Vector Database Comparison: The Dimensions That Decide It

Most vector database comparisons focus on benchmarks that do not predict production behaviour. This guide covers what actually decides it, drawing on FISTA Solutions' AI enablement delivery work.

What should the comparison cover?

Six dimensions, weighted by your situation.

Dimension	Why it decides the choice	How to test it
Filtered search behaviour	Real queries always filter	Run your filters at your scale
Hybrid search support	Exact terms matter	Test with product codes and names
Operational burden	Ongoing cost	Rebuild an index and time it
Scale characteristics	Where it degrades	Load your real corpus size
Update and delete	Corpus changes	Test deletion and reindexing
Managed or self-operated	Team capacity	Honest assessment

Why is filtering the key dimension?

Because production queries almost always filter, and products handle it very differently.

Restricting to a user's permitted documents, to a date range, or to a category changes the search entirely. Some implementations filter after retrieving, which can return nothing relevant; others filter during, which is better but harder.

Test with your actual filter selectivity. A filter matching one percent of the corpus behaves very differently from one matching half, and benchmark datasets rarely include either. See RAG quality checklist.

Why does hybrid search matter so much?

Because business corpora are full of exact terms that semantic search handles badly.

Product codes, error identifiers, person names, and version numbers are all cases where a user means that exact string. Pure vector search returns semantically similar things, which is the wrong answer.

Combining keyword and vector retrieval, with a reranking step, consistently outperforms either alone on real corpora. Whether a product supports this well is a primary selection criterion.

Do you need a dedicated store?

Often not, and the question is worth asking first.

Several general-purpose databases now support vector indexing adequately for corpora in the hundreds of thousands of chunks. Using the database you already operate, back up, and monitor removes a whole system from your architecture.

Dedicated stores earn their place at larger scale, with demanding latency requirements, or where their filtering and hybrid capabilities are materially better. Start by testing what you already have. See database schema design guide.

What does operational burden look like?

Index rebuilds, memory footprint, backup and restore, and upgrades.

Some implementations hold the index in memory, which makes cost scale with corpus size in a way that surprises teams. Others rebuild indexes in ways that require careful capacity planning.

Test a full index rebuild at your corpus size and time it. That number tells you what a re-embedding or a recovery would cost you, and it is rarely in any documentation.

How should scale be tested?

At your actual corpus size, with your actual query distribution.

Products degrade at different points and in different ways — latency, recall, or memory. A benchmark at a million vectors says little about your hundred thousand with heavy filtering, or your ten million with simple queries.

Load your real corpus, or a representative sample scaled up, and run your real queries. It takes a day and it answers the question.

What about updates and deletion?

Frequently overlooked and operationally important.

Corpora change. Documents are added, updated, and deleted, and deletion in particular must work for retention and privacy compliance. Some implementations handle incremental updates well; others effectively require a rebuild.

Test deletion explicitly, including whether deleted vectors stop appearing in results immediately. See AI data retention checklist.

How do you run your own comparison?

Load a representative corpus, run your real query distribution with your real filters, and measure recall against known-correct results alongside latency at the tail.

Then time an index rebuild and a bulk deletion. Those two operations reveal the operational character of a product better than any throughput figure.

What does switching cost later?

Moderate. Vectors can usually be re-inserted, but re-embedding may be required if you also change the embedding model, which is a full pass over the corpus with real cost.

Keep the source content and the chunking pipeline independent of the store, so a migration is a reload rather than a rebuild. See AI data migration checklist.

What do people get wrong here?

Deciding on throughput benchmarks. Not testing filtered queries. Assuming a dedicated store is necessary. Ignoring deletion. And discovering the memory footprint after loading the real corpus.

Does the embedding model matter more than the store?

Usually, yes. Embedding quality and chunking strategy affect retrieval results more than the choice of store, which mostly affects operations and filtering.

That is an argument for spending your evaluation effort on the embedding and chunking layer first, and treating the store as an operational choice. See embedding model comparison.

Which should you choose?

Test whether your existing database is sufficient before adding a dedicated store. If it is not, choose on filtered search behaviour, hybrid support, and operational burden at your real corpus size — not on benchmark throughput.

What should you do first?

Run your real queries with your real filters against your current database's vector support. If recall and latency are adequate, you have avoided a system.

How FISTA Solutions helps

FISTA Solutions builds and operates production AI systems through AI agents, AI enablement, and forward deployed engineering: stores evaluated with real corpora and real filter selectivity rather than benchmarks, and the existing database tested before a dedicated one is added, decisions documented with their reasoning, and handover that leaves your team able to maintain what was delivered. The record is 150+ projects for 50+ companies across 12+ countries.

To run this comparison against your own workload, message FISTA on WhatsApp, or read RAG quality checklist.