Weaviate vs pgvector
Weaviate
Enterprise-ready, distributed vector database with GraphQL API, advanced filtering, and multi-modal search capabilities.
AI/ML-heavy applications, SaaS platforms needing multi-tenancy, enterprises with dedicated infrastructure budgets, applications requiring sub-100ms latency at scale
pgvector
PostgreSQL extension enabling vector search alongside relational data in existing Postgres databases.
Startups and small teams already using PostgreSQL, cost-conscious deployments, applications mixing vectors with relational data, teams lacking dedicated infrastructure resources
Short Answer
Weaviate is a standalone vector database with built-in AI integration and multi-tenancy support, while pgvector is a PostgreSQL extension that leverages existing PostgreSQL infrastructure for vector search at a lower operational cost.
Our Verdict
AI-assistedChoose Weaviate if you need a purpose-built vector database with native AI integrations, built-in multi-tenancy, and superior query performance at scale. Choose pgvector if you already use PostgreSQL, want minimal operational overhead, prefer lower costs, and need vector search as a supplementary feature alongside relational data.
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Choose Weaviate if
AI/ML-heavy applications, SaaS platforms needing multi-tenancy, enterprises with dedicated infrastructure budgets, applications requiring sub-100ms latency at scale
Choose pgvector if
Startups and small teams already using PostgreSQL, cost-conscious deployments, applications mixing vectors with relational data, teams lacking dedicated infrastructure resources
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Key Differences at a Glance
Key Facts & Figures
| Metric | Weaviate | pgvector | Diff |
|---|---|---|---|
| Estimated Monthly Cost (1M vectors)(USD) | $500-800 (managed) | — | — |
| Time to First Query(minutes) | 30-45 minutes (self-hosted) | — | — |
| Maximum Vector Dimensions(dimensions) | Unlimited | 2,000 | — |
| Query Latency (p99)(milliseconds) | 50-150ms | 50-500ms | -64% |
| Indexing Methods Supported(count) | 3 methods (HNSW, flat, dynamic) | 2 methods (IVFFlat, HNSW) | +50% |
| Average Query Latency (1M vectors, 384-dim)(milliseconds) | 75ms | 120ms | -38% |
| Integrated LLM Providers(count) | 20+ providers (OpenAI, Anthropic, Cohere, Hugging Face) | None (requires external integration) | — |
| Minimum Monthly Infrastructure Cost (Self-hosted Production)(USD) | $800 | $150 | +433% |
| Maximum Scalability (distributed nodes)(nodes) | 100+ | 1-3 (read replicas) | +3233% |
| API Query Language Support(count) | 2 (GraphQL, REST) | 1 (SQL only) | +100% |
| Query Throughput(operations per second (QPS)) | 100,000 QPS | — | — |
| Maximum Collection Size(billion vectors) | 2 billion vectors | — | — |
| Setup Time (Cloud/Self-Hosted)(minutes) | 5-10 minutes (cloud) | — | — |
| GitHub Community Stars(stars) | 13,000+ stars | 4,200+ stars | +210% |
| Number of Native LLM Integrations(integrations) | 20+ LLM providers | — | — |
| Query Latency (95th percentile)(milliseconds) | 100-500 ms | — | — |
| Memory per 1M Vectors(GB) | 8-12 GB | — | — |
| Startup Time (empty instance)(seconds) | 20-30 seconds | — | — |
| Built-in LLM Integrations(count) | 15+ providers | — | — |
| Managed Cloud Base Price (monthly)(USD) | $25/month | — | — |
| Throughput (vectors/second insert)(vectors/sec) | 5,000-10,000 | — | — |
| Maximum Vectors Per Instance(vectors) | 100M+ (distributed) | — | — |
| Average Query Latency(milliseconds) | 50-150ms | — | — |
| Setup Time to First Query(minutes) | 30-60 (with Docker) | — | — |
| GitHub Stars | ~9,500 stars (as of 2026) | — | — |
| Minimum Memory for 1M Vectors(GB) | 4-8GB | — | — |
| Setup Time (First Query)(minutes) | 30-60 minutes | — | — |
| Max Recommended Vector Count(vectors) | 100M+ (distributed) | — | — |
| Maximum Vector Capacity(billion vectors) | <1 billion (practical limit) | <1 billion (practical limit) | — |
| Minimum Setup Time(minutes) | 120-300 minutes | 120-300 minutes | — |
| Cost for 1M Monthly Read Operations(USD) | $0 (self-hosted only) | $0 (self-hosted only) | — |
| Vector Dimensionality Support(maximum dimensions) | Up to 2,000 dimensions | Up to 2,000 dimensions | — |
| Query Latency (1M vectors, 768-dim, 10th percentile)(milliseconds) | ~120ms | ~120ms | — |
| GitHub Stars (as of 2026)(stars) | ~10,500 | ~10,500 | — |
All figures sourced from publicly available data. Last updated Jun 2026.
