resilience, and security for development, testing, and production environments. Provision, clone, and refresh databases effortlessly from development settings, allowing for greater efficiency. Automate administrative functions to maintain consistency with best practices, giving database administrators more time to focus on high-impact activities. Experience dependable performance, scalability, and security across all databases hosted on a hybrid multi-cloud framework. Facilitate collaboration for platform and operations teams by treating databases as code through REST API and Kubernetes integrations. The NDB Operator for Kubernetes empowers developers to provision, clone, and restore databases with minimal code, significantly reducing time-to-market. By leveraging these advanced capabilities, teams can enhance their workflow and drive innovation more effectively than ever before.

Azure Cosmos DB offers a fully managed NoSQL database solution tailored for contemporary application development, ensuring single-digit millisecond response times and an impressive availability rate of 99.999 percent, all supported by service level agreements. This service provides automatic, instantaneous scalability and supports open-source APIs for MongoDB and Cassandra, allowing for rapid data operations. With its turnkey multi-master global distribution, users can experience swift read and write operations from any location around the globe. Additionally, Azure Cosmos DB enables organizations to accelerate their decision-making processes by facilitating near-real-time analytics and AI capabilities on the operational data housed within the database. Furthermore, Azure Synapse Link for Azure Cosmos DB integrates effortlessly with Azure Synapse Analytics, ensuring smooth performance without necessitating data movement or compromising the efficiency of the operational data store, enhancing the overall functionality of your data strategy. This integration not only streamlines workflows but also empowers users to derive insights more efficiently.

Open-source, cloud-native distributed SQL database that allows for elastic scale and real time analytics. TiDB is supported by a wealth open-source data migration tools within the ecosystem. This allows you to choose your own vendor without worrying about lock-in. TiDB was designed to scale SQL without compromising your application. HTAP database platform which enables real-time situation analysis and decision making on transactional data. It eliminates friction between IT goals and business goals. TiDB is ACID compliant and strongly consistent. TiDB can be used as a scaled-out MySQL database using familiar SQL syntaxes. TiDB automatically shards data so you don’t have to do this manually. To scale horizontally or elastically to support your business growth, you can add new nodes. TiDB automates the ETL process, and automatically recovers from errors.

A cloud-native distributed HTAP database designed for seamless scaling and immediate analytics as a fully managed service, featuring a serverless tier that allows for the rapid deployment of the HTAP database within seconds. Scale transparently and elastically to hundreds of nodes for essential workloads without needing to modify your business logic. Leverage your existing SQL knowledge while preserving your relational structure and global ACID transactions, effortlessly managing hybrid workloads. The system comes with a powerful built-in analytics engine that enables operational data analysis without the requirement for ETL processes. Expand to hundreds of nodes while ensuring ACID compliance, all without the hassle of sharding or downtime interruptions. Data accuracy is upheld even with simultaneous updates to the same data source, making it reliable for high-demand environments. TiDB’s MySQL compatibility enhances productivity and accelerates your applications' time-to-market, while also facilitating the easy migration of data from current MySQL environments without necessitating code rewrites. This innovative solution streamlines your database management, allowing teams to focus on development rather than infrastructure concerns.

Instaclustr, the Open Source-as a Service company, delivers reliability at scale. We provide database, search, messaging, and analytics in an automated, trusted, and proven managed environment. We help companies focus their internal development and operational resources on creating cutting-edge customer-facing applications. Instaclustr is a cloud provider that works with AWS, Heroku Azure, IBM Cloud Platform, Azure, IBM Cloud and Google Cloud Platform. The company is certified by SOC 2 and offers 24/7 customer support.

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.

Google Cloud Bigtable provides a fully managed, scalable NoSQL data service that can handle large operational and analytical workloads. Cloud Bigtable is fast and performant. It's the storage engine that grows with your data, from your first gigabyte up to a petabyte-scale for low latency applications and high-throughput data analysis. Seamless scaling and replicating: You can start with one cluster node and scale up to hundreds of nodes to support peak demand. Replication adds high availability and workload isolation to live-serving apps. Integrated and simple: Fully managed service that easily integrates with big data tools such as Dataflow, Hadoop, and Dataproc. Development teams will find it easy to get started with the support for the open-source HBase API standard.

Enhance your operational efficiency by leveraging a widely-used open-source solution managed by AWS. Implement auditing and data security measures with an architecture that includes built-in certifications for both data centers and networks. Proactively identify potential threats and respond to system conditions by utilizing machine learning, alert notifications, and visualization tools. Streamline your time and resources to focus on strategic initiatives. Gain secure access to real-time search capabilities, monitoring, and analysis of both business and operational data. Amazon OpenSearch Service simplifies the process of conducting interactive log analytics, monitoring applications in real-time, and enabling website search functionalities. As an open-source, distributed search and analytics suite that evolved from Elasticsearch, OpenSearch allows for extensive data exploration. Amazon OpenSearch Service provides users with the latest releases of OpenSearch, compatibility with 19 different versions of Elasticsearch (ranging from 1.5 to 7.10), and visualization features through OpenSearch dashboards and Kibana, ensuring a comprehensive toolkit for data management. This versatile service empowers organizations to harness data insights efficiently while maintaining a robust security posture.

