From 2TB to 64GB: How Predictive Modeling Transformed Vector Storage in MongoDB + Voyage A

Author(s): R. Thompson (PhD)

Originally published on Towards AI.

“Scalability isn’t magic — it’s a measurable, predictable science.”

Vector databases are often celebrated for unlocking unprecedented capabilities in semantic search, recommendation systems, and retrieval-augmented generation (RAG) applications. Yet beneath the surface, scaling them remains a complex, data-driven challenge.

As data scientists, we are responsible not just for deploying cutting-edge technology but for understanding its behaviors under pressure. Scaling is not an art. It is an engineering discipline grounded in predictive analysis.

This article treats vector database scalability as a modeling problem. You will learn how to predictively estimate storage requirements, query latencies, retrieval accuracy, and cost efficiency when scaling MongoDB Atlas Vector Search with Voyage AI embeddings.

Expect an in-depth methodological breakdown, experimental results, real-world healthcare simulations, and a blueprint you can adapt to your own AI architectures.

As applications evolve from MVPs to global-scale deployments, vector databases must maintain three critical properties:

• Fast semantic recall under high concurrency.

• Minimal infrastructure overhead to control costs.

• High accuracy retrieval even under aggressive optimizations.

Our modeling goal is to predict how the system behaves as:

• Number of vectors (N) scales from millions to hundreds of millions.

• Embedding dimensionality (D) varies (e.g., 384D, 512D, 768D).

• Quantization compressions (float32, int8, binary) are applied.

• Indexing configurations (HNSW parameters like efConstruction,… Read the full blog for free on Medium.

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Published via Towards AI