Decision comparison
Milvus vs pgvector
Milvus is the clear choice for teams building large-scale AI applications that need to handle billions of vectors with distributed infrastructure, while pgvector wins for organizations already running PostgreSQL that want to add vector search without introducing new infrastructure or operational overhead.
Architecture choice. These take different approaches to the same problem. Read the table as a fit question rather than a feature race.
All 2 are vector databases.
Quick Comparison
| Decision factor | Milvus | pgvector |
|---|---|---|
| Architecture | Standalone distributed vector database with separated storage and computation layers | PostgreSQL extension adding vector columns and similarity operators to existing databases |
| Scalability | Scales horizontally to tens of billions of vectors with minimal performance loss | Optimized for 1 million to 50 million vectors with sub-second search latency |
| Index Types | Global Index, IVF, HNSW, DiskANN with automatic optimization for scale | HNSW and IVFFlat indexes supporting L2, cosine, inner product, and L1 distances |
| Query Language | Custom Python SDK and gRPC API for vector operations and hybrid search | Standard SQL syntax with vector operators integrated into PostgreSQL queries |
| Deployment Complexity | Multiple tiers from pip install Lite to fully distributed Kubernetes deployment | Single extension install on any existing PostgreSQL 13+ instance |
| Cost Model | Open-source self-hosted or Zilliz Cloud managed service with enterprise pricing | Completely free and open-source with no paid tiers or licensing fees |
Milvus
- Architecture:
- Standalone distributed vector database with separated storage and computation layers
- Scalability:
- Scales horizontally to tens of billions of vectors with minimal performance loss
- Index Types:
- Global Index, IVF, HNSW, DiskANN with automatic optimization for scale
- Query Language:
- Custom Python SDK and gRPC API for vector operations and hybrid search
- Deployment Complexity:
- Multiple tiers from pip install Lite to fully distributed Kubernetes deployment
- Cost Model:
- Open-source self-hosted or Zilliz Cloud managed service with enterprise pricing
pgvector
- Architecture:
- PostgreSQL extension adding vector columns and similarity operators to existing databases
- Scalability:
- Optimized for 1 million to 50 million vectors with sub-second search latency
- Index Types:
- HNSW and IVFFlat indexes supporting L2, cosine, inner product, and L1 distances
- Query Language:
- Standard SQL syntax with vector operators integrated into PostgreSQL queries
- Deployment Complexity:
- Single extension install on any existing PostgreSQL 13+ instance
- Cost Model:
- Completely free and open-source with no paid tiers or licensing fees
Public signals
Verified factual signals only. Bars appear only for like-for-like metrics with five weekly assessments for every tool; missing evidence stays explicit. These signals do not establish enterprise adoption, product quality, or total cost.
| Metric | Milvus | pgvector |
|---|---|---|
| Docker Hub pulls(Product adoption) | 79.4M | 236.6M |
| GitHub commits, 90d(Product adoption) | 653 | 118 |
| GitHub stars(Product adoption) | 46,000+ | 23,000+ |
| Search interest(Market interest) | 1 | 1 |
| Hacker News mentions, 90d(Community interest) | 0 | 5 |
| npm weekly downloads(Developer adoption) | 147.3k | 608.0k |
| PyPI weekly downloads(Developer adoption) | 759.1k | 8.6M |
| Stack Overflow questions(Community interest) | 208 | 91 |
As of October 12, 2026 — updated weekly.
Health & risk evidence
Observed public-source checks for mapped package versions and repositories.
Milvus
October 12, 2026Package vulnerabilities
npm · @zilliz/milvus2-sdk-node@3.0.6 · PyPI · pymilvus@3.0.2
0 vulnerabilities
across 2 packages
Repository security score
Not available
pgvector
October 12, 2026Package vulnerabilities
npm · pgvector@0.3.0 · PyPI · pgvector@0.5.1
0 vulnerabilities
across 2 packages
Repository security score
Not available
Interface Preview
Milvus
Feature Comparison
| Feature | Milvus | pgvector |
|---|---|---|
| Search Capabilities | ||
| Approximate Nearest Neighbor Search | Global Index with IVF, HNSW, and DiskANN algorithms optimized for billion-scale datasets | HNSW and IVFFlat indexes for ANN retrieval across millions of vectors |
| Distance Metrics | Supports cosine similarity, Euclidean distance, and inner product for embedding comparison | L2, inner product, cosine, L1, Hamming, and Jaccard distance with dedicated operators |
| Hybrid Search | Built-in metadata filtering combined with vector similarity search in a single query | Combines vector search with full SQL WHERE clauses, JOINs, and aggregations natively |
| Data Types & Storage | ||
| Vector Precision Options | Supports standard floating-point vectors with configurable dimensions for embeddings | Single-precision, half-precision, binary, and sparse vector types with dimension limits |
| Data Persistence | Cloud-native storage layer with separated compute providing stateless data persistence | Full PostgreSQL ACID compliance with point-in-time recovery and WAL-based replication |
| Schema Flexibility | Collection-based schema with typed fields for vectors, scalars, and metadata attributes | Standard PostgreSQL tables with vector columns alongside any relational data type |
| Scalability & Performance | ||
| Maximum Dataset Size | Designed for tens of billions of vectors with horizontal scaling across distributed nodes | Sweet spot of 1 million to 50 million vectors; not recommended for billions at millisecond latency |
| Index Build Performance | Distributed index building across cluster nodes for parallel construction at scale | Parallel index building with configurable maintenance_work_mem and worker threads |
| Query Optimization | Global Index retrieves data quickly and accurately regardless of dataset scale | PostgreSQL query planner selects optimal index strategy including iterative index scans |
| Deployment & Operations | ||
| Installation Method | Pip install for Lite, Docker Compose for Standalone, Helm charts for Distributed | Package manager install or compile from source on any PostgreSQL 13+ instance |