Key Differences
Weaviate
Standalone vector database
pgvector
PostgreSQL extension
Weaviate
Requires separate infrastructure deployment
pgvector
Integrates into existing PostgreSQL, minimal setup🏆
Weaviate
HNSW, flat, dynamic indexing🏆
pgvector
IVFFlat, HNSW (with pgvector 0.5+)
Weaviate
Native support for 20+ LLM providers🏆
pgvector
None, requires external integration
Weaviate
Native multi-tenancy with tenant isolation🏆
pgvector
Requires application-level implementation
Weaviate
$500-2000+/month for production cluster
pgvector
$100-500/month leveraging existing PostgreSQL🏆
Weaviate
50-100ms average🏆
pgvector
75-150ms average
Full Comparison
| Attribute | Weaviate | pgvector |
|---|---|---|
| Free Tier Vector Limit(vectors) | Unlimited (self-hosted) | — |
| Estimated Monthly Cost (1M vectors)(USD) | $500-800 (managed) | — |
| Time to First Query(minutes) | 30-45 minutes (self-hosted) | — |
| Maximum Vector Dimensions(dimensions) | Unlimited | 2,000 |
| Query Latency (p99)(milliseconds) | 50-150ms | 50-500ms |
| Indexing Methods Supported(count) | 3 methods (HNSW, flat, dynamic) | 2 methods (IVFFlat, HNSW) |
| Average Query Latency (1M vectors, 384-dim)(milliseconds) | 75ms | 120ms |
| Query Throughput(operations per second (QPS)) | 100,000 QPS | — |
| GPU Acceleration Support | Limited (planning phase) | — |
Show 4 more attributesQuery Latency (95th percentile)(milliseconds) 100-500 ms — Throughput (vectors/second insert)(vectors/sec) 5,000-10,000 — Average Query Latency(milliseconds) 50-150ms — Query Latency (1M vectors, 768-dim, 10th percentile)(milliseconds) ~120ms — | ||
| Uptime SLA(percent) | Not guaranteed (self-hosted) | — |
| Uptime SLA Guarantee(percent) | User dependent (no SLA) | — |
| Native Hybrid Search Support(null) | BM25 keyword + vector | — |
| Built-in Hybrid Search Support | Native BM25 + vector search | — |
| Number of Native LLM Integrations(integrations) | 20+ LLM providers | — |
| Hybrid Search Support (BM25 + Vector) | Yes | — |
| Multi-tenancy Support | Native with isolation | — |
Show 5 more attributesQuery Filtering Support Advanced GraphQL + WHERE clauses with boolean logic — Multi-Modal Search Text, image, audio, video — Vector Dimensionality Support(maximum dimensions) Up to 2,000 dimensions — SQL Relational Query Integration(native support) Yes (unified via SQL) — Built-in Embedding Generation No (external only) — | ||
| Deployment Model | Cloud-managed SaaS + Self-hosted Docker/Kubernetes | Self-hosted PostgreSQL extension only |
| Integrated LLM Providers(count) | 20+ providers (OpenAI, Anthropic, Cohere, Hugging Face) | None (requires external integration) |
| Built-in LLM Integrations(count) | 15+ providers | — |
| Minimum Monthly Infrastructure Cost (Self-hosted Production)(USD) | $800 | $150 |
| Licensing Cost(USD) | $0-5000+/month (SaaS) | — |
| Native Multi-tenancy Support | Yes, with built-in tenant isolation | No, application-level only |
| Maximum Scalability (distributed nodes)(nodes) | 100+ | 1-3 (read replicas) |
| Maximum Collection Size(billion vectors) | 2 billion vectors | — |
| Maximum Vectors Per Instance(vectors) | 100M+ (distributed) | — |
| Max Recommended Vector Count(vectors) | 100M+ (distributed) | — |
| Maximum Vector Capacity(billion vectors) | <1 billion (practical limit) | — |
| API Query Language Support(count) | 2 (GraphQL, REST) | 1 (SQL only) |
| Setup Time (First Query)(minutes) | 30-60 minutes | — |
| Setup Time (Cloud/Self-Hosted)(minutes) | 5-10 minutes (cloud) | — |
