The global vector database market is anticipated to reach a value of 20.05 billion in 2033, exhibiting a CAGR of 23.7% from 2025 to 2033. The rising adoption of artificial intelligence (AI) and machine learning (ML) technologies, particularly in the BFSI, retail and e-commerce, healthcare and life sciences, and IT and ITeS sectors, is a major driver of market growth. Furthermore, the increasing need for efficient data storage and retrieval in a variety of applications, such as natural language processing (NLP), computer vision, and recommendation systems, is further boosting market expansion. The Asia Pacific region is expected to hold a significant share of the vector database market, with key countries such as China, India, and Japan contributing to its growth. The region's burgeoning IT and ITeS sector, as well as its rapidly growing e-commerce market, are driving the demand for vector databases. Additionally, government initiatives in various countries aimed at promoting AI adoption are creating favorable conditions for market growth. The presence of major technology companies in the region, such as Alibaba Cloud, Pinecone Systems, and Zilliz, is also contributing to the market's expansion. This report provides an in-depth analysis of the Vector Database Market, a rapidly growing segment of the database industry valued at USD 1.5 billion in 2023 and projected to reach USD 10.2 billion by 2028, exhibiting a CAGR of 36.1% during the forecast period. Recent developments include: In June 2024, Salesforce, Inc. announced the general availability of the Data Cloud Vector Database, designed to help businesses unify and leverage the 90% of customer data trapped in unstructured formats, such as PDFs, emails, and transcripts. This innovation enables businesses to cost-effectively deliver transformative and integrated customer experiences across service, sales, marketing, AI, automation, and analytics , In June 2024, Oracle launched HeatWave GenAI, the first in-database large language model, scale-out vector processing, automated in-database vector store, and contextual natural language conversations informed by unstructured content. These capabilities let customers apply generative AI to enterprise data without moving data to a separate vector database or needing AI expertise , In April 2024, Vultr partnered with Qdrant, an advanced vector database technology provider, through their Cloud Alliance program to enhance cloud infrastructure and support the growing AI ecosystem. This collaboration combines Qdrant's innovative technology with Vultr's global platform, offering seamless scalability and performance for vector search workloads .

Vector Database Market size was valued at USD 2.2 Billion in 2024 and is projected to reach USD 10.4 Billion by 2032 growing at a CAGR of 21.7% from 2026 to 2032.

Global Vector Database Market Drivers

1. Growing Use of Machine Learning and AI For AI-driven applications like recommendation systems, picture recognition, and natural language processing (NLP), vector databases are essential. Demand is fueled by the growing AI-driven personalization in social media, streaming services, and e-commerce.
2. Growing Requirement for High-Performance Search and Recovery High-dimensional data is difficult for traditional relational databases to handle well. Vector search promotes adoption in search engines, fraud detection, and cybersecurity by facilitating the quick and precise retrieval of related items.

Metal–organic frameworks (MOFs) are one of the most promising hydrogen-storing materials due to their rich specific surface area, adjustable topological and pore structures, and modified functional groups. In this work, we developed automatically parallel computational workflows for high-throughput screening of ∼11,600 MOFs from the CoRE database and discovered 69 top-performing MOF candidates with work capacity greater than 1.00 wt % at 298.5 K and a pressure swing between 100 and 0.1 bar, which is at least twice that of MOF-5. In particular, ZITRUP, OQFAJ01, WANHOL, and VATYIZ showed excellent hydrogen storage performance of 4.48, 3.16, 2.19, and 2.16 wt %. We specifically analyzed the relationship between pore-limiting diameter, largest cavity diameter, void fraction, open metal sites, metal elements or nonmetallic atomic elements, and deliverable capacity and found that not only geometrical and physical features of crystalline but also chemical properties of adsorbate sites determined the H2 storage capacity of MOFs at room temperature. It is highlighted that we first proposed the modified crystal graph convolutional neural networks by incorporating the obtained geometrical and physical features into the convolutional high-dimensional feature vectors of period crystal structures for predicting H2 storage performance, which can improve the prediction accuracy of the neural network from the former mean absolute error (MAE) of 0.064 wt % to the current MAE of 0.047 wt % and shorten the consuming time to about 10–4 times of high-throughput computational screening. This work opens a new avenue toward high-throughput screening of MOFs for H2 adsorption capacity, which can be extended for the screening and discovery of other functional materials.

