Vector databases for generative AI applications refer to specialized data storage systems designed to efficiently handle and retrieve high-dimensional vectors, which are numerical representations of data. In generative AI, such as in models that create text, images, or audio, these vectors represent complex features like semantic meaning, visual patterns, or audio characteristics. Vector databases enable quick similarity searches, allowing AI models to retrieve and compare similar data points, which is crucial for generating accurate and contextually relevant outputs. This capability is essential for scaling AI applications, as it enhances the model's ability to learn from and generate data more effectively.
The global Vector Databases for Generative AI Applications market was valued at US$ 242 million in 2023 and is anticipated to reach US$ 593 million by 2030, witnessing a CAGR of 13.6% during the forecast period 2024-2030.
North American market for Vector Databases for Generative AI Applications is estimated to increase from $ million in 2023 to reach $ million by 2030, at a CAGR of % during the forecast period of 2024 through 2030.
Asia-Pacific market for Vector Databases for Generative AI Applications is estimated to increase from $ million in 2023 to reach $ million by 2030, at a CAGR of % during the forecast period of 2024 through 2030.
The global market for Vector Databases for Generative AI Applications in Natural Language Processing (NLP) is estimated to increase from $ million in 2023 to $ million by 2030, at a CAGR of % during the forecast period of 2024 through 2030.
The major global companies of Vector Databases for Generative AI Applications include Zilliz Cloud, Redis, Pinecone, Weaviate, Canonical, OpenSearch, MongoDB, Elastic, Marqo, Milvus, etc. In 2023, the world's top three vendors accounted for approximately % of the revenue.
This report aims to provide a comprehensive presentation of the global market for Vector Databases for Generative AI Applications, with both quantitative and qualitative analysis, to help readers develop business/growth strategies, assess the market competitive situation, analyze their position in the current marketplace, and make informed business decisions regarding Vector Databases for Generative AI Applications.
The Vector Databases for Generative AI Applications market size, estimations, and forecasts are provided in terms of and revenue ($ millions), considering 2023 as the base year, with history and forecast data for the period from 2019 to 2030. This report segments the global Vector Databases for Generative AI Applications market comprehensively. Regional market sizes, concerning products by Type, by Application, and by players, are also provided.
For a more in-depth understanding of the market, the report provides profiles of the competitive landscape, key competitors, and their respective market ranks. The report also discusses technological trends and new product developments.
The report will help the Vector Databases for Generative AI Applications companies, new entrants, and industry chain related companies in this market with information on the revenues for the overall market and the sub-segments across the different segments, by company, by Type, by Application, and by regions.
麻豆原创 Segmentation
By Company
Zilliz Cloud
Redis
Pinecone
Weaviate
Canonical
OpenSearch
MongoDB
Elastic
Marqo
Milvus
Snorkel AI
Qdrant
Oracle
Microsoft
AWS
Deep Lake
Fauna
Vespa
Segment by Type
Memory-Based Vector Databases
Disk-Based Vector Databases
Hybrid Vector Databases
Segment by Application
Natural Language Processing (NLP)
Computer Vision
Search and Information Retrieval
Others
By Region
North America
United States
Canada
Asia-Pacific
China
Japan
South Korea
Southeast Asia
India
Australia
Rest of Asia
Europe
Germany
France
U.K.
Italy
Russia
Nordic Countries
Rest of Europe
Latin America
Mexico
Brazil
Rest of Latin America
Middle East & Africa
Turkey
Saudi Arabia
UAE
Rest of MEA
Chapter Outline
Chapter 1: Introduces the report scope of the report, executive summary of different market segments (by Type, by Application, etc), including the market size of each market segment, future development potential, and so on. It offers a high-level view of the current state of the market and its likely evolution in the short to mid-term, and long term.
Chapter 2: Introduces executive summary of global market size, regional market size, this section also introduces the market dynamics, latest developments of the market, the driving factors and restrictive factors of the market, the challenges and risks faced by companies in the industry, and the analysis of relevant policies in the industry.
Chapter 3: Detailed analysis of Vector Databases for Generative AI Applications company competitive landscape, revenue market share, latest development plan, merger, and acquisition information, etc.
Chapter 4: Provides the analysis of various market segments by Type, covering the market size and development potential of each market segment, to help readers find the blue ocean market in different market segments.
Chapter 5: Provides the analysis of various market segments by Application, covering the market size and development potential of each market segment, to help readers find the blue ocean market in different downstream markets.
