Top 15 Vector Databases that You Must Try in 2024!

Vector Databases are the type of databases that are designed to store, manage, and index massive quantities of high dimensional vector data efficiently. These vector databases are used to make the work easier for the machine learning models to remember the past inputs which also allows machine learning to be used for text generation, search, and recommendation.

Thus, these best vector databases also provide a particular method to operationalize the embedding models. Therefore, in this article, a detailed overview has been provided of the top 15 vector databases that can be used in 2024 by developers. Before that let’s first discuss what are vector databases.

What are Vector Databases?

Vector Databases are the particular type of databases that are designed to handle vectorized data more effectively. They are specialized in managing data points in the multidimensional space which makes them a better application in the field of Machine Learning, Natural Language Processing, and Artificial Intelligence.

The main motive of these vector databases is that they can facilitate vector embedding similarity searches and the efficient handling of high-dimensional data.

Top 15 Vector Databases you must try in 2024

There are multiple vector Databases that are used by the software developers to handle the vectorized data efficiently and to make the work easier by using particular vector database features.

Some of the best vector databases that every developer needs to try in 2024 are mentioned below:

1. Chroma

Chroma DB is one of the open source vector databases that is freely available on GitHub under the Apache License 2.0. It is tailored for the Artificial Intelligence native embedding which is mainly used to simplify the development of Large Language Model (LLM) applications powered by natural language processing (NLP). Chroma is used for providing a feature-rich environment with capabilities like density estimates, queries, filtering, and many more.

Key Features

1. Chroma is a LangChain (Python and JavaScript)
2. Similar APIs are used for testing, production, and development.
3. The codebase of Chroma is well organized and modular which makes it easy to understand by the software developers.
4. Chroma DB also offers multiple ways to store vector embeddings.

2. Pinecone

Pinecone is one of the popular vector databases. Pinecone is a cloud native vector database that mainly offers a seamless API and a hassle-free infrastructure. Pinecone also helps to eliminate the user’s need and allows the users to focus on developing and expanding their Artificial intelligence solutions, it also excels in supporting metadata filters and processing the data.

Key Features

1. The Pinecone database is used to handle the large datasets and the high query of loads.
2. Some of its features are – Duplicate detection and data search.
3. Pinecone also offers high-performance search and similarity-matching features.
4. Deduplication and rank tracking are some of its other features.

3. Deep Lake

Deep Lake is a famous Artificial Intelligence database that caters to LLM-based applications and deep learning. Therefore, Deep Lake supports storage for multiple data types and this vector database offers several features like data streaming during the training, querying, and integration with tools like the Llamalndex, LangChain, and many more.

Key Features

1. Deep Lake is integrated with various other tools.
2. Querying and vector search are some of the features of the Deep Lake vector database.
3. The Deep Lake vector database can store all the data types.
4. Data streaming during training, Data versioning, and lineage are some of its other features.

4. Vespa

Vespa is a type of open source vector database. It is a data-serving engine that is particularly designed for organizing, searching, and storing large amounts of data with machine-learned judgments. Vespa is one of the popular vector databases that excels in redundancy configuration, flexible query options, and continuous write options.

Key Features

1. Vespa acknowledged the writes in milliseconds and also it continues to write at a high rate per node.
2. Vespa supports the multiple query operators.
3. Vespa also allows storing the extracted image embedding and also allows efficient similarity searches.
4. Redundancy configuration

5. Milvus

Milvus is another famous open source vector database that is designed for efficient similarity searches and vector embedding. Milvus is used to simplify the unstructured data search and also provides a better experience across multiple deployment environments. It is one of the most popular vector database used for applications such as chatbots, chemical structure search, and image search.

Key Features

1. By using Milvus developers can search trillions of the vector datasets in milliseconds.
2. Milvus consists of simple unstructured data management.
3. This vector database is highly adaptable and scalable.
4. The Milvus database is supported by the community and it has a unified Lambda structure.

6. ScaNN

ScaNN is abbreviated Scalable Nearest Neighbors, is a type of method for searching the vector similarity at a scale more effectively. Google’s ScaNN also presented a brand new compression method which initially increases the accuracy.

Key Features

1. It also includes search space trimming and quantization for the maximum Inner Product search as well as the additional distance functions just like the Euclidean distance.
2. ScaNN offers an increase in accuracy and compression.
3. It is used for efficiently searching for vector similarity at scale.
4. It is also used to balance the efficiency and accuracy in vector search.

7. Weaviate

Weaviate is also another famous open source vector database. It is a cloud-native database that is resilient, quick, and scalable. This vector database tool is used to convert photos, text, and multiple data into a searchable vector database by using algorithms and machine learning models.

Software developers use this tool to vectorize their data during the import process which ultimately creates systems for question-and-answer extraction, categorization, and summarization.

Key Features

1. Weaviate consists of built-in modules for AI-powered searches, automated categorization, and combining LLMs and Q&A.
2. This vector database is used to seamlessly transfer machine learning models to MLOps using the database.
3. This vector database is distributed and cloud-native.
4. Weaviate operates perfectly on Kubernetes.

