Qdrant or pgvector? The right vector database for SMEs
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pgvector is sufficient if PostgreSQL is already in use and an AI search is to run over several tens of thousands of text sections with simple filters. Qdrant is the better choice if large document inventories, many filters such as authorizations or departments, and a hybrid search of meaning and keywords are needed. Both are open source and can be operated in Germany itself.
pgvector is sufficient if you are already running PostgreSQL and need an AI search over a manageable amount of text sections with simple filters. Qdrant is the better choice for large document inventories, many filters such as authorizations and departments, and if a hybrid search of meaning and keywords is desired. Both are open source and can be operated on servers in Germany.
If you are planning an AI document search or a chatbot with company knowledge, you will quickly come across the question of the vector database. What this actually is is explained in the article Vector databases explained simply. This is about the selection.
What distinguishes the two
pgvector is an extension for the popular database PostgreSQL. Vectors are stored in a normal table next to other data and are queried with SQL. Qdrant is a standalone database written in Rust that is built exclusively for vector search and is licensed under the Apache 2.0 license.
Criterion | pgvector | Qdrant |
|---|---|---|
Art | Extension for PostgreSQL | Standalone vector database |
Query | SQL | Own interface (REST/gRPC) |
Filter | SQL-WHERE; for proximity indexes after the index scan | Filters directly in the search via metadata |
Hybrid search | Can be combined with additional effort | built-in: dense and thin vectors with fusion |
The establishment | one less database if Postgres is already running | additional service |
License | open source | Apache 2.0 |
The key point: filters
In companies, "find the most similar texts" is rarely asked, but "find the most similar texts that this person is allowed to see, from Department X, not older than two years". According to the documentation, pgvector filters for proximity indices according to the index scan, which can lead to few or no hits with strict filters; there are antidotes for this such as iterative scans or partitioning. Qdrant applies filters to metadata directly in the search. The more filters, the more likely it is to speak for Qdrant. More on this in the article Permissions in AI Search.
Hybrid search: meaning plus keyword
Pure meaning search finds "vacation entitlement" when asked for "days off" – but not reliably the item number "HX-2040". Qdrant combines dense vectors (meaning) with thin vectors (keywords) and merges the result lists, for example with Reciprocal Rank Fusion. The article Hybrid Search shows how this works.
Decision support
Your situation | Recommendation |
|---|---|
PostgreSQL is already running, a few thousand documents, hardly any filters | pgvector |
Prototype, test quickly | pgvector or the solution built into Open WebUI |
Thousands of documents from multiple sources, rights per department | Qdrant |
Article numbers, standards, technical terms must be found safely | Qdrant with hybrid search |
Growth foreseeable | Qdrant |
More important than the database
The choice of database rarely determines the quality alone. At least as important are the division of the documents into sections, the chosen embedding model and the maintenance of the data. The article Embeddings simply explained describes the connection.
We usually use Qdrant for customer projects, self-hosted in Germany, as the basis of our AI document search.
Frequently Asked Questions
What is pgvector?
An open-source extension for PostgreSQL that stores vectors and enables similarity search via SQL. It is suitable if PostgreSQL is already in use.
Is Qdrant faster than pgvector?
This cannot be said in general; it depends on the amount of data, index and filters. Qdrant's advantage lies above all in many filters and built-in hybrid search.
Can I switch from pgvector to Qdrant later?
Yes. The documents are re-embedded or the existing vectors are transferred. With a cleanly built connection, this is a manageable step.
Do I even need my own vector database?
Not necessarily for small collections – Open WebUI comes with a built-in solution. If you have a lot of documents, automatic updates and rights, it's worth having your own.
Are you planning an AI search? Let's contact page. discuss the right architecture via the
Muhamed Alahmed
With over 10 years’ experience in IT, I develop solutions that not only work from a technical perspective, but also create real added value and open up new possibilities.
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