Dedicated Vector Stores vs Relational Databases With Vector Search

Quick answer: if your data already lives in a relational database and your vector count is modest, adding vector search there is usually simpler and keeps data consistent. A dedicated vector store makes sense at very large scale, with high query rates, or when you need specialized search features. Decide from measured workload and operational capacity, not from hype.

What Do Vector Search Systems Actually Do?

Vector search finds items whose embeddings are closest to a query embedding. For small collections, you can compare the query against every vector, which is exact and simple. As the collection grows, exact search becomes too slow, so systems use approximate nearest neighbor indexes that trade a little accuracy for large speed gains.

Two index families are common. Graph-based indexes offer high recall and low latency but use more memory and take longer to build. Cluster-based indexes partition vectors and search only the nearest partitions, which uses less memory but depends on tuning how many partitions are searched. Both expose parameters that trade recall against speed, so test them on your data.

Real applications rarely search vectors alone. They filter by tenant, date, category, or permissions, and often combine vector similarity with keyword search. How well a system handles filtered and hybrid queries often matters more than raw vector speed, because filters can interact badly with approximate indexes.

When Does a Relational Database Fit?

Many relational databases offer vector types and indexes, through built-in features or extensions. The main advantage is that your vectors sit next to your business data. A single query can join, filter by permissions, and rank by similarity, and a transaction can update a record and its embedding together, so they never drift apart.

• One system to operate, back up, secure, and monitor, with tools your team already knows.
• Strong consistency and transactions between metadata and embeddings.
• Rich filtering using ordinary SQL, including joins and row-level access rules.
• Lower cost for collections in the range of thousands to a few million vectors, depending on dimension and hardware.

The limits appear with scale and load. Large indexes consume a lot of memory, index builds and updates can be slow, and heavy similarity queries compete with your transactional workload for resources. A read replica dedicated to search can help, and so can partitioning, but there is a ceiling. Check the documentation for the vector features and limits of your database version.

When Does a Dedicated Vector Store Pay Off?

Dedicated systems are built around vector workloads. They typically offer more index types, better handling of very large collections, horizontal scaling, tuned filtered search, and features such as hybrid ranking, quantization to reduce memory, and managed replication. If you serve high query volumes with strict latency, or hold hundreds of millions of vectors, these capabilities can justify the extra system.

The cost is operational and architectural. You now have a second datastore that must stay in sync with your source of truth, with its own failure modes, security model, and backups. Keeping embeddings and metadata consistent across two systems requires careful pipelines, and permission rules may need duplicating. Weigh those costs honestly against the benefits.

A practical approach is to start simple and keep an abstraction layer. Put retrieval behind an interface, measure recall, latency, and cost on a realistic dataset, and migrate when numbers show the current system cannot meet requirements. Re-embedding and reindexing are manageable if your source text and pipeline are reproducible.

Frequently Asked Questions

How many vectors can a relational database handle?

It depends on dimension, hardware, index type, and query load. Many systems work well into the millions, but test with your own data.

Do I need approximate search for small datasets?

Not necessarily. For small collections, exact search may be fast enough and gives perfect recall.

Why is filtered vector search hard?

Filters can exclude many candidates found by an approximate index, lowering recall or increasing latency. Systems handle it in different ways.

Can I switch later?

Yes, if you keep source text and a reproducible embedding pipeline, and hide the store behind an interface.

Summary

Start with the database you already run when the scale allows, and move to a dedicated vector store when measurements demand it. Evaluate recall, filtering, latency, consistency, and operations together. Check documentation for current features and limits.