| Setup Time to First Query(minutes) | 30-60 (with Docker) | — |
| Minimum Setup Time(minutes) | 120-300 minutes | — |
| GitHub Community Stars(stars) | 13,000+ stars | 4,200+ stars |
| GitHub Stars (as of 2026)(stars) | ~10,500 | — |
| Memory per 1M Vectors(GB) | 8-12 GB | — |
| Startup Time (empty instance)(seconds) | 20-30 seconds | — |
| Supported Deployment Modes | Docker, Kubernetes, Cloud (AWS/GCP/Azure) | — |
| Minimum Setup Infrastructure | Docker/Kubernetes cluster (4GB+ RAM minimum) | — |
| Managed Cloud Base Price (monthly)(USD) | $25/month | — |
| Cost for 1M Monthly Read Operations(USD) | $0 (self-hosted only) | — |
| Multi-modal Support (native)(modalities) | 3 (text, image, audio) | — |
| GitHub Stars | ~9,500 stars (as of 2026) | — |
| Minimum Memory for 1M Vectors(GB) | 4-8GB | — |
| Kubernetes Support | Native Kubernetes-ready Helm charts | — |
| LangChain Integration Maturity | Supported but secondary to GraphQL API | — |
| Installation Complexity(minutes) | Integrated (no new deployment) | — |
| SQL Filtering Capability | Full SQL WHERE clauses (unlimited) | — |
| Open Source License | PostgreSQL License (permissive) | — |
| Supported Index Types(count) | IVFFlat, HNSW (v0.7+) | — |
Show 4 more attributes
Show 5 more attributes
Visual Comparison
Side-by-side comparison of numeric attributes
Pros & Cons
Weaviate
Pros
- Native integration with 20+ LLM providers (OpenAI, Anthropic, Cohere, Hugging Face) for zero-shot and few-shot classification
- Built-in multi-tenancy with tenant isolation and per-tenant pricing models
- Advanced indexing with HNSW, flat, and dynamic indexing strategies with configurable quantization
- GraphQL API and REST API with built-in filtering, searching, and aggregation across vector and scalar data
- Horizontal scaling with distributed architecture supporting sharding across 100+ nodes
Cons
- Higher operational complexity and infrastructure costs compared to PostgreSQL extensions
- Smaller ecosystem compared to PostgreSQL's mature tooling and third-party integrations
- Requires dedicated DevOps expertise for production deployment and maintenance
pgvector
Pros
- Zero additional infrastructure—runs within existing PostgreSQL installations
- Leverages PostgreSQL's mature ecosystem: ACID transactions, replication, backup tools, monitoring
- Significantly lower operational costs by consolidating vector and relational data in one system
- Simple SQL syntax for vector operations integrated seamlessly with traditional queries
- Active open-source community with rapid development cycle (version 0.8+ released 2024)
Cons
- Limited to PostgreSQL's single-machine performance ceiling without complex sharding solutions
- No native multi-tenancy or generative AI integrations—requires custom application logic
- IVFFlat indexing can suffer from performance degradation on high-dimensional vectors (>1000 dimensions) without proper tuning
Frequently Asked Questions
Only if you have significant vector-heavy workloads (>10M vectors) requiring sub-100ms latency, need native multi-tenancy, or plan extensive generative AI integrations. If you're mixing vectors with relational data or have cost constraints, pgvector within PostgreSQL is more efficient. Migration is irreversible for relational data, so a hybrid approach (PostgreSQL + Weaviate for vectors only) is often optimal.
Resources & Learn More
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