This repository contains databases of protein domains for use with Foldclass and Merizo-search. We provide databases for all 365 million domains in TED, as well as all classified domains in CATH 4.3.Foldclass and Merizo-search use two formats for databases. The default format uses a PyTorch tensor and a pickled list of Python tuples to store the data. This format is used for the CATH database, which is small enough to fit in memory. For larger-than-memory datasets, such as TED, we use a binary format that is searched using the Faiss library.The CATH database requires approximately 1.4 GB of disk space, whereas the TED database requires about 885 GB. Please ensure you have enough free storage space before downloading. For best search performance with the TED database, the database should be stored on the fastest storage hardware available to you.IMPORTANT:We recommend going in to each folder and downloading the files; if you attempt to download each folder in one go, it will download a zip file which will need to be decompressed. This is particularly an issue if downloading the TED database, as you will need to have roughly twice the storage space needed as compared to downloading the individual files. Our GitHub repository (see Related Materials below) contains a convenience script to download each database; we recommend using that.

The data fabric market is projected to reach USD 2.6521 billion by 2033, growing at a CAGR of 15.99% from 2025 to 2033. The increasing adoption of cloud computing, the need for data integration, and the growing volume of data are driving the growth of the market. The key market trends include the growing adoption of cloud-based data fabrics, the convergence of data management and data integration, and the increasing use of data fabrics for regulatory compliance. The major market segments include type, application, service, and vertical. The major companies operating in the market include SAP SE, Informatica, Splunk Inc., Denodo, and Syncsort Inc. Recent developments include: In November 2023, DataStax announced the development of its relationship with Amazon Web Services (AWS), focusing on new generative AI technologies and integrations across various aspects, such as go-to-market strategies, products, and technology. The partnership aims at providing generative artificial intelligence solutions to customers leading to enhanced efficiency and effective growth, improving precise generative artificial intelligence applications for a better user experience., In December 2023, Fortinet (FTNT) revealed that it had expanded its strategic partnership with Digital Realty (DLR), the largest global provider of cloud-agnostic carrier-neutral data center solutions. The company intends to accelerate the worldwide deployment of Fortinet’s Universal Secure Access Service Edge (SASE) solution which is already deployed in over one hundred locations worldwide. SASE refers to a security framework that combines network security capabilities with Wide Area Network (WAN) functions that satisfy evolving access demands securely for enterprises., In August 2022, Zilliz contributed significantly to the Milvus 2.1 release. Milvus is one of the most superior vector database systems globally capable of processing huge volumes, including ones from different origins, enabling next-generation information fabrics’ improvements., In March 2022, Vyasa released Cortex; this insightful data management tool represents the “blueprint” for information sources related to Vyasa Layar data fabrics so that users can build, manage or gain access from these links., In December 2021, SAP launched a fresh version of SAP Data Intelligence. This update contains deployment and delivery as well as metadata and governance, pipeline modeling, connectivity and integration and intelligent processing. SAP Data Intelligence is an on-premises version., In October 2021, NetApp Inc. released ONTAP data management software, a hybrid multi-cloud data management tool. It provides high-performance storage and enables public cloud integration., In July 2021, Teradata revealed a Teradata QueryGrid upgrade that included more cloud-native functionality to boost Vantage’s hybrid-multi-cloud capacity while allowing Teradata clients access to data and analytics across multiple platforms from different vendors. With new cloud-native features, it enables clients to access data and analytics spanning heterogeneous devices and public cloud providers.. Potential restraints include: Security And Data Privacy Concerns 26.