Vector databases provide an efficient solution for storing and retrieving large amounts of vector data, allowing for the development of retrieval augmented generation (RAG) systems at scale. With dozens of vector stores available, it can be difficult to choose the one that will perform best for your company. This is where Mano's enterprise version comes in: we make a data-driven decision based on your companies data, thanks to our plug-and-play architecture. In this blog post, we explore the most popular vector databases and evaluate them based on:
Our initial exploration in the realm of vector databases led us to Milvus, a highly respected entity in this field. With its robust, multi-layered architecture, Milvus has garnered significant attention on GitHub, thereby solidifying its position among the most popular vector databases.
At the heart of Milvus lies a design that is both intricate and comprehensive. Its default configuration deploys a substantial number of pods, demonstrating an impressive degree of scalability and resilience. However, this also implies a significant demand for resources, which, while appropriate for certain scenarios, proved to be somewhat excessive for our specific requirements.
Despite the undeniable strengths of Milvus, including its high performance, scalable architecture, and robust community support, it might to be a more extensive tool than what your particular situation necessitates.
Our investigation subsequently led us to Pinecone, a fully managed vector database that is highly regarded for its proficiency in managing unstructured search engine requirements. Pinecone sets itself apart with its user-friendly features and streamlined operations, as demonstrated in the recent 2.0 release.
The most notable enhancement in this latest release was the implementation of single-stage filtering. This groundbreaking feature significantly simplifies data querying, enabling users to extract pertinent data more efficiently, without the necessity for multiple filtering stages. This unique characteristic undoubtedly contributes value, particularly for teams in pursuit of streamlined and efficient data management.
However, despite Pinecone's high scores on most of our key considerations - such as performance, scalability, and data persistence - it did not meet our expectations in a few critical areas. Firstly, Pinecone operates as a proprietary paid solution, rather than an open-source platform. Secondly, Pinecone does not offer a self-hosted option. This is a pivotal requirement for many organizations that care about data privacy and governance.
Our exploration led us to the next milestone, Qdrant, a vector database entirely constructed in Rust. Upon delving deeper into our research, it became abundantly clear that Qdrant stands as a formidable competitor in the vector database sphere.
A distinguishing feature of Qdrant is its dynamic query planning. This attribute facilitates more efficient query processing, leading to expedited retrieval of pertinent information. The payload data indexing feature also emerged as a significant highlight, enhancing data access speed and refining search capabilities.
Qdrant's Scalar Quantization feature is another noteworthy element. Frequently cited in discussions and articles, this feature is recognized for its substantial contribution to performance and efficiency. It accomplishes this by diminishing the size of stored vectors while preserving their unique characteristics, resulting in optimized resource utilization.
Qdrant's ease of container operation is a major pro. This feature facilitates seamless deployment and management of Qdrant within a Kubernetes environment, proving particularly advantageous for teams utilizing container orchestration systems.
As a relatively new database offering superior performance, Qdrant presents an appealing option for enterprises looking to embed massive amounts of data.
Chroma is an open source vector database built to provide developers and organizations of all sizes with the resources they need to build large language model (LLM) applications. It gives developers a highly-scalable and efficient solution for storing, searching, and retrieving high-dimensional vectors.
One of the reasons Chroma has become so popular is its flexibility. You have the option to deploy it on the cloud or as an on-premise solution. It also supports multiple data types and formats, allowing it to be used in a wide range of applications. It works particularly well with audio data, making it one of the best vector database solutions for audio-based search engines, music recommendations, and other audio-related use cases.
The robustness and reliability of PostgreSQL, which has earned it a solid reputation among many businesses. As such, a lot of customers are often interested in leveraging PG Vector in their RAG architecture. PGVector which is the extension built directly into postgres, offers significant advantages when it comes to storing additional metadata for filtering. Postgres gives you access controls, user consent management, and audit trails out of the box which are essential elements for GDPR compliance. This ensures that personal data is handled with care, protected from unauthorized access, and allows you to meet the strict data protection standards set forth by the GDPR.
Choosing the right vector database is a critical decision in RAG systems, since it significantly impacts the efficiency and effectiveness of your applications and employees. In general, here are the five factors you should consider:
Even when looking at the above factors, remember that the best vector database for you ultimately depends on your specific needs and circumstances. Please reach out to founder@usemano.com, if you are interested in deploying on-prem RAG solutions.
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