向量数据库
SaaS Browser 跟踪 106 向量数据库 家软件公司，其中 1 家是在过去 30 天内新增的。 向量数据库 SaaS 的主要国家包括 United States (21), United Kingdom (3), Germany (2)。
向量数据库 的关键数据点
106
全面软件1
1
新增（过去 30 天）2
3
联盟计划3
5
平均年龄（岁）4
+42
新增（过去 365 天）5
7
过去一年流失率6
14.3%
年度客户流失率7
39.6%
免费试用8
公司规模分布9
3
大型（201-1,000）
11.1% 总
6
中型（51-200）
22.2% 总
18
小型（2-50）
66.7% 总
最知名的 向量数据库 公司
| 公司 | 雇员 | 域名评分 | 创立 | 额外 |
|---|---|---|---|---|
| Pinecone | 200 | 77 | 2019 | 2025-02-11 |
| Turso | 50 | 75 | 2021 | 2025-02-10 |
| Weaviate | 200 | 71 | 2019 | 2025-02-04 |
| Jina AI | 50 | 68 | 2020 | 2025-02-11 |
| Tavily | 200 | 67 | 2024 | 2025-01-23 |
| SkyVector | 10 | 67 | 2005 | 2025-02-28 |
| KDB-X | 1,000 | 66 | 1996 | 2025-02-07 |
| Zilliz | 200 | 65 | 2017 | 2025-02-12 |
| Vectary | 50 | 62 | 2014 | 2025-02-26 |
| Dragonfly | 50 | 59 | 2022 | 2025-02-28 |
向量数据库 SaaS 排名前十的国家/地区
根据我们对 106 软件产品的数据，以下国家/地区代表了大多数 向量数据库 SaaS 公司的总部所在地。
| 国家 | 数数 | 占总数的百分比 |
|---|---|---|
| United States | 21 | 19.8% |
| United Kingdom | 3 | 2.8% |
| Germany | 2 | 1.9% |
| Ireland | 1 | 0.9% |
| Israel | 1 | 0.9% |
| Netherlands | 1 | 0.9% |
| Serbia | 1 | 0.9% |
| Brazil | 1 | 0.9% |
| South Africa | 1 | 0.9% |
| Canada | 1 | 0.9% |
向量数据库 的 SEO 和域名权重
平均域名评分10
17
与 14 的总体平均值相比
向量数据库 的定价数据
平均起价（美元）12
$10/月
平均最高价格（美元）13
$84/月
向量数据库 的市场饱和度
该评分由产品总数、域名权威性、引荐域名、公司成立时间、流量增长、定价和企业影响力等因素综合计算得出。
饱和度评分14
28 / 100 (低的)
向量数据库 中的联盟计划
3 向量数据库 SaaS 公司提供联盟计划（占总数的 2.8%）。
平均佣金15
25%
佣金类型分配
一度
1
再次发生的
1
最新添加 向量数据库 SaaS
WarmMemory
添加了 2026-10-13
Vector Data Loader
添加了 2026-09-24
WoodFireERD
添加了 2026-09-22
CarDefiner
添加了 2026-09-22
AtherGraph
添加了 2026-09-13
OntoBricks
添加了 2026-09-11
Multimode Mind
添加了 2026-08-27
Sirius Memory
添加了 2026-08-11
Vectornosis
添加了 2026-08-09
VectorLens
添加了 2026-08-05
向量数据库 SaaS 公司的平均年龄
平均而言，向量数据库 SaaS 公司成立于 5.0 年前（平均成立年份：2021）。
向量数据库 的 B2B / B2C 细分16
24
无论是B2B还是B2C
22.6%
81
企业（B2B）
76.4%
1
消费者（B2C）
0.9%
基于 106 家拥有消费者类型数据的公司（占总数的 100.0%）。
向量数据库 的 SaaS 发现趋势
向量数据库 的月度 SaaS 发现数量与其占所有已发现 SaaS 的比例对比。比例上升表明该细分市场的增长速度快于整体市场。
探索 向量数据库 SaaS
显示过滤器
高级过滤器
十
This lightweight database solution is designed to seamlessly scale across millions of instances, enabling the development of AI agents, intelligent applications, and on-device functionalities. Key features include native vector search capabilities, asynchronous design for responsiveness, support for concurrent writes, and full compatibility with existing SQLite systems, addressing the challenges of data management in the evolving landscape of AI and multi-tenant applications, making it ideal for developers and organizations focused on building next-generation software.