| Managed Cloud Option | Zilliz Cloud offers fully managed Milvus with serverless and dedicated cluster options | Available through managed PostgreSQL providers with pgvector pre-installed |
| High Availability | Distributed architecture with stateless components designed for elastic scaling and failover | Inherits PostgreSQL replication, streaming replicas, and standard HA configurations |
| Developer Experience | ||
| Client Libraries | Official Python SDK (pymilvus) with one-line deployment and extensive community notebooks | Works with any PostgreSQL client library in any language with a Postgres driver |
| Learning Curve | Purpose-built API requires learning collection management and Milvus-specific query syntax | Familiar SQL syntax with vector operators means minimal learning for PostgreSQL developers |
| Community & Ecosystem | Active community with guided notebooks for RAG, image search, multimodal, and Graph RAG | Over 22,000 GitHub stars with active development; latest stable release v0.8.2 in February 2026 |
Search Capabilities
Approximate Nearest Neighbor Search
Distance Metrics
Hybrid Search
Data Types & Storage
Vector Precision Options
Data Persistence
Schema Flexibility
Scalability & Performance
Maximum Dataset Size
Index Build Performance
Query Optimization
Deployment & Operations
Installation Method
Managed Cloud Option
High Availability
Developer Experience
Client Libraries
Learning Curve
Community & Ecosystem
Which approach fits
Milvus is the clear choice for teams building large-scale AI applications that need to handle billions of vectors with distributed infrastructure, while pgvector wins for organizations already running PostgreSQL that want to add vector search without introducing new infrastructure or operational overhead.
When each approach fits
Choose Milvus if:
We recommend Milvus for teams building dedicated AI and GenAI applications at scale. If your dataset will grow beyond 50 million vectors, you need horizontal scaling across multiple nodes, or you are building production systems like large-scale RAG pipelines, recommendation engines, or multimodal search applications, Milvus provides the distributed architecture and purpose-built performance to handle those workloads reliably.
Choose pgvector if:
We recommend pgvector for teams that already rely on PostgreSQL and want to add vector search capabilities without deploying a separate database. If your dataset fits within the 1 million to 50 million vector range, you value ACID compliance and the ability to combine vector similarity search with standard SQL queries, JOINs, and transactions, pgvector delivers powerful search with zero additional infrastructure cost.
These scenarios reflect the available product evidence. Your requirements, existing stack, and team expertise should guide the final decision.
Frequently Asked Questions
Can pgvector handle the same scale as Milvus for vector search?
No, the two tools are designed for different scale requirements. Milvus is built to handle tens of billions of vectors with horizontal scaling across distributed nodes, maintaining performance through its separated storage and computation architecture. pgvector is optimized for datasets in the 1 million to 50 million vector range with sub-second search latency. Beyond that threshold, pgvector may struggle with millisecond latency requirements. For most mid-scale applications, pgvector performs well, but organizations expecting rapid growth into the billions should plan for Milvus from the start.
Do I need to migrate away from PostgreSQL to use Milvus?
Not necessarily, but you will run two separate data systems. Milvus operates as a standalone vector database with its own storage layer, so your relational data stays in PostgreSQL while vector embeddings live in Milvus. This means you manage two systems and handle data synchronization between them. With pgvector, your vectors live alongside your relational data in the same PostgreSQL database, giving you ACID transactions, JOINs, and unified backups. The trade-off is simplicity versus scale: pgvector keeps everything in one place while Milvus provides dedicated vector infrastructure.
Which distance metrics does each tool support for similarity search?
pgvector supports a broader set of distance metrics out of the box, including L2 (Euclidean) distance, inner product, cosine distance, L1 (Manhattan) distance, Hamming distance for binary vectors, and Jaccard distance for binary vectors. Milvus supports cosine similarity, Euclidean distance, and inner product as its core metrics. For most AI embedding use cases, cosine similarity and L2 distance cover the primary needs, so both tools handle standard workloads well. pgvector's additional metrics like Hamming and Jaccard are useful for specialized binary vector applications.
What are the cost differences between running Milvus and pgvector in production?
pgvector is completely free and open-source with no paid tiers, running as an extension on your existing PostgreSQL infrastructure. Your only costs are the PostgreSQL server resources themselves. Milvus is also open-source for self-hosted deployments, but the distributed architecture requires more infrastructure: multiple nodes for compute, storage, and coordination services. For managed options, Zilliz Cloud offers fully managed Milvus with serverless and dedicated cluster tiers at enterprise pricing (contact for pricing). pgvector is available through many managed PostgreSQL providers at standard PostgreSQL hosting rates, making it significantly less expensive for small to mid-scale deployments.
Which tool is better for building RAG applications with LLMs?
Both tools work well for RAG (Retrieval Augmented Generation) applications, but the best choice depends on your scale and existing infrastructure. pgvector is excellent for RAG applications where you want to store document embeddings alongside metadata, use SQL to filter by document type or date, and keep everything in a single database. Milvus is better suited for RAG systems that need to search across very large document collections with billions of embeddings, require distributed deployment for high availability, or need to serve many concurrent queries with consistent low latency. For a team starting a new RAG project with moderate data volumes, pgvector offers the fastest path to production.