pgvector vs Qdrant
- ✦ PostgreSQL Extension
- ✦ Exact Nearest Neighbor
- ✦ Approximate Nearest Neighbor (IVFFlat, HNSW)
- ✦ Open Source (Apache 2.0)
- ✦ Written in Rust
- ✦ HNSW Index
pgvector and Qdrant are both Vector Databases tools. Compare features, pricing, and ratings below to find the best fit for your team.
When to Choose pgvector vs Qdrant
The question that matters: “In what situation will I regret choosing A over B after 3 months?”
pgvector stores embeddings as a native column type and queries them with standard SQL, avoiding the operational complexity of a separate vector database for applications already running on Postgres.
Qdrant's HNSW indexes integrate payload filtering natively, executing filtered nearest-neighbor search without a post-filter scan step, maintaining sub-50ms latency on complex metadata filters.
pgvector's HNSW index achieves sub-50ms similarity search for collections under 10M vectors, covering most product recommendation and semantic search use cases without a specialized vector database.
Qdrant supports sparse vectors natively alongside dense vectors, enabling BM25 and embedding search in the same collection for hybrid retrieval without maintaining two separate indexes.
pgvector writes and deletes embeddings within standard Postgres transactions, ensuring vector index and application data never diverge in multi-step operations that require rollback.
Qdrant's on-disk HNSW stores vectors on SSD while keeping only graph navigation data in RAM, serving collections larger than server memory at acceptable latency for cost-sensitive deployments.
Pricing Comparison & PlansHigh· Verified Jul 8, 2026
pgvector Plans
Free tier available
Open Source
Open SourceBest for: You get a fully-featured, self-hosted PostgreSQL extension for vector search
- ✓Vector similarity search (L2 distance, inner product, cosine distance, L1 distance)
- ✓Exact and approximate nearest neighbor search
- ✓HNSW (Hierarchical Navigable Small World) indexing
- ✓IVFFlat (Inverted File with Flat Compression) indexing
- ✓ACID compliance and transactional safety via PostgreSQL
Qdrant Plans
Free tier available
Open Source (Self-Hosted)
Open SourceBest for: Ideal for users who prefer full control and self management of their vector database
- ✓Apache 2.0 licensed
- ✓Full data sovereignty
- ✓Runs on your own infrastructure
- ✓Zero licensing cost at any scale
Free Tier
FreeBest for: Testing and prototypes
- ✓Single Node Cluster
- ✓Free Cloud Inference with selected models
- ✓Ideal for testing and prototypes
Standard
$65/moBest for: Production workloads + scaling
- ✓Production workloads
- ✓Dedicated clusters
- ✓Higher availability
- ✓Automated daily backups
- ✓Built-in monitoring and alerting
Capability Breakdown
6 differences found across 14 standardized features
- •PostgreSQL Extension
- •Exact Nearest Neighbor
- •Approximate Nearest Neighbor (IVFFlat, HNSW)
- •L2 / Cosine / Inner Product Distance
- •Indexing for Large Datasets
- •SQL Query Interface
- •JOIN with Relational Data
- •ACID Transactions
- •Standard PostgreSQL Tooling
- •Works with Supabase/RDS/Neon
- •pgvectorscale Extension
- •Python/JS/Ruby Support
- •Concurrent Queries
- •Type Safety
- •Open Source (MIT)
- •No Additional Ops
- •Open Source (Apache 2.0)
- •Written in Rust
- •HNSW Index
- •Sparse Vectors (BM25-compatible)
- •Multi-vector Support
- •Payload Filtering
- •Full-Text Search
- •Named Vectors
- •Quantization (Scalar, Product, Binary)
- •Distributed Mode
- •Snapshot & Recovery
- •REST & gRPC APIs
- •Python/JS/Rust/Go SDKs
- •LangChain Integration
- •On-Premise + Cloud
- •Web UI Dashboard
Strengths & Limitations
Evaluative strengths and weaknesses: not feature lists
- +No new infrastructure: runs inside existing PostgreSQL
- +SQL interface familiar to every developer
- +ACID transactions across vectors and relational data
- +Works on Supabase, RDS, Neon: all managed PG providers
- −Performance trails purpose-built vector DBs at 10M+ vectors
- −No distributed vector search without manual sharding
- +Top benchmark performance via Rust and quantization
- +Named vectors enable multimodal and complex search patterns
- +Binary quantization reduces memory 32x
- +Excellent documentation and developer experience
- −The area for improvement in Qdrant is its clustering capability.
At a Glance
Recent Price History
Qdrant removed the "Cloud Standard" plan
Plan removed · May 30, 2026
Qdrant removed the "Open Source" plan
Plan removed · May 30, 2026
Qdrant added a new "Free Tier" plan at $0/mo (Free)
Plan added · May 30, 2026
Qdrant added a new "Standard" plan at $65/mo
Plan added · May 30, 2026
Qdrant removed the "Enterprise" plan
Plan removed · May 30, 2026
Frequently Asked Questions
Related Comparisons
pgvectorFree plan
QdrantFree · from $65/moSources & Data Trail · pgvector
- 1.Official Website·Official vendor website
- 2.G2·G2 verified reviews · 3.8/5 · 12 reviews
Sources & Data Trail · Qdrant
- 1.Official Pricing Page·Source of verified tiers(Checked: 2026-07-08)
- 2.Official Website·Official vendor website
- 3.G2·G2 verified reviews · 4.5/5 · 12 reviews
- 4.PeerSpot·PeerSpot enterprise peer reviews