Vectary is an online platform that allows users to create and manage interactive 3D and augmented reality designs directly in the browser, without the need for downloads or coding. It offers powerful features for modeling, collaboration, and sharing, making it suitable for designers, engineers, and marketers across various industries.
定价：
$19-$99/mo
此SaaS产品的月度定价范围。价格尽可能以月度金额为准。
AI-powered Virtual Avatars & Digital Assistants
此列表的类别。
Zilliz offers a powerful vector database designed for enterprise-level AI applications, providing both a fully-managed cloud service and an open-source option. With features like high performance, scalability, and seamless integration, it simplifies the process of building and deploying vector search applications.
SkyVector provides tools for flight planning and access to aeronautical charts. It features DROTAMs, which are notices about areas where drones can operate, helping pilots stay informed about unmanned aerial system activities.
Vector Databases
此列表的类别。
Tavily offers a specialized Search API designed to enhance AI applications with real-time, accurate information optimized for Large Language Models (LLMs) and Retrieval Augmented Generation (RAG). With seamless integration and a user-friendly setup, Tavily empowers developers to transform vast amounts of unstructured web data into actionable insights, ensuring reliable and precise data for informed decision-making.
Dragonfly is an advanced, high-performance in-memory data store designed to handle massive workloads with ultra-low latency and high throughput, making it ideal for real-time applications such as media streaming, financial data analysis, advertising bidding, and machine learning feature storage. It offers key features including multi-threaded architecture for efficient resource utilization, seamless compatibility with popular APIs like Redis, Valkey, and Memcached, native support for search and probabilistic data structures, robust security and management capabilities, and scalability that reduces infrastructure costs while supporting millions of queries per second, serving developers, data engineers, and enterprise solutions seeking reliable, fast, and scalable data caching and real-time processing.
This technology provides a high-performance, serverless solution for vector searches, enabling users to efficiently query unstructured data using standard SQL without the need for complex infrastructure management. Key features include seamless integration with S3 for direct data ingestion, robust SQL support with extensions, and the ability to scale to petabyte-sized datasets, making it ideal for developers and data scientists seeking to perform advanced analytics and semantic searches on large volumes of data.
Lantern Cloud is an open-source Postgres vector database designed for developers to create AI applications easily. It offers features like vector search, embedding generation, and seamless integration with existing SQL workflows, making it suitable for both small projects and large-scale production environments.
Agentset is an open-source platform designed for developers to create high-quality Retrieval Augmented Generation (RAG) applications easily. It offers powerful features like high accuracy, deep research capabilities, and automatic citation of sources, making it a valuable tool for efficient information retrieval and processing.
This advanced infrastructure facilitates the transformation of diverse data types, such as images, videos, and documents, into precise vector representations, enabling AI agents to perform sophisticated reasoning and semantic searches. Key features include high-quality vectorization, powerful semantic search capabilities, and layered search workflows, which collectively address challenges in data retrieval and content identification, making it ideal for developers and organizations seeking to enhance their AI systems with domain-specific knowledge.
Vector Databases
此列表的类别。
VectorChord (vchord) is a PostgreSQL extension that enables fast and efficient vector similarity searches, making it ideal for building AI applications. It offers advanced features like better filtering, hybrid search, and complete SQL support, ensuring high performance and scalability for large datasets.
MonkDB is an AI-native unified database that combines various data types, including vector, time-series, and geospatial, into one powerful platform. It simplifies data management for enterprises, enabling faster development and real-time insights while ensuring high performance and security.
Vector Databases
此列表的类别。
KDB.AI offers a powerful vector database designed for AI, enabling advanced querying through a combination of literal, semantic, and time series searches. Users can explore various features, including hybrid search and metadata filtering, to gain deeper insights from their data.
