Every RAG project hits the same fork: store embeddings in Postgres with pgvector, or stand up a dedicated vector database. The honest answer for most SaaS is pgvector — it keeps your data in one place, one backup strategy, one operational surface, and scales to several million vectors with the right index. Dedicated vector databases earn their operational cost only at scale or with specialised filtering needs that Postgres handles awkwardly.
This guide covers Vector storage choices for AI features across seven sections: context, the engineering reality, the concrete requirements, implementation, common mistakes, the DACH context, and next steps.
We write from practice. Innopulse Consulting advises DACH businesses and operates its own SaaS portfolio under the same conditions we recommend — the patterns here are ones our own products depend on.
What it comes down to
Every RAG project hits the same fork: store embeddings in Postgres with pgvector, or stand up a dedicated vector database. The honest answer for most SaaS is pgvector — it keeps your data in one place, one backup strategy, one operational surface, and scales to several million vectors with the right index. Dedicated vector databases earn their operational cost only at scale or with specialised filtering needs that Postgres handles awkwardly. The practical question is what this means for a real team or product. The core fits into a few points:
- pgvector with HNSW indexing scales to millions of rows
- One datastore means one backup, one RLS model, one ops surface
- Dedicated stores win at very large scale or complex filtering
- Hybrid search needs BM25 — Postgres does this natively
The engineering reality
Building with LLMs sits at the intersection of software engineering and a probabilistic component that behaves unlike anything else in the stack. The model is non-deterministic, its behaviour changes when the provider ships an update, and its cost scales with usage rather than amortising. None of that is a reason to avoid it — it is a reason to apply more engineering discipline, not less. The patterns that work treat the model as an untrusted, metered, versioned dependency: abstracted behind an interface, observed in production, evaluated on every change, and fenced off from anything it should not be able to reach. Teams that skip this discipline ship impressive demos that degrade quietly in production.
The concrete requirements
At the centre of Vector storage choices for AI features sit the following points. Each carries direct consequences for architecture, process, or cost:
- pgvector with HNSW indexing scales to millions of rows
- One datastore means one backup, one RLS model, one ops surface
- Dedicated stores win at very large scale or complex filtering
- Hybrid search needs BM25 — Postgres does this natively
- Embedding dimension affects index size and query speed
- Re-embedding on model change is a migration to plan for
Implementation in practice
Moving from theory to practice follows a clear path. For Vector storage choices for AI features, a three-phase approach works:
- Assessment (1-2 weeks): map the current state, identify stakeholders, name the biggest gaps or risks honestly.
- Design (2-4 weeks): define the target state, assign ownership, specify the technical and organisational measures.
- Implementation and operation (ongoing): build, measure, adjust. Most initiatives fail not at the start but in the absence of phase three.
Common mistakes
The same mistakes recur in practice:
- treating Vector storage choices for AI features as a one-time project rather than an ongoing discipline
- choosing tools before understanding the process
- ignoring the DACH context and copying US templates unchanged
- deferring documentation until it has to be produced under pressure
- measuring success by activity rather than outcome
The DACH context
Switzerland, Germany, and Austria differ in law and market reality. Switzerland often sits outside the EU regimes but is bound in practice through market access and data flows; Germany implements most strictly; Austria follows EU standards closely. A business operating in all three builds to the strictest common denominator and adapts regional details deliberately rather than by accident.
Next steps
The pragmatic entry into Vector storage choices for AI features is an honest assessment: where are we, where do we want to be, and what are the three highest-impact next steps? Innopulse Consulting works with DACH businesses on exactly these questions — from analysis through design to implementation. Reach us at info@innopulse.io. The first thirty minutes are free.