Alternatives to Vald

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.

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.

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.

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.

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.

Chroma is an AI-native, open-source embedding system. Chroma provides all the tools needed to embeddings. Chroma is creating the database that learns. You can pick up an issue, create PRs, or join our Discord to let the community know your ideas.

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.

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

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.

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.

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).

Vectara offers LLM-powered search as-a-service. The platform offers a complete ML search process, from extraction and indexing to retrieval and re-ranking as well as calibration. API-addressable for every element of the platform. Developers can embed the most advanced NLP model for site and app search in minutes. Vectara automatically extracts text form PDF and Office to JSON HTML XML CommonMark, and many other formats. Use cutting-edge zero-shot models that use deep neural networks to understand language to encode at scale. Segment data into any number indexes that store vector encodings optimized to low latency and high recall. Use cutting-edge, zero shot neural network models to recall candidate results from millions upon millions of documents. Cross-attentional neural networks can increase the precision of retrieved answers. They can merge and reorder results. Focus on the likelihood that the retrieved answer is a probable answer to your query.

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.

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.

Vectorize is an open-source platform that transforms unstructured data to optimized vector search indices. This allows for retrieval-augmented generation pipelines. It allows users to import documents, or connect to external systems of knowledge management to extract natural languages suitable for LLMs. The platform evaluates chunking and embedding methods in parallel. It provides recommendations or allows users to choose the method they prefer. Vectorize automatically updates a real-time pipeline vector with any changes to data once a vector configuration has been selected. This ensures accurate search results. The platform provides connectors for various knowledge repositories and collaboration platforms as well as CRMs. This allows seamless integration of data in generative AI applications. Vectorize also supports the creation and update of vector indexes within preferred vector databases.

Create a natural language interface to your data. NLP is the heart of modern enterprise data processing. We provide developers the tools they need to quickly and efficiently build NLP systems that are ready for production. Our open-source framework allows for API-driven, scalable NLP application architectures. We believe in sharing. Our software is open-source. We value our community and make modern NLP accessible, practical, scalable, and easy to use. Natural language processing (NLP), a branch in AI, allows machines to interpret and process human language. Companies can use human language to interact and communicate with data and computers by implementing NLP. NLP is used in areas such as semantic search, question answering (QA), conversational A (chatbots), text summarization and question generation. It also includes text mining, machine translation, speech recognition, and text mining.

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.

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.

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.

Use user feedback and semantic relevance to reliably retrieve optimal document chunks for your retrieval-augmented generation system. In your search system, combine semantic relevance with document freshness because recent results are more accurate. Create a personalized ecommerce feed in real-time using user vectors based on the SKU embeddings that were viewed by the user. A vector index in your warehouse can be used to discover behavioral clusters among your customers. Use spaces to build your indices, and run queries all within a Python Notebook.

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.

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.

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.

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.

A user-friendly interface will help you improve your embedding metadata, and embedding tokens. Apply advanced NLP cleaning techniques such as TF-IDF to normalize and enrich your embedded tokens. This will improve efficiency and accuracy for your LLM applications. Optimize relevance of content returned from vector databases by intelligently splitting and merging content based on structure, adding void or invisible tokens to make chunks more semantically coherent. Embedditor can be installed locally on your PC, in your enterprise cloud or on premises. Embedditor's advanced cleansing techniques can help you save up to 40% in embedding costs and vector storage by filtering out non-relevant tokens such as stop-words and punctuation.

Vector search can give you a competitive edge. Streamline your AI/ML workflows, reduce costs and stay ahead with up to a 10x faster time-to market. ApertureDB’s unified multimodal management of data will free your AI teams from data silos and allow them to innovate. Setup and scale complex multimodal infrastructure for billions objects across your enterprise in days instead of months. Unifying multimodal data with advanced vector search and innovative knowledge graph, combined with a powerful querying engine, allows you to build AI applications at enterprise scale faster. ApertureDB will increase the productivity of your AI/ML team and accelerate returns on AI investment by using all your data. You can try it for free, or schedule a demonstration to see it in action. Find relevant images using labels, geolocation and regions of interest. Prepare large-scale, multi-modal medical scanning for ML and Clinical studies.

Zeta Alpha is the best Neural Discovery Platform to AI and beyond. You and your team can use state-of the-art NeuralSearch to improve the way you and others discover, organize, and share knowledge. Modern AI can help you make better decisions, avoid reinventing your wheel, and make it easier to stay in the know. The most up-to-date neural discovery across all relevant AI research sources and engineering information sources. With a seamless combination search, organization, recommendation, you can ensure that nothing is left behind. You can improve decision-making and reduce risks by having a single view of all relevant information, both internal and external. Get a clear view of what your team is reading or working on.

Zevi is a site-search engine that uses natural language processing (NLP), and machine learning (ML), to better understand users' search intent. Zevi uses its ML models to produce the most relevant search results instead of relying on keywords. They have been trained using vast amounts of multilingual data. Zevi is able to deliver highly relevant results regardless of search query, providing users with an intuitive search experience that minimizes cognitive load. Zevi also allows website owners to create personalized search results, promote specific search results based upon different criteria, and use search data to inform business decisions.

