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:
- Data characteristics: Are you embedding short product titles, full documents, or multimodal data like images?
- Scale trajectory: Will you store thousands, millions, or billions of vectors?
- Write vs. read patterns: Do embeddings update constantly (live user behavior) or remain mostly static (knowledge base)?
- Latency requirements: Does your application demand sub-100ms responses or is one second acceptable?
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 actual 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), and 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. That's roughly 12KB per vector. Scale that to one million documents and you're looking at 12GB just for raw vectors — before indexing, replication, or overhead.
The good news? Two optimization techniques can dramatically reduce these costs:
Precision reduction: Store dimensions as float16 instead of float32. You lose some decimal precision, but for most enterprise applications, the difference is negligible. Storage: cut in half.
Dimensionality reduction: Modern embedding models let you choose fewer dimensions. Using 512 instead of 3,072 means each vector is 6x 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.
Consider 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, custom re-ranking to incorporate metadata like recency or authority, and 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: storing embeddings (storage layer) and processing queries (query layer). How these layers interact determines cost and flexibility at scale.
Coupled architectures combine both 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're paying for capacity you don't need.
Decoupled architectures separate the storage layer from the query layer, allowing independent scaling. If your embeddings grow 50x as you onboard document repositories, but queries only double, you scale storage massively while keeping query infrastructure minimal. Conversely, during a product launch with 10x query spikes but stable data, you add query capacity without touching storage.
Model entity-document relationships
Enterprise data is interconnected — documents link to customers, projects to suppliers, support tickets to products. Yet 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.
Systems with native relationship support solve this elegantly. Documents reference parent entities rather than duplicating data. Update the project once, and all queries automatically resolve to current values — no mass updates, no synchronization bugs, 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 continues converging, with search platforms adding vector capabilities and vector stores 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
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