8. Qdrant

Qdrant is one of the best vector database which offers a production-ready service with an easy-to-use API for searching, storing, and managing the points vectors. Qdrant vector database is designed to provide extensive filtering support. The versatility of Qdrant’s makes it a good fit for semantic-based matching or neural networks.

Key Features

1. Qdrant supports a large range of query criteria and data types such as numerical ranges, text matching, geo locations, and many more.
2. The query planner is used for the cached payload information to improve the query execution.
3. The Qdrant functions are independent of the orchestration controllers or external databases.
4. Write ahead during the power outages.

9. Vald

Vald is a scalable, fast, and distributed vector search engine that employs the quickest ANN algorithm, NGT to help find neighbors. Vald mainly offers index backup, vector indexing, and horizontal scaling which allows it to search across multiple feature vector data. It is easy to use and also extremely configurable.

Key Features

1. Through the Persistent or Object Storage Vald provides automatic backups.
2. Vald helps in distributing the vector indexes to multiple agents each of which retains a unique index.
3. This vector database supports various programming languages.
4. Vald consists of a highly adaptable configuration.

10. Faiss

Faiss is another open-source vector database for fast, fast, dense vector similarity search and for grouping. It also includes several methods for searching sets of vectors for random size. Faiss vector database is based on the index type which maintains the set of vectors and also offers a function for searching through them by using L2 or dot product vector comparison.

Key Feature

1. Faiss mainly uses the greatest inner product search than the minimal Euclidean search.
2. It helps in returning all the elements within a specified radius of the query location.
3. By using Faiss users can search several vectors at once rather than just one.
4. Faiss also supports multiple distances.

11. OpenSearch

OpenSearch is another vector database that brings together the power of analytics, vector search, and classical search into a single solution. OpenSearch helps to speed up AI application development by minimizing the work that is required for the software developers to manage, operationalize, and integrate AI-generated assets.

Key Features

1. With the help of OpenSearch, users can create product and user embedding by using collaborative filtering techniques.
2. For aiding the data quality operations, the users of OpenSearch can use the similarity search to automate duplication in data and pattern matching.
3. OpenSearch is used for vector data engines, search, personalization, and data quality.
4. Semantic, gen AI agents, Multimodal, and visual search are some of its key features.

12. Pgvector

Pgvector is an extension of PostgreSQL which is used for searching the vector similarity and also used to keep the embeddings. Pgvector also helps users to store all of the application’s data in a place and the users can get the advantage from ACID compliance, JOINs, point-in-time recovery, and other features of PostgreSQL.

Key Features

1. Pgvector is used to calculate the exact and approximate nearest neighbor search.
2. It can also be used in any of the languages with the PostgreSQL client.
3. It supports the inner product, L2 distance, and cosine distance.
4. Pg vector lets the users add embedding columns to the existing tables.

13. Apache Cassandra

Apache Cassandra is an open-source NoSQL database management system that was designed to handle big volumes of data across multiple supported commodity servers while also maintaining high availability with no failure. Apache Cassandra also consists of a new data type to facilitate the storage of high-dimensional vectors which further allows for the storage and manipulation of Float32 embeddings.

Key Features

1. Apache Cassandra offers a new storage attached index (SAI) dubbed “VectorMemtableIndex”.
2. This vector database also supports the Approximate Nearest Neighbor (ANN) search capabilities.
3. It also offers a new Cassandra Query Language (CQL) operator, ANN OF, to make it easier for the users to run the ANN searches on the data.
4. Extension to the already existing SAI framework.

14. Elasticsearch

Elasticsearch is a type of open source, RESTful analytics engine that can handle geographic, numerical, unstructured and structured data which is designed to handle a large range of use cases such as helping to store the data for lightning-fast search and sophisticated analytics that scale easily.

Key Features

1. Elastic search helps in automatic node recovery and data rebalancing.
2. With the help of Elastic search users can identify the errors to keep the clusters secure.
3. Elasticsearch works on distributed architecture which was developed from the ground up.
4. High availability, clustering, and horizontal scalability are some of its features.

15. ClickHouse

ClickHouse is a column-oriented DBMS for online analytical processing that enables users to produce analytical reports in real-time by running up SQL queries. The real column-oriented DBMS design is an important part of ClickHouse and this different design provides a compact storage with no necessary data accompanying the values which further improves the performance.

Key Features

1. One of the features of ClickHouse is Data compression which mainly improves the performance of ClickHouse.
2. ClickHouse uses multi-server and multi-core setups to accelerate the massive queries.
3. ClickHouse provides Robust SQL support.
4. Efficient data compression is one of its features.

Conclusion

Nowadays the demand for vector databases is increasing due to the rise in demand for high-dimensional data. These vector databases allow the software developers to develop and innovate experiences powered by vector search. Therefore, in this article, detailed knowledge has been provided about the Vector databases and the top 15 vector databases with their features.