Scaling RAG to 1 Billion Vectors: Taming the Search Infrastructure Without Losing Your Mind (or Wallet)

Scaling vector search to a billion vectors presents enterprises with a formidable challenge: a high-stakes balancing act impacting latency, cost, and search quality. Zeta Alpha's CTO, Fernando, dives into a detailed view of the significant technical and financial challenges involved, drawing from our direct experience building these demanding systems for enterprise applications.

The surge in GenAI and advanced RAG applications means data volumes, and consequently vector counts, are exploding. Fernando highlighted how quickly this occurs: "100 million text documents can easily translate to 1 billion vectors," and with visual data models like ColPali, "just 200,000 documents could reach the billion-vector mark." This requires strategic resource planning from day one.

Managing this billion-vector scale presents a significant financial hurdle, primarily due to the high RAM requirements of standard Approximate Nearest Neighbor (ANN) search algorithms like HNSW. For instance, a 1 billion vector system can demand over 3.5TB of RAM, translating to cloud costs exceeding $22,000 monthly. Fernando shows how this can be reduced by employing vector quantization and rescoring, a technique often called disk-based vector search. This method slashes monthly costs by 10x, before rescoring a smaller candidate set with full-precision vectors from disk for optimal accuracy. However, that’s not all. Our journey involved addressing critical nuances such as subtle bugs, performance anomalies, engine behaviors, and filter interactions.

These "practical lessons learned" are important for any organization building robust, efficient, and financially viable AI search systems at scale. Zeta Alpha's expertise in model selection, text processing, hardware essentials, and advanced embedding strategies, including fine-tuning and inference optimization, empowers enterprises to overcome these challenges.

Ready to tame your vectors and succeed at scale?

➡️ Reach out to us to discuss your enterprise AI and vector search needs.

➡️ The link to the full talk: https://www.youtube.com/watch?v=DF0LqNNFwpw