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Top 26 Vector Database Software SaaS Companies in May 2026

As of May 2026, there are 26 SaaS companies in Vector Database Software. They have combined revenues of $20.6B and employ 13.4K people. They have raised $18.5B and serve 2M customers combined.

Vector Database Software is designed to store, manage, and retrieve high-dimensional data representations, typically in the form of vectors. These databases enable efficient similarity searches and rapid data retrieval, which are essential for applications like machine learning and artificial intelligence. Primary use cases include natural language processing, recommendation systems, and image or video search, where traditional databases fall short in handling complex data structures.

Companies
26
Revenue
$20.6B
Funding
$18.5B
Employees
13.4K

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Top Vector Database Software Companies

Showing 10 of 7 companies ranked by annual revenue.

1
Supabase

San Francisco, California, United States

Build in a weekend. Scale to billions.

Revenue
$70M
Customers
-
Year founded
2020
Funding
$495M
Team size
230
Growth
372.97%
2
KX

NY, United States

We power the time-aware data-driven decisions that enable fast-moving organizations to realize the full potential of their AI investments and outpace competitors. Our technology delivers transformational value by addressing data challenges around completeness, timeliness, and efficiency. We enable organizations to understand change over time and generate faster, more accurate insights — at any scale, and with cost efficiency. Our technology is essential to the operations of the world's top investment banks, aerospace and defense, high-tech manufacturing, healthcare and life sciences, automotive, and fleet telematics organizations.

Revenue
$59.6M
Customers
-
Year founded
1996
Funding
-
Team size
542
Growth
-
3
ZILLIZ

Redwood City, California, United States

Developer of AI-oriented intelligent data processing platform intended to improve query performance. The company's OLAP database systems are based on GPU hardware acceleration which focuses on multiple fields including finance, telecommunications, healthcare and retail, enabling its customers to reduce hardware, operation and maintenance cost by over 10 times.

Revenue
$16.2M
Customers
1K
Year founded
2016
Funding
$53M
Team size
136
Growth
-
4
Pinecone

New York, New York, United States

Pinecone is the leading vector database for building accurate and performant AI applications at scale in production. Pinecone's mission is to make AI knowledgeable. More than 5000 customers across various industries have shipped AI applications faster and more confidently with Pinecone's developer-friendly technology. Pinecone is based in New York and raised $138M in funding from Andreessen Horowitz, ICONIQ, Menlo Ventures, and Wing Venture Capital. For more information, visit pinecone.io.

Revenue
$14M
Customers
4K
Year founded
2019
Funding
$138M
Team size
127
Growth
-47.48%
5
PingCAP

Sunnyvale, California, United States

Developer of an open source distributed (HTAP) database designed to serve as a one-stop service for online transactions and analysis. The company's cloud native TiDB, is an open source distributed hybrid transactional analytical processing (HTAP) database with features that include MYSQL compatibility and distributed transaction, providing users with horizontal scalability and high availability for more versatile database management.

Revenue
$13.1M
Customers
-
Year founded
2015
Funding
$341.6M
Team size
430
Growth
26.5%
6
Weaviate

Amsterdam, Netherlands

Weaviate is a cloud-native, real-time vector database that allows you to bring your machine-learning models to scale. There are extensions for specific use cases, such as semantic search, plugins to integrate Weaviate in any application of your choice, and a console to visualize your data.

Revenue
$12.3M
Customers
-
Year founded
2019
Funding
$67.7M
Team size
104
Growth
-
7
Chroma Technology Corp.

Bellows Falls, Vermont, United States

Chroma is a leading manufacturer of highly precise optical filters using thin-film coating technology. Our reputation is built on dedicated customer service, including technical and application support. We manufacture high-performance optical filters covering a spectral range from 200-5000nm with superior durability and longevity, across many industries and applications. Our filter types include long, short, and multi-bandpass filters, notch rejection filters, neutral density filters, beamsplitters, and reflective metal mirrors. Our high-performance filters offer the highest levels of transmission and blocking of unwanted wavelengths, resulting in high signal/noise ratios. Sputtered hard coatings provide superior durability, including resistance from damage resulting from harsh environmental conditions such as heat and high humidity, as well as from scratches and handling. We’re so confident that these filters will last as long as you need them to that we offer an industry-leading lifetime warranty. Offering off-the-shelf, custom, and high-volume production products and solutions, we can design and deliver optical filters that do precisely what our customers need. All manufacturing occurs in the United States with worldwide distribution and sales support. Chroma was founded in 1991 as a 100% employee-owned company to the present day. Chroma is a certified B-Corp and is committed to being a socially responsible business. Chroma provides outstanding benefits for all employees including health care, profit sharing, paid time off, and many other benefits. Chroma demonstrates how a for-profit company can exercise capitalism to the benefit of people and the environment. Please contact us for your custom filter solution, and take advantage of our free Spectra Viewer to access hundreds of fluorochrome excitation and emission wavelengths.

Revenue
$11.4M
Customers
-
Year founded
1991
Funding
-
Team size
104
Growth
-

Inclusion Criteria

- Must efficiently store and index high-dimensional vectors. - Should support fast retrieval and similarity search capabilities. - Must provide capabilities for CRUD (Create, Read, Update, Delete) operations specifically for vector data. - Should allow for metadata filtering to enhance search results. - Not just a general-purpose database; must be optimized specifically for vector data handling.