Metal serves as a comprehensive, fully-managed machine learning retrieval platform ready for production. With Metal, you can uncover insights from your unstructured data by leveraging embeddings effectively. It operates as a managed service, enabling the development of AI products without the complications associated with infrastructure management. The platform supports various integrations, including OpenAI and CLIP, among others. You can efficiently process and segment your documents, maximizing the benefits of our system in live environments. The MetalRetriever can be easily integrated, and a straightforward /search endpoint facilitates running approximate nearest neighbor (ANN) queries. You can begin your journey with a free account, and Metal provides API keys for accessing our API and SDKs seamlessly. By using your API Key, you can authenticate by adjusting the headers accordingly. Our Typescript SDK is available to help you incorporate Metal into your application, although it's also compatible with JavaScript. There is a mechanism to programmatically fine-tune your specific machine learning model, and you also gain access to an indexed vector database containing your embeddings. Additionally, Metal offers resources tailored to represent your unique ML use-case, ensuring you have the tools needed for your specific requirements. Furthermore, this flexibility allows developers to adapt the service to various applications across different industries.

Azure SQL Database, a member of the Azure SQL suite, is a sophisticated and adaptable relational database service designed specifically for cloud environments. It is continuously updated, ensuring you benefit from the latest advancements, including AI-driven features that enhance both performance and reliability. With serverless computing and Hyperscale storage options, resources can effortlessly adjust according to your needs, allowing you to concentrate on creating innovative applications without the stress of managing storage or resources. This fully managed SQL database simplifies the challenges of ensuring high availability, performing tuning, handling backups, and executing other essential database management tasks. You can expedite your application development on the unique cloud platform that offers evergreen SQL, utilizing up-to-date SQL Server features while remaining free from concerns about updates, upgrades, or the end of support. Customize your modern app development experience with both provisioned and serverless compute choices, ensuring flexibility and efficiency tailored to your specific needs. This way, you can unleash your creativity while relying on a robust foundation.

Couchbase Capella serves as a cloud-based database solution that enhances the storage and processing of both operational and analytical data in a JSON-friendly environment. It stands out as a superior DBaaS for transactions, search functionalities, AI applications, and edge computing, all while providing real-time analytics capabilities. Getting started with Capella is straightforward; you can utilize SQL to interact with your JSON data, and benefit from the natural language coding support offered by Capella iQ. This versatile database is engineered to handle both operational and analytic tasks, accommodating use cases such as JSON analytics, vector searches, and mobile data synchronization. Capella ensures rapid response times measured in milliseconds, all while offering exceptional price-performance metrics for any document-based Database-as-a-Service. You can easily sign up for a free trial and be operational within minutes. In addition to its impressive features, Capella is designed to address various challenges in development, architecture, and deployment, making it a smart choice for anyone looking to secure their technological future with a robust database solution.

Optimize your time and reduce expenses by simplifying data pipelines using hassle-free open source solutions. Covering everything from data ingestion to visualization, all components are seamlessly integrated, fully managed, and exceptionally reliable, ensuring your engineering team enjoys working with data. You can opt for any of DoubleCloud’s managed open source services or take advantage of the entire platform's capabilities, which include data storage, orchestration, ELT, and instantaneous visualization. We offer premier open source services such as ClickHouse, Kafka, and Airflow, deployable on platforms like Amazon Web Services or Google Cloud. Our no-code ELT tool enables real-time data synchronization between various systems, providing a fast, serverless solution that integrates effortlessly with your existing setup. With our managed open-source data visualization tools, you can easily create real-time visual representations of your data through interactive charts and dashboards. Ultimately, our platform is crafted to enhance the daily operations of engineers, making their tasks more efficient and enjoyable. This focus on convenience is what sets us apart in the industry.

Fauna is a data API that supports rich clients with serverless backends. It provides a web-native interface that supports GraphQL, custom business logic, frictionless integration to the serverless ecosystem, and a multi-cloud architecture that you can trust and grow with.

Vectorize is a specialized platform that converts unstructured data into efficiently optimized vector search indexes, enhancing retrieval-augmented generation workflows. Users can import documents or establish connections with external knowledge management systems, enabling the platform to extract natural language that is compatible with large language models. By evaluating various chunking and embedding strategies simultaneously, Vectorize provides tailored recommendations while also allowing users the flexibility to select their preferred methods. After a vector configuration is chosen, the platform implements it into a real-time pipeline that adapts to any changes in data, ensuring that search results remain precise and relevant. Vectorize features integrations with a wide range of knowledge repositories, collaboration tools, and customer relationship management systems, facilitating the smooth incorporation of data into generative AI frameworks. Moreover, it also aids in the creation and maintenance of vector indexes within chosen vector databases, further enhancing its utility for users. This comprehensive approach positions Vectorize as a valuable tool for organizations looking to leverage their data effectively for advanced AI applications.