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pgvector agencies in Cologne
pgvector is a PostgreSQL extension that brings vector search directly into a database you already have, without running an additional system. For companies already working with Postgres, that's often the most pragmatic way into RAG applications and semantic search. This list shows agencies with pgvector experience, filterable by case studies, minimum budget, and verified credentials.
Filled mark = core expertise, outline = additional experience. Combine with more filters →
No agency with pgvector in their stack yet in Cologne.
Guide
What sets pgvector apart from a dedicated vector database
pgvector installs directly into PostgreSQL as an extension (via the CREATE EXTENSION vector command) and is released under the PostgreSQL license, a permissive license similar to BSD or MIT. That lets you store and search vectors, numerical representations of text or images, in the same database as the rest of your business data. The advantage over a separate vector database like Qdrant or Weaviate: one system, one backup strategy, one operations team that already knows it. For many RAG projects at mid-sized companies, that's the fastest route to working search without building up new operational know-how. The downside shows up with very large data volumes or high load requirements, where specialized systems offer more performance and more search features.
Where pgvector hits its limits
At very large vector counts in the high tens of millions, or with requirements for complex filter combinations and very low response times under heavy load, dedicated vector databases show their strengths more clearly than a Postgres extension. If search load noticeably affects the rest of your database operations, splitting it off into its own system also pays off. A switch is rarely a complete restart: migrating data from pgvector to a dedicated database can usually be planned in stages if the architecture is kept cleanly separated from the start. For most mid-sized projects with a few hundred thousand to a few million entries, though, pgvector is entirely sufficient.
What a good pgvector agency should bring
Experience with indexing strategies inside Postgres matters, for example the HNSW index, which trades off search speed against accuracy. General database operations experience is just as important: backups, scaling, monitoring query times, since pgvector shares resources with the rest of your database operations. Also ask about experience combining structured filters and vector search in a single query, which is often more complex in practice than pure similarity search. A good agency will also tell you honestly at what data volume a switch to a dedicated solution makes sense, instead of keeping you on a suboptimal architecture longer than necessary.
What adding pgvector costs
Adding semantic search to an existing Postgres application usually costs €6,000 to €18,000. A complete RAG pipeline with document processing, indexing, and connection to a language model runs €20,000 to €55,000. Agencies specializing in both databases and AI charge €90 to €160 per hour.
Category, search, and next steps
The RAG agencies category gives an overview of the whole field, filtered by technology through search. At larger data volumes, it's also worth looking at dedicated solutions like Qdrant. Our methodology explains how this list is put together, and for a specific project, a short project request is enough.
Frequently asked questions
pgvector agencies: questions and answers
What is pgvector?
A PostgreSQL extension that enables vector search directly in your existing database, installed with the simple command CREATE EXTENSION vector. It lets you implement similarity search, for RAG applications or semantic search, for example, without a separate system. The extension is released under the permissive PostgreSQL license.
Do I need a dedicated vector database, or is pgvector enough?
For most mid-sized projects with a few hundred thousand to a few million entries, pgvector is entirely sufficient, especially if you're already running Postgres. Only at very large data volumes, with complex filter requirements, or under very high load with tight time constraints does a dedicated solution like Qdrant or Weaviate pay off.
What does introducing pgvector cost?
Adding semantic search to an existing Postgres application usually costs €6,000 to €18,000. A complete RAG pipeline with document processing and connection to a language model runs €20,000 to €55,000. Agencies charge €90 to €160 per hour for this work.
At what point does a dedicated vector database pay off instead of pgvector?
When search load noticeably affects the rest of your database operations, when data volume grows into the high tens of millions, or when complex filter combinations are needed alongside low response times. Until then, pgvector saves operational effort, since there's no second system to maintain.