Chapter 6, 7, 8, 9, 10: North America, Europe, Asia Pacific, Latin America, Middle East and Africa segment by country. It provides a quantitative analysis of the market size and development potential of each region and its main countries and introduces the market development, future development prospects, market space, and capacity of each country in the world.
Chapter 11: Provides profiles of key players, introducing the basic situation of the main companies in the market in detail, including product sales, revenue, price, gross margin, product introduction, recent development, etc.
Chapter 12: The main points and conclusions of the report.
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1 Report Overview
1.1 Study Scope
1.2 麻豆原创 Analysis by Type
1.2.1 Global Vector Databases for Generative AI Applications 麻豆原创 Size Growth Rate by Type: 2019 VS 2023 VS 2030
1.2.2 Memory-Based Vector Databases
1.2.3 Disk-Based Vector Databases
1.2.4 Hybrid Vector Databases
1.3 麻豆原创 by Application
1.3.1 Global Vector Databases for Generative AI Applications 麻豆原创 Growth by Application: 2019 VS 2023 VS 2030
1.3.2 Natural Language Processing (NLP)
1.3.3 Computer Vision
1.3.4 Search and Information Retrieval
1.3.5 Others
1.4 Assumptions and Limitations
1.5 Study Objectives
1.6 Years Considered
2 Global Growth Trends
2.1 Global Vector Databases for Generative AI Applications 麻豆原创 Perspective (2019-2030)
2.2 Global Vector Databases for Generative AI Applications Growth Trends by Region
2.2.1 Global Vector Databases for Generative AI Applications 麻豆原创 Size by Region: 2019 VS 2023 VS 2030
2.2.2 Vector Databases for Generative AI Applications Historic 麻豆原创 Size by Region (2019-2024)
2.2.3 Vector Databases for Generative AI Applications Forecasted 麻豆原创 Size by Region (2025-2030)
2.3 Vector Databases for Generative AI Applications 麻豆原创 Dynamics
2.3.1 Vector Databases for Generative AI Applications Industry Trends
2.3.2 Vector Databases for Generative AI Applications 麻豆原创 Drivers
2.3.3 Vector Databases for Generative AI Applications 麻豆原创 Challenges
2.3.4 Vector Databases for Generative AI Applications 麻豆原创 Restraints
3 Competition Landscape by Key Players
3.1 Global Top Vector Databases for Generative AI Applications Players by Revenue
3.1.1 Global Top Vector Databases for Generative AI Applications Players by Revenue (2019-2024)
3.1.2 Global Vector Databases for Generative AI Applications Revenue 麻豆原创 Share by Players (2019-2024)
3.2 Global Vector Databases for Generative AI Applications 麻豆原创 Share by Company Type (Tier 1, Tier 2, and Tier 3)
3.3 Global Key Players Ranking by Vector Databases for Generative AI Applications Revenue
3.4 Global Vector Databases for Generative AI Applications 麻豆原创 Concentration Ratio
3.4.1 Global Vector Databases for Generative AI Applications 麻豆原创 Concentration Ratio (CR5 and HHI)
3.4.2 Global Top 10 and Top 5 Companies by Vector Databases for Generative AI Applications Revenue in 2023
3.5 Global Key Players of Vector Databases for Generative AI Applications Head office and Area Served
3.6 Global Key Players of Vector Databases for Generative AI Applications, Product and Application
3.7 Global Key Players of Vector Databases for Generative AI Applications, Date of Enter into This Industry
3.8 Mergers & Acquisitions, Expansion Plans
4 Vector Databases for Generative AI Applications Breakdown Data by Type
4.1 Global Vector Databases for Generative AI Applications Historic 麻豆原创 Size by Type (2019-2024)
4.2 Global Vector Databases for Generative AI Applications Forecasted 麻豆原创 Size by Type (2025-2030)
5 Vector Databases for Generative AI Applications Breakdown Data by Application
5.1 Global Vector Databases for Generative AI Applications Historic 麻豆原创 Size by Application (2019-2024)
5.2 Global Vector Databases for Generative AI Applications Forecasted 麻豆原创 Size by Application (2025-2030)
6 North America
6.1 North America Vector Databases for Generative AI Applications 麻豆原创 Size (2019-2030)