Substrate is a platform for agentic AI. Elegant abstractions, high-performance components such as optimized models, vector databases, code interpreter and model router, as well as vector databases, code interpreter and model router. Substrate was designed to run multistep AI workloads. Substrate will run your task as fast as it can by connecting components. We analyze your workload in the form of a directed acyclic network and optimize it, for example merging nodes which can be run as a batch. Substrate's inference engine schedules your workflow graph automatically with optimized parallelism. This reduces the complexity of chaining several inference APIs. Substrate will parallelize your workload without any async programming. Just connect nodes to let Substrate do the work. Our infrastructure ensures that your entire workload runs on the same cluster and often on the same computer. You won't waste fractions of a sec per task on unnecessary data transport and cross-regional HTTP transport.

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.

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.

Atlas integrates with your workflow by organizing text, embedding datasets and creating interactive maps that can be explored in a web browser. To understand your data, you don't need to scroll through Excel files or log Dataframes. Atlas automatically analyzes, organizes, and summarizes your documents, surfacing patterns and trends. Atlas' pre-organized data interface makes it easy to quickly identify and remove any data that could be harmful to your AI projects. You can label and tag your data, while cleaning it up with instant sync to your Jupyter notebook. Although vector databases are powerful, they can be difficult to interpret. Atlas stores, visualizes, and allows you to search through all your vectors within the same API.

Jina Search makes it easy to search for any topic in seconds. It's faster and more accurate than traditional search engines. Our AI search captures all information stored in images, text, and provides you with the most comprehensive results. Jina Search unlocks the power of search to revolutionize how you find what your looking for. Classical Search was unable to retrieve relevant results because not all items in the dataset had the right label. Jina Search does not rely on tags and was able to find better items. Make the most of state-of-the art ML models that can work with multiple data types, including images and text. All customizations are maintained by Elasticsearch. Jina Search will automatically recognize each image in your database and store it accordingly.

Semantee, a managed database that is easy to configure and optimized for semantic searches, is hassle-free. It is available as a set REST APIs that can be easily integrated into any application in minutes. It offers multilingual semantic searching for applications of any size, both on-premise and in the cloud. The product is significantly cheaper and more transparent than most providers, and is optimized for large-scale applications. Semantee also offers an abstraction layer over an e-shop's product catalog, enabling the store to utilize semantic search instantly without having to re-configure its database.

Businesses and developers can now create cutting-edge neural searches, generative AI and multimodal services using state of the art LMOps, LLOps, and cloud-native technology. Multimodal data is everywhere. From tweets to short videos on TikTok to audio snippets, Zoom meeting records, PDFs containing figures, 3D meshes and photos in games, there's no shortage of it. It is powerful and rich, but it often hides behind incompatible data formats and modalities. High-level AI applications require that one solve search first and create second. Neural Search uses AI for finding what you need. A description of a sunrise may match a photograph, or a photo showing a rose can match the lyrics to a song. Generative AI/Creative AI use AI to create what you need. It can create images from a description or write poems from a photograph.

You can access countless corporate documents and systems, regardless of platform, and keep track millions of data. A combination of state-of-the art neural search techniques, enterprise search functionality, and many standard connectors create a new search experience. INTERGATOR Cloud can also be hosted by a German hoster. This allows you to comply with all requirements of German and European law, especially data protection. We adapt to your needs. INTERGATOR Cloud is easily scaleable to meet your search needs. You can search your company data anywhere in the world, and access information without complicated VPN solutions. Natural Language Processing (NLP), and neural networks are used to train models that can extract the most important information from documents and data, and then consider the entire information stock. This comprehensive solution provides you with the best information and knowledge management.

A true second brain for knowledge-based work. Orchard is an AI assistant that can converse with you and understand complex requests. Orchard Classic is still the best AI editor for text editing. Ask questions about your documents from wherever they are located. Neural search across all your documents + synthesis using AI = The best way to learn from your work. A text editor that can finish your sentences and suggest related ideas, based on your institutional knowledge. AI text editing is now contextually aware. Orchard should be your personal analyst, able to understand you and your work. Orchard will determine if and how it can use the information it has about you each time you submit a request. It's almost as if ChatGPT had cited sources that included resources relevant to your work. Orchard is able to break down complex tasks much more accurately than ChatGPT. Orchard creates a search engine that can find all your data. Orchard is being integrated with businesses.

Sinequa is an intelligent enterprise search that connects workers in the digital workplace to the information, expertise, and insights they require to do their jobs. It can handle large and complex data volumes and ensure compliance in even the most challenging environments. Employees can access relevant information and insights to increase innovation and customer responsiveness. Intelligent search empowers people to do their jobs more effectively, which results in significant cost savings. Employees can get insights from their work context to help them comply with regulations quickly and reduce financial and reputational risk. Sinequa’s Neural Search provides the most sophisticated engine for discovering enterprise information assets available on the market today. By combining state-of-the-art deep learning language models with the best NLP and statistical techniques, employees and customers spend less time searching for information and more time developing insights to drive decisions and solutions.

