Qdrant vs pgvector
Both are free/open-source alternatives to Pinecone. Here's how they stack up — verified facts, no spin.
Qdrant
TOP PICKFast, open-source, one binary — the default self-hosted vector DB.
Qdrant is a high-performance vector database written in Rust and licensed Apache-2.0 — free to self-host with no usage limits or feature gates. It ships as a single self-contained binary with REST and gRPC APIs, runs via Docker or Kubernetes (official Helm chart and Operator), and offers a managed Qdrant Cloud on AWS, GCP, and Azure when you'd rather not operate it. Strong filtering, quantization, and hybrid search make it the most common 'we left Pinecone' landing spot.
pgvector
Vector search inside the Postgres you already run — zero new infrastructure.
pgvector is an open-source extension (PostgreSQL license) that adds vector similarity search to PostgreSQL itself. If your app already has Postgres, this is the no-new-moving-parts answer: embeddings live next to your relational data, joins and filters are just SQL, and every major managed Postgres (RDS, Cloud SQL, Azure, Supabase, Neon) supports it. Recent releases added parallel HNSW index builds, iterative scans for filtered queries, and halfvec quantization — it now handles serious workloads, not just prototypes.
Side by side
| Qdrant | pgvector | |
|---|---|---|
| Sovereignty Score | 92 | 94 |
| Open source | Yes | Yes |
| Self-hostable | Yes | Yes |
| Local-first | Yes | Yes |
| License | Apache-2.0 | PostgreSQL |
| Pricing | Free self-hosted (no limits); Qdrant Cloud managed tiers with a free 1GB cluster | Free — an extension of PostgreSQL; runs wherever your Postgres runs |
Qdrant is Macrostack's recommended Pinecone alternative, so it's our pick here.
Qdrant
Strengths
- +Apache-2.0, no feature gates — the full engine is open
- +Single Rust binary: laptop to cluster with the same API
- +Excellent metadata filtering, quantization, hybrid search
- +Managed cloud exists when you want zero ops
Trade-offs
- −Self-hosting means you own scaling and backups
- −Smaller managed-service ecosystem than Pinecone's
pgvector
Strengths
- +No new database to operate — it's your existing Postgres
- +Vectors join directly with relational data in SQL
- +Supported by every major managed Postgres provider
- +HNSW indexes, filtered iterative scans, quantization
Trade-offs
- −Very large collections (hundreds of millions of vectors) favor a dedicated engine
- −Tuning happens in Postgres terms — indexes, memory, vacuum
Facts verified 2026-07-15. Licenses and pricing change — spotted something out of date? That's a correction we want.