How to Choose the Right Vector Database for Enterprise AI

Source: Dev.to

Every enterprise building LLM‑powered products, from chatbots to document‑retrieval systems, eventually faces the same question: where do we store and search embeddings efficiently?

Choosing a vector database shapes your application’s scalability, latency, and cost. The wrong choice can double query times or inflate your cloud bill. The right one becomes invisible infrastructure — quietly powering smarter search, personalization, and reasoning across your data.

This guide offers practical evaluation criteria to help you choose a vector database that fits enterprise‑scale AI.

Start with your workload, not the benchmark

Public benchmarks are tempting but often misleading. A system that dominates synthetic tests may struggle with your production data distribution.

Instead, start by mapping your actual workload across four dimensions:

Consider three contrasting scenarios:

• A product‑recommendation engine needs high‑speed retrieval at scale.
• A legal‑compliance archive prioritizes precision over raw speed.
• A security system performing real‑time identity verification can’t tolerate delays.

Designing around these specifics ensures you’re evaluating systems against your requirements — not someone else’s use case.

Understand the trade‑offs: recall, speed, and resource usage

Vector databases face a fundamental challenge: finding similar items in high‑dimensional space is computationally expensive. Unlike traditional databases that match exact values, vector search must calculate distances between thousands of dimensions — a process that becomes prohibitive at scale without optimization.

This creates a three‑way trade‑off between:

• Recall – finding all relevant results.
• Speed – query latency.
• Resource usage – memory and compute.

Higher accuracy requires more computation. Faster queries may miss semantically relevant results. Some algorithms prioritize RAM for speed; others optimize disk storage at the cost of latency.

The numbers illustrate the challenge.

Take OpenAI’s text-embedding-3-large: 3,072 dimensions at float32 precision → roughly 12 KB per vector. Scale that to one million documents and you’re looking at 12 GB just for raw vectors — before indexing, replication, or overhead.

Two optimization techniques can dramatically reduce these costs

1. Precision reduction – Store dimensions as float16 instead of float32. You lose some decimal precision, but for most enterprise applications the difference is negligible. Storage is cut in half.
2. Dimensionality reduction – Many modern embedding models let you choose fewer dimensions. Using 512 instead of 3,072 makes each vector 6× smaller, and many domain‑specific use cases see minimal performance impact.

The key is choosing a system flexible enough to tune these trade‑offs per dataset — high recall for medical diagnostics, aggressive compression for product recommendations, or balanced performance for general enterprise search.

Consider hybrid search capabilities

Pure vector search excels at semantic meaning but fails at exact matching — a critical gap in enterprise environments filled with acronyms, product codes, and technical terms.

Example: Searching for “EBITDA trends Q3 2025.”
Pure embedding search might return documents about profit margins or operating income — semantically related but missing the specific metric. Meanwhile, documents explicitly analyzing EBITDA could rank lower without sufficient semantic context.

Hybrid search solves this by combining vector similarity with traditional keyword matching. The system retrieves candidates using both methods, then merges and ranks results using weighted scores. This delivers:

• Precision when needed – exact matches for regulatory codes, SKUs, or technical specifications.
• Semantic breadth – conceptually related content that keyword search would miss.
• Configurable balance – adjustable weights between semantic and keyword signals.

Look for systems that support:

• Weighted blending of vector and keyword scores.
• Custom re‑ranking to incorporate metadata (e.g., recency, authority).
• Field‑level filtering for structured queries like “product reviews containing ‘defect’ with rating < 3 from verified purchasers.”

Evaluate architecture for scalability

Vector databases handle two core functions:

1. Storing embeddings – the storage layer (disk, SSD, or memory).
2. Processing queries – the compute layer that performs similarity search.

When assessing scalability, examine:

A well‑designed architecture lets you scale storage and compute independently, ensuring that a surge in write traffic (e.g., real‑time user embeddings) doesn’t degrade query latency for downstream services.

TL;DR checklist for enterprise vector‑database selection

• Map your workload (data type, scale, write/read ratio, latency).
• Prioritize trade‑offs (recall vs. speed vs. cost) and verify the DB lets you tune them.
• Require hybrid search if exact‑match precision matters.
• Confirm scalability: sharding, replication, independent compute/storage, and robust ops tooling.
• Test with real data – run a pilot on a representative subset before committing.

By grounding your decision in these practical criteria, you’ll pick a vector database that remains a silent, reliable backbone for every AI‑driven product your enterprise builds.

Scaling Strategies: Coupled vs. Decoupled Architectures

Coupled architectures combine storage and query functions in the same nodes. This simplicity works at smaller scales but creates challenges: if your data grows faster than query volume (or vice‑versa), you end up paying for capacity you don’t need.

Decoupled architectures separate the storage layer from the query layer, allowing independent scaling.

• If your embeddings grow 50× as you onboard document repositories, but queries only double, you can scale storage massively while keeping query infrastructure minimal.
• Conversely, during a product launch with a 10× query spike but stable data, you add query capacity without touching storage.

Modeling Entity‑Document Relationships

Enterprise data is highly interconnected—documents link to customers, projects to suppliers, support tickets to products. Many vector databases treat embeddings as isolated entities, forcing denormalization.

The problem
When you rebrand “Project Phoenix” to “Project Firebird,” you must update every related embedding individually, risking partial failures and inconsistent search results.

The solution
Systems with native relationship support let documents reference parent entities instead of duplicating data. Updating the project once automatically propagates to all queries—no mass updates, no synchronization bugs, and less storage overhead.

For enterprises managing interconnected information, native relationship support brings graph‑like capabilities to your vector database.

Conclusion: Focus on Fit, Not Hype

The “best” vector database doesn’t exist in the abstract. It’s the one whose trade‑offs align with your data characteristics, latency requirements, scale trajectory, and operational capacity.

The landscape is converging: search platforms are adding vector capabilities, and vector stores are expanding features. Long‑term winners will balance specialized performance with comprehensive functionality.

Good infrastructure becomes invisible—letting your applications shine rather than fighting database limitations. Focus on fit, not features, and choose a foundation that quietly enables the AI experiences you’re building.

Resources

• OpenAI Embeddings Documentation – Details on text-embedding-3-large and dimensional flexibility
• Understanding HNSW – Deep dive into the most common vector index algorithm
• Hybrid Search Explained – How vector and keyword search combine
• Vespa Documentation – Open‑source engine for vector search, hybrid retrieval, and scalable AI applications