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| title | sort | section-id | keywords | description | language |
|---|---|---|---|---|---|
| Comparison | 130 | overview | comparison, Postgres, pgvector, Pinecone, Weaviate, MongoDB, alternatives | How NeuralDB compares to Postgres+pgvector, Pinecone, Weaviate, and MongoDB Atlas Vector Search | en |
Comparison
This page provides an honest comparison of NeuralDB against the most common alternatives for applications that need vector search.
NeuralDB vs PostgreSQL + pgvector
pgvector is the most popular way to add vector search to an existing PostgreSQL deployment. If you already run Postgres, the low barrier to entry is attractive. Here is where the two diverge:
| Feature | NeuralDB | Postgres + pgvector |
|---|---|---|
| Vector index algorithm | HNSW (native) | HNSW, IVFFlat |
| Max dimensions | 65,536 | 16,000 |
| Index build speed | Native Rust (fast) | C extension (moderate) |
| Parallel index builds | Yes | Limited |
| Vector data memory isolation | Dedicated vector buffer pool | Shared with row pages |
| Horizontal sharding | Built-in | Manual (Citus, Patroni) |
| Automatic embeddings | Yes | No |
| Multi-modal vectors | Yes | No (one VECTOR column type) |
| Streaming ingestion | Yes | No |
| Read replica for vector queries | Automatic routing | Manual |
When to choose Postgres + pgvector: You already have a Postgres deployment, your dataset is under 10M vectors, and you do not need automatic embeddings or horizontal scaling. The operational overhead of a new database system is not worth it.
When to choose NeuralDB: Your vector dataset exceeds 10M rows, you need horizontal sharding, you want automatic embedding pipelines, or you are starting a new project and want a purpose-built system.
NeuralDB vs Pinecone
Pinecone is a fully managed, purpose-built vector database. It excels at pure vector search at massive scale.
| Feature | NeuralDB | Pinecone |
|---|---|---|
| Relational data | Full SQL | Metadata filters only |
| Hybrid queries | Single query, query planner | Metadata post-filter |
| ACID transactions | Yes | No |
| SQL interface | Yes | Proprietary API |
| Self-hosted option | Yes | No |
| Pricing model | Infrastructure cost | Per-request + storage |
| Latency (p99, 1M vectors) | ~5ms | ~10ms (managed) |
| Data gravity | Stays in your infra | Vendor-managed |
When to choose Pinecone: You need a fully managed solution with no operational overhead, your workload is pure vector search with simple metadata filtering, and you are comfortable with a vendor-specific API and pricing model.
When to choose NeuralDB: You need relational data co-located with vectors, ACID transactions, SQL compatibility, self-hosting, or lower total cost of ownership at scale.
NeuralDB vs Weaviate
Weaviate is an open-source vector database with a GraphQL-based query language and built-in module support for embedding generation.
| Feature | NeuralDB | Weaviate |
|---|---|---|
| Query language | SQL (NQL) | GraphQL |
| Relational joins | Yes | No |
| ACID transactions | Yes | Eventually consistent |
| SQL wire compatibility | PostgreSQL wire protocol | Proprietary |
| Embedding modules | Yes | Yes (vectorizers) |
| BM25 hybrid search | Yes | Yes |
| Multi-tenancy | Row-level, schema-level | Class-level |
| Replication | Sync + async | Eventual |
When to choose Weaviate: You want an open-source solution with a rich ecosystem of vectorizer modules and a GraphQL interface. If your team is more comfortable with graph-shaped queries than SQL, Weaviate is a natural fit.
When to choose NeuralDB: You need SQL, transactional guarantees, relational joins between your vector data and other structured data, or PostgreSQL wire protocol compatibility (so existing tools like dbt, Metabase, and psql work out of the box).
NeuralDB vs MongoDB Atlas Vector Search
MongoDB Atlas added vector search as an extension to its document model. It is a convenient choice if you already run Atlas.
| Feature | NeuralDB | MongoDB Atlas Vector Search |
|---|---|---|
| Data model | Relational + vector | Document + vector |
| Query language | SQL | MQL (MongoDB Query Language) |
| ACID transactions | Yes (all operations) | Yes (within a session) |
| Horizontal scaling | Native sharding | Atlas sharding |
| Vector index type | HNSW | ENN (exact), HNSW |
| Full-text + vector hybrid | Yes | Yes (Atlas Search) |
| Self-hosted | Yes | Atlas only |
When to choose MongoDB Atlas Vector Search: Your application already uses MongoDB and you want to add vector search without changing your data model or infrastructure. The document model maps well to semi-structured data.
When to choose NeuralDB: You need relational data integrity, SQL, lower query latency, or the ability to self-host. If your data is inherently tabular (rather than document-shaped), NeuralDB's relational model will be a better fit.
Performance Benchmarks
The following benchmarks were run against 10M 1536-dimensional vectors on equivalent hardware (32 vCPU, 128 GB RAM, NVMe SSD):
| System | QPS (recall@95%) | p50 latency | p99 latency | Index build time |
|---|---|---|---|---|
| NeuralDB 1.0 | 8,400 | 1.2ms | 4.8ms | 22 min |
| pgvector 0.7 | 3,100 | 2.9ms | 12ms | 45 min |
| Pinecone (s1) | 5,200 | 1.8ms | 8ms | Managed |
| Weaviate 1.24 | 4,600 | 2.1ms | 9ms | 31 min |
Benchmarks are inherently workload-dependent. Run your own benchmarks against your specific data and query patterns before making infrastructure decisions.