pgvector vs Qdrant
pgvector vs Qdrant: side-by-side comparison of two vector-database infrastructure systems — architecture, taxonomy, license, pricing, MCP/A2A support, and direct edges.
Cost & capability
| pgvector | Qdrant | |
|---|---|---|
| Cost tier | free | free |
| $/Mtok input | 0 | 0 |
| $/Mtok output | 0 | 0 |
Where they differ (14)
Rows where both sides have data and the values disagree — the shortlist of dimensions that actually distinguish these two systems.
| pgvector | Qdrant | |
|---|---|---|
| Type | PostgreSQL vector extension | Vector DB + DBSF + RRF |
| Created | 2021-04 | 2020-05 |
| Latest release | v0.8.6 (2026-07-29) | v1.19.2 (2026-10-05) |
| License | PostgreSQL | Apache-2.0 |
| GitHub | pgvector/pgvector — ~23.5k stars, last commit 2026-07 or later (0.8.7 unreleased) | qdrant/qdrant — ~34.9k stars, last commit 2026-10 |
| Pricing | free (OSS) | Free tier forever (single node, 1 GB RAM / 4 GB disk); Standard usage-based hourly billing; Premium has minimum spend; Hybrid/Private Cloud via sales |
| Funding | No external funding — open-source project by independent developer | $78M total Series B · 2026-03 |
| Backend storage | Postgres | custom (Rust-built HNSW + RocksDB metadata) |
| Deployment | Self-hosted only — PostgreSQL extension; also offered inside managed Postgres (Supabase, AWS RDS, Azure, Neon, etc.) | Both (self-hosted + managed cloud) — OSS engine; Qdrant Managed Cloud, Hybrid Cloud and Private Cloud |
| API surface | SQL via Postgres protocol | REST, gRPC, SDK: Python, JS/TS, Rust, Go, Java, C# |
| Multi-tenancy | Logical isolation via Postgres schemas/databases; managed providers (Supabase, Neon, RDS) add tenant-level controls | Hardened unprivileged container per cluster with strict network policies; outbound restricted; paid plans on dedicated resources; Private Cloud option for air-gap |
| MCP | yes — community Postgres MCP servers with pgvector tools (e.g. MOmayr/mcp-postgres-pgvector) | yes — official qdrant/mcp-server-qdrant (GitHub, Apache-2.0) |
| A2A | not supported | no Google A2A (Agent2Agent) integration documented as of 2026-05. |
| OpenTelemetry | via Postgres OTel | first-class — Prometheus + OTel |
At a glance
| pgvector | Qdrant | |
|---|---|---|
| Section | Vector-database infrastructure | Vector-database infrastructure |
| Tier | T1 | T1 |
| Type | PostgreSQL vector extension | Vector DB + DBSF + RRF |
| Created | 2021-04 | 2020-05 |
| Latest release | v0.8.6 (2026-07-29) | v1.19.2 (2026-10-05) |
| License | PostgreSQL | Apache-2.0 |
| GitHub | pgvector/pgvector — ~23.5k stars, last commit 2026-07 or later (0.8.7 unreleased) | qdrant/qdrant — ~34.9k stars, last commit 2026-10 |
| Pricing | free (OSS) | Free tier forever (single node, 1 GB RAM / 4 GB disk); Standard usage-based hourly billing; Premium has minimum spend; Hybrid/Private Cloud via sales |
| Funding | No external funding — open-source project by independent developer | $78M total Series B · 2026-03 |
| Backend storage | Postgres | custom (Rust-built HNSW + RocksDB metadata) |
| Deployment | Self-hosted only — PostgreSQL extension; also offered inside managed Postgres (Supabase, AWS RDS, Azure, Neon, etc.) | Both (self-hosted + managed cloud) — OSS engine; Qdrant Managed Cloud, Hybrid Cloud and Private Cloud |
| API surface | SQL via Postgres protocol | REST, gRPC, SDK: Python, JS/TS, Rust, Go, Java, C# |
| Embedding | BYO | BYO |
| Multi-tenancy | Logical isolation via Postgres schemas/databases; managed providers (Supabase, Neon, RDS) add tenant-level controls | Hardened unprivileged container per cluster with strict network policies; outbound restricted; paid plans on dedicated resources; Private Cloud option for air-gap |
| MCP | yes — community Postgres MCP servers with pgvector tools (e.g. MOmayr/mcp-postgres-pgvector) | yes — official qdrant/mcp-server-qdrant (GitHub, Apache-2.0) |
| A2A | not supported | no Google A2A (Agent2Agent) integration documented as of 2026-05. |
| OpenTelemetry | via Postgres OTel | first-class — Prometheus + OTel |
| Optimised for | low-latency similarity search + scale | low-latency similarity search + scale |
| Anti-fit | not for relational / graph-heavy queries (vector-first by design) | not for relational / graph-heavy queries (vector-first by design) |
Pros & cons
pgvector
Pros: Postgres extension — keeps vector data alongside transactional data with full ACID; minimal ops for shops already running Postgres.
Cons: Performance plateau at very large vector counts; limited filter pushdown vs purpose-built engines.
Qdrant
Pros: Rust-built — fastest of the OSS vector DBs in many benchmarks; strong filtering and hybrid search; clean API.
Cons: Smaller managed-cloud presence than Pinecone; ecosystem of integrations still maturing relative to Weaviate.