6.2 North America Vector Databases for Generative AI Applications 麻豆原创 Growth Rate by Country: 2019 VS 2023 VS 2030
6.3 North America Vector Databases for Generative AI Applications 麻豆原创 Size by Country (2019-2024)
6.4 North America Vector Databases for Generative AI Applications 麻豆原创 Size by Country (2025-2030)
6.5 United States
6.6 Canada
7 Europe
7.1 Europe Vector Databases for Generative AI Applications 麻豆原创 Size (2019-2030)
7.2 Europe Vector Databases for Generative AI Applications 麻豆原创 Growth Rate by Country: 2019 VS 2023 VS 2030
7.3 Europe Vector Databases for Generative AI Applications 麻豆原创 Size by Country (2019-2024)
7.4 Europe Vector Databases for Generative AI Applications 麻豆原创 Size by Country (2025-2030)
7.5 Germany
7.6 France
7.7 U.K.
7.8 Italy
7.9 Russia
7.10 Nordic Countries
8 Asia-Pacific
8.1 Asia-Pacific Vector Databases for Generative AI Applications 麻豆原创 Size (2019-2030)
8.2 Asia-Pacific Vector Databases for Generative AI Applications 麻豆原创 Growth Rate by Country: 2019 VS 2023 VS 2030
8.3 Asia-Pacific Vector Databases for Generative AI Applications 麻豆原创 Size by Region (2019-2024)
8.4 Asia-Pacific Vector Databases for Generative AI Applications 麻豆原创 Size by Region (2025-2030)
8.5 China
8.6 Japan
8.7 South Korea
8.8 Southeast Asia
8.9 India
8.10 Australia
9 Latin America
9.1 Latin America Vector Databases for Generative AI Applications 麻豆原创 Size (2019-2030)
9.2 Latin America Vector Databases for Generative AI Applications 麻豆原创 Growth Rate by Country: 2019 VS 2023 VS 2030
9.3 Latin America Vector Databases for Generative AI Applications 麻豆原创 Size by Country (2019-2024)
9.4 Latin America Vector Databases for Generative AI Applications 麻豆原创 Size by Country (2025-2030)
9.5 Mexico
9.6 Brazil
10 Middle East & Africa
10.1 Middle East & Africa Vector Databases for Generative AI Applications 麻豆原创 Size (2019-2030)
10.2 Middle East & Africa Vector Databases for Generative AI Applications 麻豆原创 Growth Rate by Country: 2019 VS 2023 VS 2030
10.3 Middle East & Africa Vector Databases for Generative AI Applications 麻豆原创 Size by Country (2019-2024)
10.4 Middle East & Africa Vector Databases for Generative AI Applications 麻豆原创 Size by Country (2025-2030)
10.5 Turkey
10.6 Saudi Arabia
10.7 UAE
11 Key Players Profiles
11.1 Zilliz Cloud
11.1.1 Zilliz Cloud Company Details
11.1.2 Zilliz Cloud Business Overview
11.1.3 Zilliz Cloud Vector Databases for Generative AI Applications Introduction
11.1.4 Zilliz Cloud Revenue in Vector Databases for Generative AI Applications Business (2019-2024)
11.1.5 Zilliz Cloud Recent Development
11.2 Redis
11.2.1 Redis Company Details
11.2.2 Redis Business Overview
11.2.3 Redis Vector Databases for Generative AI Applications Introduction
11.2.4 Redis Revenue in Vector Databases for Generative AI Applications Business (2019-2024)
11.2.5 Redis Recent Development
11.3 Pinecone
11.3.1 Pinecone Company Details
11.3.2 Pinecone Business Overview
11.3.3 Pinecone Vector Databases for Generative AI Applications Introduction
11.3.4 Pinecone Revenue in Vector Databases for Generative AI Applications Business (2019-2024)
11.3.5 Pinecone Recent Development
11.4 Weaviate
11.4.1 Weaviate Company Details
11.4.2 Weaviate Business Overview
11.4.3 Weaviate Vector Databases for Generative AI Applications Introduction
11.4.4 Weaviate Revenue in Vector Databases for Generative AI Applications Business (2019-2024)
11.4.5 Weaviate Recent Development
11.5 Canonical
11.5.1 Canonical Company Details
11.5.2 Canonical Business Overview
11.5.3 Canonical Vector Databases for Generative AI Applications Introduction
11.5.4 Canonical Revenue in Vector Databases for Generative AI Applications Business (2019-2024)
11.5.5 Canonical Recent Development
11.6 OpenSearch
11.6.1 OpenSearch Company Details
11.6.2 OpenSearch Business Overview
11.6.3 OpenSearch Vector Databases for Generative AI Applications Introduction
11.6.4 OpenSearch Revenue in Vector Databases for Generative AI Applications Business (2019-2024)
