3 Enterprise-Ready Open Source Vector Databases for Your AI Workloads

For enterprises now actively planning, building, and training their own AI models and developing AI-powered applications, the prospect of producing AI-fueled solutions with hallucinations and reliability issues is — rightfully — a big concern. The good news is that vector databases make generative AI considerably more reliable and less prone to hallucinations. The even better news is that several fully open source vector databases are especially great options for supporting AI workloads.

The good news keeps coming for enterprises exploring the open source vector database path: It isn't necessary to invest in implementing new and exotic specialized data-layer solutions to harness vector databases. Many enterprises will find that their existing infrastructure can already support AI workloads (while continuing to provide the familiar data availability, scalability, and performance they already know they can trust).

In particular, PostgreSQL (with the pgvector extension), OpenSearch, and Apache Cassandra 5.0 (with its new native vector indexing) are three completely open source technologies — no proprietary or open core solutions needed — that tick all the boxes for meeting enterprises' AI workloads requirements.

Vector Databases, LLMs, and RAG

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First, a quick primer. Vector databases utilize embedding vectors (lists of numbers) that represent the similarity among pieces of data and make it possible to plot their relationships spatially. For example, words like "plant" and "shrub" will have vector coordinates nearer to one another than the words "plant" and "car." In doing so, vector databases enable enterprises to build their own LLMs, explore particularly massive text datasets, and understand the distance among embeddings to empower search capabilities.

Vector databases and embeddings similarly empower the much-discussed process known as retrieval augmented generation (RAG), which boosts the accuracy of an LLM by fine-tuning its understanding of new information. For example, this RAG process can allow users to query documentation. It creates embeddings sourced from an enterprise's documents, translates the words into vector numbers, searches for words in the documentation that are similar to the words in the query, and retrieves the most relevant information. The RAG process then provides that data to an LLM in a format it can digest, and the LLM generates an accurate text answer for the user.

Now let's look at the three free and open source databases that enterprise teams can leverage as their intelligent data infrastructure for storing those embeddings vectors:

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pgvector

"The world's most advanced open source relational database," PostgreSQL is also one of the most widely deployed — meaning that most enterprises will already have a strong foothold in the technology. The pgvector extension turns Postgres into a high-performance vector store, offering a path of least resistance for organizations familiar with PostgreSQL to quickly stand up intelligent data infrastructure.

From a RAG and LLM training perspective, pgvector excels at enabling distance-based embedding search, exact nearest neighbor search, and approximate nearest neighbor search. Pgvector efficiently captures semantic similarities using L2 distance, inner product, and (the OpenAI-recommended) cosine distance. Teams can also harness OpenAI's embeddings model — available as an API — to calculate embeddings for documentation and user queries. As an enterprise-ready open source option, pgvector is an already-proven solution for achieving efficient, accurate, and performant LLMs, helping equip teams to confidently launch differentiated and AI-fueled applications into production.

OpenSearch

Because OpenSearch is a mature search and analytics engine already popular with a wide swath of enterprises, new and current users will be glad to know that the open source solution is ready to up the pace of AI application development as a singular search, analytics, and vector database. OpenSearch has long offered low latency, high availability, and the scale to handle tens of billions of vectors while backing stable applications. It provides great nearest-neighbor search functionality to support vector, lexical, and hybrid search and analytics. These capabilities significantly simplify the implementation of AI solutions, from generative AI agents to recommendation engines with trustworthy results and minimal hallucinations.

Cassandra 5.0

The newest version of the highly popular open source Apache Cassandra database introduces several new features built for AI workloads. It now includes Vector Search and Native Vector indexing capabilities. Additionally, there is a new vector data type specifically for saving and retrieving embedding vectors, and new CQL functions for easily executing on those capabilities. By adding these features, Cassandra 5.0 has emerged as an especially ideal database for intelligent data strategies and for enterprises rapidly building out AI applications across myriad use cases. In short, Cassandra's earned reputation for delivering high availability and scalability now adds AI-specific functionality, making it one of the most enticing open source options for enterprises.

Open Source Opens the Door to Successful AI Workloads

Clearly, given the tremendously rapid pace at which AI technology is advancing, enterprises cannot afford to wait to build out differentiated AI applications. But in this pursuit, engaging with the wrong proprietary data-layer solutions — and suffering the pitfalls of vendor lock-in or simply mismatched features — can easily be (and, for some, already is) a fatal setback. By instead tapping into one of the very capable open source vector databases available, enterprises can put themselves in a more advantageous position.