Vector Databases
此列表的类别。
Supavec is an open-source platform that allows developers to create powerful Retrieval-Augmented Generation (RAG) applications using any data source. It offers flexibility, privacy, and scalability, making it a strong alternative to proprietary systems like Carbon.ai.
Virtual Diamond Boutique (VDB) offers a comprehensive suite of technology solutions designed to enhance the jewelry industry, catering to both B2B and B2C markets. With features such as customizable jewelry builders, advanced sales tools, and seamless integrations for online retail, VDB empowers businesses to streamline operations, expand their reach, and elevate the customer experience.
A distributed, in-memory graph database designed for managing complex hierarchical and semantic data structures, providing high-performance storage, querying, and version control through a model-based architecture. Its main features include RDF knowledge graph support, controlled document APIs with JSON-LD-like syntax, advanced query languages such as GraphQL and Datalog, and git-for-data collaboration capabilities that facilitate branching, merging, and synchronization; it effectively solves challenges related to managing evolving semantic content, maintaining data provenance, and enabling collaborative development in data-centric environments. This system is ideal for data scientists, knowledge engineers, semantic web developers, and organizations needing sophisticated graph data infrastructures for building semantic content ecosystems, knowledge graphs, and data mesh architectures.
Vector Databases
此列表的类别。
VectorCat is a data catalog and AI solution tailored for the biotech and pharmaceutical industries, focusing on making data easy to find and manage. It integrates various data sources, automates processes, and enhances knowledge extraction, all while ensuring security and customization to fit existing systems.
Vector Databases
此列表的类别。
RedVector offers online training and continuing education for various industries, helping professionals stay compliant and reduce risk. With over 1,500 courses available, individuals and companies can enhance skills, renew licenses, and improve workforce performance at their own pace.
Vector Databases
此列表的类别。
A comprehensive vector embedding service streamlines the process of transforming and managing large volumes of data for artificial intelligence applications. Key features include a technology-agnostic system for embedding data from diverse sources, automatic retries for reliability, and a user-friendly API that simplifies integration, making it ideal for AI developers, enterprises, and organizations seeking efficient solutions for semantic search, question answering, and automated classification.
Vector Databases
此列表的类别。
This sophisticated document database is engineered to optimize AI workloads by integrating native vector search, time travel, and automatic tiered storage, enabling seamless semantic similarity queries, historical data retrieval, and cost-efficient data management. Key features include compatibility with MongoDB drivers, built-in HNSW and DiskANN indexes for efficient vector similarity matching, Raft consensus for high availability with rapid failover, and the ability to automatically tier data between hot and cold storage, providing operational simplicity and significant cost reductions. It addresses challenges such as managing large-scale AI training and inference data, maintaining data consistency and durability, reducing infrastructure expenses, and simplifying integration for developers working with complex machine learning pipelines, data scientists, and organizations seeking scalable, reliable, and cost-effective AI-native database solutions.
