Explore Technology Competitors, Complementaries, Teams, and People

vector databases

Last updated May 21, 2025, generated by Sumble

Explore more →

What is vector databases?

A vector database is a type of database that stores data as high-dimensional vectors. These vectors represent the features or characteristics of the data points. Vector databases are commonly used for similarity search, recommendation systems, and other applications where finding data points that are similar to a given query is important. They enable efficient similarity searches by utilizing specialized indexing techniques to quickly find the nearest neighbors of a query vector.

Find 1.5k organizations using vector databases on Sumble →

What other technologies are related to vector databases?

vector databases Competitor Technologies

Pinecone is a managed vector database service, directly competing with other vector database offerings.

FAISS (Facebook AI Similarity Search) is a library for efficient similarity search and clustering of dense vectors. It is an alternative for some vector database functionalities.

Weaviate is an open-source vector search engine, making it a direct competitor to other vector database solutions.

Chroma is an open-source embedding database, placing it as a direct competitor to other vector database solutions.

Milvus is an open-source vector database designed for similarity search and AI applications. This makes it a direct competitor.

Number of organizations that mention technology

vector databases

ⓘ Tap on a tech to explore matching organizations

vector databases Complementary Technologies

Retrieval-Augmented Generation (RAG) is a technique that enhances LLMs by retrieving relevant information from a knowledge source before generating a response. Vector databases are often used as a key component in RAG systems for efficient information retrieval.

LangChain is a framework for developing applications powered by language models. It facilitates the integration of vector databases into LLM-powered applications, making it a complementary technology.

Large Language Models (LLMs) benefit greatly from vector databases. Vector databases allow for efficient storage and retrieval of embeddings, which are crucial for LLMs to understand and generate contextually relevant responses.

Number of organizations that mention technology

vector databases

ⓘ Tap on a tech to explore matching organizations

Which job functions commonly mention vector databases?

1.0% of all AI Engineer jobs mention vector databases

View 892 jobs on Sumble

Machine Learning

0.3% of all Machine Learning jobs mention vector databases

View 315 jobs on Sumble

0.2% of all Data Scientist jobs mention vector databases

View 532 jobs on Sumble

Data, Analytics & Machine Learning

0.1% of all Data, Analytics & Machine Learning jobs mention vector databases

View 1.8k jobs on Sumble

Software Engineer

896 Software Engineer jobs mention vector databases

View 896 jobs on Sumble

See more or filter by date, location, industry, etc →

Which organizations are mentioning vector databases?

Microsoft

Scientific and Technical Services

26 team
mention vector databases

16 people
use vector databases

HubSpot

Scientific and Technical Services

15 team
mention vector databases

2 people
use vector databases

Oracle

Scientific and Technical Services

14 team
mention vector databases

14 people
use vector databases

Ernst & Young

Professional Services

14 team
mention vector databases

12 people
use vector databases

JPMorgan Chase

Finance and Insurance

13 team
mention vector databases

8 people
use vector databases

See more or filter by date, location, industry, etc →

Summary powered by

Find the right accounts, contact, message, and time to sell

Whether you're looking to get your foot in the door, find the right person to talk to, or close the deal — accurate, detailed, trustworthy, and timely information about the organization you're selling to is invaluable.

Use Sumble to:

Sign in to continue exploring

or

Book a call to discuss your needs