11.6.5 OpenSearch Recent Development
11.7 MongoDB
11.7.1 MongoDB Company Details
11.7.2 MongoDB Business Overview
11.7.3 MongoDB Vector Databases for Generative AI Applications Introduction
11.7.4 MongoDB Revenue in Vector Databases for Generative AI Applications Business (2019-2024)
11.7.5 MongoDB Recent Development
11.8 Elastic
11.8.1 Elastic Company Details
11.8.2 Elastic Business Overview
11.8.3 Elastic Vector Databases for Generative AI Applications Introduction
11.8.4 Elastic Revenue in Vector Databases for Generative AI Applications Business (2019-2024)
11.8.5 Elastic Recent Development
11.9 Marqo
11.9.1 Marqo Company Details
11.9.2 Marqo Business Overview
11.9.3 Marqo Vector Databases for Generative AI Applications Introduction
11.9.4 Marqo Revenue in Vector Databases for Generative AI Applications Business (2019-2024)
11.9.5 Marqo Recent Development
11.10 Milvus
11.10.1 Milvus Company Details
11.10.2 Milvus Business Overview
11.10.3 Milvus Vector Databases for Generative AI Applications Introduction
11.10.4 Milvus Revenue in Vector Databases for Generative AI Applications Business (2019-2024)
11.10.5 Milvus Recent Development
11.11 Snorkel AI
11.11.1 Snorkel AI Company Details
11.11.2 Snorkel AI Business Overview
11.11.3 Snorkel AI Vector Databases for Generative AI Applications Introduction
11.11.4 Snorkel AI Revenue in Vector Databases for Generative AI Applications Business (2019-2024)
11.11.5 Snorkel AI Recent Development
11.12 Qdrant
11.12.1 Qdrant Company Details
11.12.2 Qdrant Business Overview
11.12.3 Qdrant Vector Databases for Generative AI Applications Introduction
11.12.4 Qdrant Revenue in Vector Databases for Generative AI Applications Business (2019-2024)
11.12.5 Qdrant Recent Development
11.13 Oracle
11.13.1 Oracle Company Details
11.13.2 Oracle Business Overview
11.13.3 Oracle Vector Databases for Generative AI Applications Introduction
11.13.4 Oracle Revenue in Vector Databases for Generative AI Applications Business (2019-2024)
11.13.5 Oracle Recent Development
11.14 Microsoft
11.14.1 Microsoft Company Details
11.14.2 Microsoft Business Overview
11.14.3 Microsoft Vector Databases for Generative AI Applications Introduction
11.14.4 Microsoft Revenue in Vector Databases for Generative AI Applications Business (2019-2024)
11.14.5 Microsoft Recent Development
11.15 AWS
11.15.1 AWS Company Details
11.15.2 AWS Business Overview
11.15.3 AWS Vector Databases for Generative AI Applications Introduction
11.15.4 AWS Revenue in Vector Databases for Generative AI Applications Business (2019-2024)
11.15.5 AWS Recent Development
11.16 Deep Lake
11.16.1 Deep Lake Company Details
11.16.2 Deep Lake Business Overview
11.16.3 Deep Lake Vector Databases for Generative AI Applications Introduction
11.16.4 Deep Lake Revenue in Vector Databases for Generative AI Applications Business (2019-2024)
11.16.5 Deep Lake Recent Development
11.17 Fauna
11.17.1 Fauna Company Details
11.17.2 Fauna Business Overview
11.17.3 Fauna Vector Databases for Generative AI Applications Introduction
11.17.4 Fauna Revenue in Vector Databases for Generative AI Applications Business (2019-2024)
11.17.5 Fauna Recent Development
11.18 Vespa
11.18.1 Vespa Company Details
11.18.2 Vespa Business Overview
11.18.3 Vespa Vector Databases for Generative AI Applications Introduction
11.18.4 Vespa Revenue in Vector Databases for Generative AI Applications Business (2019-2024)
11.18.5 Vespa Recent Development
12 Analyst's Viewpoints/Conclusions
13 Appendix
13.1 Research Methodology
13.1.1 Methodology/Research Approach
13.1.1.1 Research Programs/Design
13.1.1.2 麻豆原创 Size Estimation
13.1.1.3 麻豆原创 Breakdown and Data Triangulation
13.1.2 Data Source
13.1.2.1 Secondary Sources
13.1.2.2 Primary Sources
13.2 Author Details
13.3 Disclaimer
Zilliz Cloud
Redis
Pinecone
Weaviate
Canonical
OpenSearch
MongoDB
Elastic
Marqo
Milvus
Snorkel AI
Qdrant
Oracle
Microsoft
AWS
Deep Lake
Fauna
Vespa
听
听
*If Applicable.
