VectorChord
A high-performance PostgreSQL extension for vector similarity search, positioned as a **drop-in replacement for pgvector** with order-of-magnitude speedups at billion scale. Built by **TensorChord** (creators of pgvecto.rs). Uses a proprietary IVF + **RaBitQ** index — branded **VChord** — that is disk-based and SIMD-optimized instead of pgvector's memory-bound HNSW, cutting RAM requirements ~**70%** while keeping query speed.
Summary
A high-performance PostgreSQL extension for vector similarity search, positioned as a **drop-in replacement for pgvector** with order-of-magnitude speedups at billion scale. Built by **TensorChord** (creators of pgvecto.rs). Uses a proprietary IVF + **RaBitQ** index — branded **VChord** — that is disk-based and SIMD-optimized instead of pgvector's memory-bound HNSW, cutting RAM requirements ~**70%** while keeping query speed.
VectorChord is the choice when a team has PostgreSQL operational expertise and wants to lift the vector-search ceiling without migrating to Pinecone, Qdrant, or Milvus. Vectors stay inside PostgreSQL — same transactions, same joins, same ACID guarantees. v1.1 (Feb 2026) makes a 1B-vector index practical on a 16-vCPU machine. Earth Genome runs **3.2B vectors** for environmental monitoring on this stack.
- "Drop-in replacement" is true at the SQL/data-type layer but the index type changes — existing pgvector tables must rebuild indexes to get the speedup. Schema-compatible, not index-compatible.
- The 70% RAM cut vs HNSW is real but it's a disk-based index — under-provisioned IOPS makes it look slow. Use NVMe, not generic gp3 EBS, at billion scale.
- 100× faster index build is on billion-scale, not on 100K-vector small workloads — at small scale pgvector's HNSW is still fast enough that the operational risk of switching isn't justified.
- VectorChord-BM25 is a separate extension — keyword + vector hybrid search isn't out-of-the-box.
- Apache-style license but the project is governed by TensorChord (commercial entity); single-vendor governance risk applies the same way it would for any extension under a startup's stewardship.
Performance posture: 100M vectors indexed in 20 minutes (16 vCPU); 1B vectors indexed in 1.8 hours (AWS i7ie.6xlarge). 40 ms P99 at 95% recall on 1B vectors. 3× QPS vs pgvector at the same recall on LAION 5M.
scoped_toVector Indexing on Object Storage — billion-scale vector index inside PostgreSQLcompetes_withQdrant — alternative billion-scale vector pathcompetes_withMilvus — alternative billion-scale vector pathcompetes_withWeaviate — alternative billion-scale vector pathsolvesCold Scan Latency — disk-based IVF with cache-friendly traversal
Definition
A high-performance PostgreSQL extension for vector similarity search, positioned as a **drop-in replacement for pgvector** with order-of-magnitude speedups at billion-scale. Built by **TensorChord** (creators of pgvecto.rs), fully wire-compatible with pgvector's data types and SQL syntax. Introduces a proprietary indexing algorithm called **VChord** that combines an Inverted File Index (IVF) with **RaBitQ** — a Product Quantization technique optimized for modern CPU SIMD instructions. Disk-based design instead of pgvector's HNSW (which is memory-bound), cutting RAM requirements by up to **70%** while maintaining query speed. Supports vectors up to 60,000 dimensions with both L2 and cosine operators. v1.1 shipped February 2026.
pgvector's HNSW index is memory-bound — billion-scale vector workloads either spill to disk and lose performance or buy enormous RAM machines. VectorChord's IVF+RaBitQ design lets a single PostgreSQL instance serve a billion vectors on a 16-vCPU box with 192 GB RAM in 1.8 hours of index build time. For teams with PostgreSQL operational expertise, VectorChord lifts the ceiling without forcing a migration to Pinecone, Qdrant, or Milvus — vectors stay inside PostgreSQL transactions, joins, and ACID guarantees.
Billion-scale RAG embedding stores inside an existing PostgreSQL deployment; replacing pgvector at scale without rewriting the data layer; production RAG with strict ACID/transaction requirements; environmental monitoring at planet scale (Earth Genome runs **3.2B vectors**); cost-sensitive deployments wanting to avoid the all-RAM pricing curve.
Connections 5
Outbound 5
scoped_to1solves1Resources 3
Source repository for VectorChord, the IVF+RaBitQ-based PostgreSQL extension. Drop-in pgvector replacement with disk-based indexing for billion-scale workloads on a single PostgreSQL instance.
TensorChord's positioning piece on cost-per-vector economics — frames the "70% RAM cut" claim and the per-dollar-per-vector throughput vs pgvector HNSW.
Companion extension for BM25 keyword search — the hybrid-search story is opt-in, not bundled, so this is what teams need for dense+sparse retrieval inside PostgreSQL.