Table of contents
Choosing a vector database is one of the few infrastructure decisions in a RAG system that's genuinely hard to reverse cheaply once you have real data volume in production — re-indexing millions of embeddings into a different store is a real migration, not a config change. The right choice depends far more on your actual scale, latency requirements and existing infrastructure than on any single benchmark number, and the category has enough legitimate options (managed vector databases, self-hosted open-source options, and vector extensions to a database you may already run) that "just pick the most popular one" is not a reliable strategy.
This guide covers the real tradeoffs between these three categories, the dimensions worth evaluating beyond raw query speed, and a practical decision framework — distinct from the broader RAG architecture decisions covered in our [RAG development guide](/blog/rag-development-guide) and [enterprise RAG architecture guide](/blog/enterprise-rag-architecture).
- Already run Postgres?
- pgvector is often the right first choice
- Want zero ops burden
- A managed vector database
- Need maximum control/scale
- A self-hosted dedicated vector database
- Most overrated factor
- Raw query-speed benchmarks in isolation
The three categories
Postgres with a vector extension (pgvector and similar) adds vector similarity search to a database you may already be running for everything else. This is often the most pragmatic starting point: no new infrastructure to operate, your embeddings live alongside your relational data (so joins between vector search results and business data are trivial), and Postgres's operational maturity — backups, replication, monitoring — comes for free. The tradeoff is that it's not purpose-built for vector search at very large scale, and performance at tens of millions of vectors requires more careful tuning than a dedicated vector database.
Managed vector databases (a dedicated vector search service run by a vendor) trade cost and some control for near-zero operational burden — you don't manage indexing infrastructure, scaling, or uptime yourself. This is a strong fit for teams that want to move fast and don't have (or don't want to build) dedicated infrastructure expertise for vector search specifically. The tradeoff is ongoing cost that scales with data volume and query load, and your data living in a third-party service, which matters for teams with strict data-residency requirements.
Self-hosted dedicated vector databases (open-source vector databases you run yourself) give the most control — over data location, scaling behavior, and cost at very high volume — at the cost of real operational ownership: you're responsible for uptime, scaling, backups and upgrades yourself. This is usually only worth it once you have enough scale or specific data-residency requirements that the operational investment clearly pays for itself.
What actually matters beyond query speed
Query latency benchmarks dominate vector database marketing, but they're rarely the deciding factor in practice — most RAG applications' end-to-end latency is dominated by the LLM generation step, not the vector search step, so a difference of a few milliseconds in retrieval rarely matters to the user. The dimensions that actually decide most real selections are less flashy.
| Factor | Postgres + pgvector | Managed vector database | Self-hosted dedicated |
|---|---|---|---|
| Operational burden | Low — reuses existing Postgres ops | Lowest — vendor operates it | Highest — you operate everything |
| Cost model | Included in existing database cost | Usage-based, scales with data + queries | Infrastructure cost, no per-query fee |
| Data residency control | Full — your own database | Depends on vendor's regions/policies | Full — your own infrastructure |
| Best fit | Teams already on Postgres, moderate scale | Teams wanting to move fast with no ops burden | High scale or strict data-residency needs |
| Joins with relational data | Native and trivial | Requires a separate query + join step | Requires a separate query + join step |
Start simpler than you think you need to
Most RAG applications never reach the scale where a dedicated vector database's advantages over pgvector actually matter. Starting with a Postgres extension — if you're already running Postgres — gets a working RAG system to production fastest, with a clear, well-understood migration path to a dedicated option later if you genuinely outgrow it. Migrating early based on anticipated scale that doesn't materialize is a common, avoidable cost.
Migration cost is real — factor it in upfront
Moving from one vector database to another isn't just a data export/import — it typically means re-embedding your entire corpus if you're also changing embedding models at the same time, re-tuning index parameters for the new system's specific behavior, and validating retrieval quality hasn't regressed before cutting over. This is manageable at moderate data volumes and a real project at large ones, which is exactly why the initial choice deserves more deliberation than "pick whatever's trending."
A practical way to reduce this risk: keep your embedding generation and vector storage layers cleanly separated in your application code from day one, so that swapping the storage layer later doesn't require touching how embeddings are generated or how retrieval results are consumed by the rest of your RAG pipeline (see our RAG development guide for this layered architecture pattern in more depth).
Decision framework
- Already running Postgres in production? Start with pgvector unless you have a specific, current reason not to.
- Want to avoid operating vector search infrastructure at all? A managed vector database is the right tradeoff, provided its data-residency terms fit your requirements.
- Have a hard data-residency requirement or genuinely large scale (tens of millions+ of vectors with high query volume)? A self-hosted dedicated vector database is worth the operational investment.
- Uncertain about future scale? Start with the option requiring the least new infrastructure, and design your embedding/retrieval layers to be swappable later — don't pre-optimize for scale you don't have yet.
- Checked whether you already run a database that could support a vector extension
- Estimated realistic data volume and query load, not aspirational scale
- Confirmed any data-residency or compliance requirements against candidate options
- Modeled ongoing cost at expected volume for any managed/usage-based option
- Designed embedding and retrieval code as swappable layers, independent of the storage choice
- Avoided choosing based on a benchmark leaderboard alone
Building or scaling a RAG system?
Talk to our AI engineering team about the right vector database and retrieval architecture for your actual scale and data requirements.
Frequently asked questions
Should I use pgvector or a dedicated vector database?+
If you already run Postgres and don't yet have very large scale, pgvector is usually the pragmatic starting point — no new infrastructure, native joins with relational data. A dedicated vector database earns its operational cost once you have genuinely large scale or specific requirements pgvector can't meet.
Is query speed the most important factor in choosing a vector database?+
Rarely in practice — most RAG applications' end-to-end latency is dominated by LLM generation, not vector search, so small retrieval-speed differences seldom matter to the user. Operational burden, cost model and data residency are usually more decisive.
Are managed vector databases worth the cost?+
For teams that want to avoid operating vector search infrastructure themselves, yes — the tradeoff is ongoing usage-based cost and your data residing in a third-party service, which matters if you have strict data-residency requirements.
How hard is it to migrate between vector databases later?+
It's a real project, not a config change — it can involve re-embedding your corpus, re-tuning index parameters, and validating retrieval quality before cutover. Designing embedding and retrieval code as swappable layers reduces this cost significantly.
What scale justifies a self-hosted dedicated vector database?+
Generally tens of millions of vectors or more with high query volume, or a hard data-residency requirement that rules out managed options — most RAG applications never reach a scale where this is clearly necessary.
Can I start with one vector database and switch later?+
Yes, and this is often the right approach — start with the option requiring the least new infrastructure, and keep your embedding and retrieval logic decoupled from the specific storage choice so a later migration is manageable.
Written by
CodeSurge AI Engineering Team
The CodeSurge AI team designs and builds AI systems, SaaS products and enterprise integrations for clients in India, the UAE and beyond — this section shares the architecture patterns, cost drivers and implementation tradeoffs we work through on real projects.