With just a few lines, you can integrate natural language understanding and generation into the product. The Cohere API allows you to access models that can read billions upon billions of pages and learn the meaning, sentiment, intent, and intent of every word we use. You can use the Cohere API for human-like text. Simply fill in a prompt or complete blanks. You can create code, write copy, summarize text, and much more. Calculate the likelihood of text, and retrieve representations from your model. You can filter text using the likelihood API based on selected criteria or categories. You can create your own downstream models for a variety of domain-specific natural languages tasks by using representations. The Cohere API is able to compute the similarity of pieces of text and make categorical predictions based on the likelihood of different text options. The model can see ideas through multiple lenses so it can identify abstract similarities between concepts as distinct from DNA and computers.

The complete platform for all aspects of research. {Instantly retrieve and wrangle the insights you need, no matter your source of unstructured data.|No matter what source of unstructured data, you can instantly retrieve and extract the insights that you need.} Find answers from millions of public sources like SEC Filings, Earnings Calls and expert network transcripts. Or leverage your firm's expertise. Hebbia can instantly connect to any source of unstructured information in your company, and can ingest any file type or API. You can work faster by using tools for diligence and research processes, regardless of the task. With a single click, you can spread financials, find public comps or structure unstructured information. Hebbia is trusted with the most sensitive data by some of the world's most powerful financial institutions and governments. Security is our core. Hebbia is the only encrypted search engine available on the market.

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.

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.

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.

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.

dbForge Index Manager for SQL Server is a user-friendly tool designed to help database specialists detect and resolve index fragmentation issues. It gathers index fragmentation statistics, displays detailed information in a visual interface, identifies indexes in need of maintenance, and provides recommendations for addressing these issues. Key Features: - Detailed information about all database indexes - Customizable thresholds for rebuilding and reorganizing indexes - Automatic resolving index fragmentation issues - Generation of scripts for index rebuilding and reorganization with options to save and reuse them - Exporting index analysis results as detailed reports - Scanning multiple databases for fragmented indexes - Efficient index analysis with sorting and searching through - Task automation for regular index analysis and defragmentation via the command-line interface dbForge Index Manager integrates seamlessly into Microsoft SQL Server Management Studio (SSMS), allowing users to quickly master its functionality and incorporate it into their workflows.

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.

*GraphDB allows the creation of large knowledge graphs by linking diverse data and indexing it for semantic search. * GraphDB is a robust and efficient graph database that supports RDF and SPARQL. The GraphDB database supports a highly accessible replication cluster. This has been demonstrated in a variety of enterprise use cases that required resilience for data loading and query answering. Visit the GraphDB product page for a quick overview and a link to download the latest releases. GraphDB uses RDF4J to store and query data. It also supports a wide range of query languages (e.g. SPARQL and SeRQL), and RDF syntaxes such as RDF/XML and Turtle.

Voldemort does not have a relational database. It doesn't attempt to satisfy arbitrary relationships while also satisfying ACID properties. It is not an object database that attempts transparently to map object reference graphs. Nor does it introduce a new abstraction such as document-orientation. It is essentially a large, distributed, persistent, fault-tolerant, hash table. This will allow applications to use O/R maps like active-record and hibernate, which will provide horizontal scaling and greater availability, but with a great loss in convenience. A system may consist of many functionally partitioned APIs or services that can manage storage resources across multiple data centres using storage systems that may be themselves horizontally partitioned. This is useful for large applications that are subject to internet-type scalability. Because all data is not in one database, it is impossible to make arbitrary in-database connections for applications in this space.

The graph database is designed for graphs up to super large scale with very low latency. We continue to work with the community to promote, popularize, and prepare the graph database. Nebula Graph allows only authenticated access through role-based access control. Nebula Graph can support multiple storage engines and the query language is extensible to support new algorithms. Nebula Graph offers low latency read/write while maintaining high throughput to simplify complex data sets. Nebula Graph's distributed, shared-nothing architecture allows for linear scaling. Nebula Graph's SQL query language is similar to SQL and can be used to address complex business requirements. Nebula Graph's horizontal scalability, snapshot feature and high availability guarantee that there will be no downtime. Nebula Graph has been used in production environments by large Internet companies such as JD, Meituan and Xiaohongshu.

NGINX Service Mesh is always free and can scale from open-source projects to a fully supported enterprise-grade solution. NGINX Service Mesh gives you control over Kubernetes. It features a single configuration that provides a unified data plan for ingress and exit management. NGINX Service Mesh's real star is its fully integrated, high-performance Data Plan. Our data plane leverages the power of NGINX Plus in order to operate highly available, scalable containerized environments. It offers enterprise traffic management, performance and scalability that no other sidecars could offer. It offers seamless and transparent load balancing and reverse proxy, traffic routing and identity as well as encryption features that are required for high-quality service mesh deployments. It can be paired with NGINX Plus-based NGINX Ingress Controller to create a unified data plan that can be managed from one configuration.