Vector Databases
此列表的类别。
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常见问题解答
有多少家 向量数据库 型 SaaS 公司？
SaaS Browser 截至 November 2026 已跟踪 106 向量数据库 个软件产品。过去 30 天内新增了 1 家 SaaS 公司。
每个月新增多少家 向量数据库 SaaS 公司？
过去 30 天内，SaaS Browser 新增了 1 家 SaaS 公司。我们专有的爬虫基础设施会持续发现新的软件产品。
最成熟的 向量数据库 SaaS 公司有哪些？
按域名权威性排名，最成熟的 向量数据库 SaaS 公司包括 Pinecone, Turso, Weaviate, Jina AI, Tavily。这些公司在 向量数据库 类别中代表着最高的域名排名得分。
向量数据库 SaaS 公司的典型规模是多少？
在拥有员工数据的 向量数据库 家 SaaS 公司中，规模分布为： - 3 (11.1%), - 6 (22.2%), - 18 (66.7%)。
向量数据库 家 SaaS 公司的平均域名权威性是多少？
向量数据库 SaaS 公司的平均域名评分为 17，而 SaaS 公司的整体平均评分为 14。
哪些国家拥有最多的 向量数据库 SaaS 公司？
向量数据库 SaaS 的主要国家是：United States (21), United Kingdom (3), Germany (2), Ireland (1), Israel (1)。这些国家代表了全球 向量数据库 软件公司最集中的地区。
向量数据库 家 SaaS 公司中，有多少百分比提供联盟营销计划？
3 向量数据库 SaaS 公司提供联盟计划，占 SaaS Browser 跟踪的所有 向量数据库 软件的 2.8%。 平均佣金率为 25%。
向量数据库 SaaS 公司的平均年龄是多少？
平均而言，向量数据库 SaaS 公司成立于 5.0 年前，平均成立年份为 2021 年。
向量数据库 SaaS 公司主要面向企业还是面向消费者？
根据现有数据，向量数据库 SaaS 公司细分如下：无论是B2B还是B2C (22.6%), 企业（B2B） (76.4%), 消费者（B2C） (0.9%)。
最新的 向量数据库 SaaS 公司有哪些？
最新发现的 向量数据库 SaaS 公司包括 WarmMemory, Vector Data Loader, WoodFireERD, CarDefiner, AtherGraph。SaaS Browser 使用专有的爬虫基础设施不断发现和索引新的软件产品。
关于 SaaS 浏览器
SaaS Browser 是互联网上最大、最新的 SaaS 数据库，是 SaaS 公司进行客户开发和研究的终极工具。
我们会在新兴工具发布数周后就发现它们，甚至早于它们出现在 Apollo、ZoomInfo 或其他数据库中。我们专门打造的平台只追踪真实软件产品，并提供经过验证的数据，这与充斥着代理机构、咨询公司和过时信息的通用产品信息搜寻工具截然不同。
从财富 500 强企业到本周刚刚起步的自筹资金创业公司——只要是 SaaS，我们都会追踪。
这些数字是如何计算的
- 全面软件. 当前为此选择列出的已发布 SaaS 公司数量。仅统计通过我们自动化质量检查的公司。
- 新增（过去 30 天）. 过去 30 天内为此选择净新增发布的 SaaS 公司数量。
- 联盟计划. 运营经过验证的联盟或推荐计划的已收录 SaaS 公司数量。
- 平均年龄（岁）. 此选择中公司的年龄中位数（以年计），基于成立年份。中位数是中间值，因此一半更年长，一半更年轻。
- 新增（过去 365 天）. 过去 365 天内为此选择发布的 SaaS 公司数量。
- 过去一年流失率. 在过去一年内发布但已不再列出的公司——它们已下线、被移除，或不再通过我们的质量检查。
- 年度客户流失率. 年度流失率——已流失公司占过去一年内所有已发布公司（仍在列加上已流失）的百分比。
- 免费试用. 此选择中提供免费试用的已收录公司所占百分比。
- 公司规模分布. 按员工人数划分的公司分布：独立（1）、小型（2–50）、中型（51–200）、大型（201–1,000）、企业级（1,000+）。百分比基于拥有员工数据的公司。
- 平均域名评分. 已收录公司的域名评分（0–100）中位数，是我们根据 Common Crawl 网页图谱衡量链接权威度的指标。数值越高越强。展示时与所有 SaaS 的整体中位数进行对比。
- 平均起价（美元）. 此选择中拥有定价数据的公司的入门级月度价格（USD）中位数。
- 平均最高价格（美元）. 此选择中拥有定价数据的公司的高级月度价格（USD）中位数。
- 饱和度评分. 0–100 的市场饱和度评分，综合产品总数、域名权威性、引荐域名、公司成立时间、流量增长、定价和企业影响力。数值越高表示竞争越激烈。
- 平均佣金. 此选择中运营联盟计划的公司所提供的联盟佣金率中位数。
- B2B/B2C细分. 在拥有消费者类型数据的公司中，按目标市场——企业（B2B）、消费者（B2C）或两者兼有——划分的公司细分。
