Vector databases compared
Vector database choice depends on hosting model, hybrid search needs, operations capacity, scale, and governance requirements.
| Criterion | Qdrant | Weaviate | Milvus | Chroma | Pinecone | Elasticsearch / OpenSearch |
|---|---|---|---|---|---|---|
| ease of use | Developer-friendly | Feature-rich | Scale-oriented | Simple for prototypes | Managed service | Familiar to search teams |
| target user | AI engineers | AI/search teams | Platform teams | Learners/prototypers | Managed-service teams | Enterprise search teams |
| visual workflow support | External | External | External | External | External | Dashboards vary by product |
| developer flexibility | High | High | High | Good | Service API | High search stack flexibility |
| RAG features | Vector search and filtering | Vector and hybrid search | Large-scale vector search | Embedding store | Managed vector search | Keyword/vector/hybrid retrieval |
| agentic workflow support | External | External | External | External | External | External |
| integrations | Broad | Broad | Broad | Common in frameworks | Broad | Broad in enterprise search |
| self-hosting | Yes | Yes | Yes | Yes | No for Pinecone service | Yes, product-dependent |
| production readiness | Commonly used | Commonly used | Scale-oriented | Validate for workload | Managed production service | Mature search infrastructure |
| learning curve | Moderate | Moderate | Higher operations | Low | Low to moderate | Moderate to high |
| best use cases | Filtered vector RAG | Hybrid RAG | Large-scale workloads | Local prototypes | Managed vector search | Hybrid enterprise search |