By Omar Khaled Ahmed and Ahmed Elyamany

Sept 16, 2024

4 min read

Open-Source vs. Proprietary LLM Vector Databases

While we are building AI Solutions for Sales Enablement and GTM Optimization, we repetitively find ourselves facing the same debate and question. Is this LLM vector database the right one for the current business-scalaple solution in hand, Versus. maybe could we reuse that quick VDB we used for that small research task? Or could be the sort of problem where a proprietary vector databases could be a better choice? This is a field that is evolving very fast, and the answer to this involves several considerations.

• Open-source databases are cost-effective, flexible, and enjoy wider community support, regular updates, and enhanced security through transparent code. They also avoid vendor lock-in, offering long-term flexibility.

• Conversely, as mentioned in this Sabrepc post, proprietary databases provide comprehensive support and enterprise-level features. They offer user-friendly interfaces, easy deployment, and specific compliance guarantees, making them suitable for companies needing ready-to-use solutions. Proprietary options also ensure stability in product roadmaps and long-term planning.

Top 3 Open-Source LLM Vector Databases for our choice

Based on the above criteria, our decisions usually lean towards the open-source LLM Vector databases for our research heavy business projects for the advantages highlighted. Let’s then go over some of the most widely adopted and highest performer open-source vector databases to show how we select the right one for each problem.

Milvus

Milvus is an open-source vector database developed by Zilliz, designed to manage massive amounts of vector data for AI and machine learning applications. It excels in handling high-dimensional data, offering fast and scalable similarity searches. Milvus supports various indexing methods like IVF, HNSW, and ANNOY, ensuring optimized performance for different use cases.

Milvus also integrates seamlessly with popular data processing frameworks such as TensorFlow, PyTorch, and Kubernetes. Its robust community and active development ensure continuous improvements and support.

• Milvus is completely free to self-host and can be linked to cloud storage.

Weaviate

Weaviate is an open-source vector database built by SeMI Technologies, tailored for natural language processing and semantic search. It uses machine learning to understand the context of unstructured data, providing precise and relevant search results.

Weaviate offers a GraphQL API for easy data integration and manipulation, making it accessible for developers. It also includes built-in modules for various use cases, such as text, image, and video search, with the ability to create custom modules. Weaviate’s focus on context and semantics sets it apart in the realm of vector databases.

• Weaviate is completely free to self-host, but there is a cost per dimension to connect cloud storage or use Weviate’s cloud service.

Qdrant

Qdrant is an open-source vector database developed for real-time, high-performance search and recommendation systems. It is optimized for handling high-dimensional vectors and provides fast, accurate similarity searches.

Qdrant supports filtering and hybrid search, combining vector similarity with traditional search criteria. The database offers a user-friendly REST API, making it easy to integrate with various applications. With a focus on performance and scalability, Qdrant is ideal for dynamic, large-scale data environments.

• Qdrant is completely free to self-host, but there is a cost per month to connect cloud storage or use Qdrant’s cloud, hybrid cloud, or private cloud service.

Comparison Table

Scalability Vs. Control: LLM Vector Database Recommendations for Sustainable RAG Application

1. For Large-Scale Production with Minimal Operational Overhead:
   • Pinecone is our recommendation if you prioritize scalability and ease of use and don’t want to manage infrastructure. It’s a fully managed service with seamless scaling and high performance, making it ideal for enterprise-level RAG applications.

1. For Open-Source Flexibility and Control:
   • Milvus or Qdrant are good choices if you prefer open-source solutions where you have control over the infrastructure. Milvus provides strong scalability and performance for large-scale applications, while Qdrant offers flexibility and is slightly easier to manage at smaller scales.

1. For Combining Vector Search with Knowledge Graphs:
   • Weaviate is the ideal choice if you need semantic search combined with knowledge graph capabilities. It’s a powerful option for applications that need reasoning in addition to standard vector search. It is ideal if you need both structured and unstructured search in your application.

LLM Vector Database Recommendations based on project and team Size

• For Large-Scale, Production-Ready Vector Search:
  • Milvus is arguably the best choice for large-scale applications that need to handle many records, as it is highly scalable, performance-optimized, and open-source. It is well-suited for enterprise-level deployments and offers a range of indexing methods for different needs.
• For Teams Looking for an Open-Source, Metadata-Rich, and Extensible Option:
  • Qdrant is a great alternative, especially if you need flexible metadata filtering and want a less complex setup compared to Milvus. It’s a good choice for teams looking to balance scalability and usability in RAG applications.
  • Weaviate is unique in its ability to integrate semantic search with knowledge graphs, making
• For Small to Mid-Sized Projects or Quick Prototyping:
  • Chroma rises as one of the the easiest to set up and use VDBs, especially in Python-native environments, but it may not be the best option if you’re dealing with extremely large datasets or need sophisticated scalability.
• For Performance-Critical Applications with Advanced Infrastructure:
  • Finally, FAISS offers extreme speed and flexibility, but it’s not ideal for easily scalable, distributed systems out of the box. It’s best for research-heavy environments or highly optimized projects where performance tuning is essential.

Final Verdict

For scalable applications handling many records, we would recommend Milvus or Qdrant over FAISS and Chroma because they provide the necessary scalability, distribution, flexibility, and performance. They are open-source enjoying active development community and relatively easier to manage compared to FAISS. Weaviate can be considered if your application benefits from integrating knowledge graphs.

References:

Vector Database Benchmarks – Qdrant

Best vector database to use with RAG

Milvus vs Qdrant: Vector Database Performance Comparison

Top 3 Open-Source Vector Database to Use for RAG | SabrePC Blog