Do I need Qdrant if I'm using PostgreSQL with pgvector?

Not necessarily.

If your use case is straightforward vector search and you already use PostgreSQL, pgvector is often enough. You may not need Qdrant unless you run into scale, performance, or feature requirements that PostgreSQL struggles with.

Use PostgreSQL + pgvector if:

• You want simplicity and fewer moving parts
• Your dataset is small to medium
• You already store your app data in Postgres and want vectors nearby
• You need basic similarity search and metadata filtering
• You prefer normal SQL, transactions, backups, and ops tooling

Consider Qdrant if:

• You have large-scale vector workloads
• You need very fast nearest-neighbor search at high QPS
• You want a system built specifically for vector retrieval
• You need advanced vector features like:
  • efficient approximate indexing tuned for vectors
  • more flexible filtering at scale
  • payload indexing and vector-specific optimizations
• You expect vector search to become a core infrastructure component

Rule of thumb

• Start with pgvector if you’re early-stage or your workload is moderate.
• Move to Qdrant if you hit limits in latency, throughput, or operational efficiency.

Practical note

A lot of teams begin with Postgres + pgvector, then later add Qdrant only if they see a real need. That’s usually the safest path.

If you want, I can also give you a decision matrix comparing pgvector vs Qdrant by scale, latency, filtering, ops, and cost.