pgvector vs Weaviate
- ✦ PostgreSQL Extension
- ✦ Exact Nearest Neighbor
- ✦ Approximate Nearest Neighbor (IVFFlat, HNSW)
- ✦ Open Source (Apache 2.0)
- ✦ Hybrid Search (BM25 + Vector)
- ✦ Multimodal Support
pgvector and Weaviate are both Vector Databases tools. Compare features, pricing, and ratings below to find the best fit for your team.
When to Choose pgvector vs Weaviate
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.
Weaviate's structured schema enforces data types on vector objects, enabling filtered vector search that combines nearest neighbor with exact property matches and reducing false positives in metadata-sensitive retrieval.
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.
Weaviate's multi2vec module indexes text and image objects in the same collection, enabling cross-modal search where a text query returns images and vice versa without separate pipelines.
pgvector writes and deletes embeddings within standard Postgres transactions, ensuring vector index and application data never diverge in multi-step operations that require rollback.
Weaviate's Generative Search module passes retrieved objects directly to an LLM within the same query, cutting latency by eliminating a separate LLM API call for RAG retrieval-generation pipelines.
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
Weaviate Plans
Free tier available
Open-Source
Open SourceBest for: Good for self-hosting and full control over your vector database
- ✓Full feature access
- ✓Control over infrastructure
- ✓Self-hosted deployment
- ✓BSD-3 license
Sandbox
FreeBest for: Ideal for testing and development without commitment
- ✓Managed cloud
- ✓Learning and prototyping
- ✓2,000 requests/day limit
- ✓14-day trial
Flex
$45/moBest for: Prototypes, pilots, small use cases
- ✓Entry-level managed tier
- ✓Shared multi-tenant infrastructure
- ✓Pay-as-you-go pricing
- ✓99.5% SLA
- ✓Self-service scaling
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)
- •Hybrid Search (BM25 + Vector)
- •Multimodal Support
- •GraphQL API
- •REST API
- •gRPC API
- •Built-in Vectorizers (OpenAI, Cohere, etc.)
- •Generative Search
- •Multi-tenancy
- •Modules Architecture
- •Kubernetes Deployment
- •Schema Management
- •Batch Import
- •Cross-References (Graph)
- •Python/JS/Go SDKs
- •LangChain Integration
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
- +Open source with managed cloud option gives deployment flexibility
- +Built-in vectorizers reduce pipeline complexity
- +Knowledge graph cross-references unique in category
- +Active community and excellent documentation
- −GraphQL API has steeper learning curve
- −Performance benchmarks trail Qdrant at very high scale
At a Glance
Frequently Asked Questions
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