A Comparative Guide to Leading Vector Databases
| Database | License & Hosting | Scale & Performance | Filtering & Search | Ecosystem & Tooling | Strengths | Caveats |
|---|---|---|---|---|---|---|
| Qdrant | Apache-2, Self-host & Cloud | ⚡️ <3ms p50 (1M vec) 💾 Quantization cuts RAM ~30x |
Payload-aware filters Multi-vector per doc |
HTTP & gRPC APIs LangChain/LlamaIndex |
✅ High RPS ✅ Multi-vector out-of-box |
⚠️ Newer managed cloud ⚠️ Fewer enterprise RBAC |
| Milvus | Apache-2 & Zilliz Cloud | 📈 Billions of vectors 🖥️ CPU/GPU HNSW, IVF-PQ |
Hybrid sparse + dense Boolean filters |
Dozens of RAG tutorials | ✅ Mature scaling ✅ Laptop → Cluster |
⚠️ On-prem ops complexity ⚠️ Cloud vendor lock-in |
| Weaviate | Apache-2, Self-host & Serverless | Go core; auto-sharding Scales to billions |
BM25 + vector "hybrid" Metadata & geo filters |
GraphQL & REST APIs Agents SDK, RAG e-book |
✅ Turn-key vectorization ✅ Multi-tenant RBAC, SOC 2 |
⚠️ Higher write latencies at scale |
| Faiss | BSD-2 (Library, not DB) | 🚀 Fastest raw ANN on GPU | None; must build custom | Python/C++ wrappers Underpins many DBs |
✅ Ultimate algorithm control ✅ No network hop |
⚠️ No persistence/auth ⚠️ You build the server |
| Chroma | Apache-2, Local & Cloud | Single-node (~100M vec) ~20ms p50 (100k vec) |
Metadata & full-text Multimodal support |
3-line Python API Built into LangChain |
✅ Easiest local prototyping ✅ "Works out-of-the-box" |
⚠️ Single-node today ⚠️ Distributed in preview |
| Pinecone | Proprietary; Managed Serverless | 12B vectors GA O(10 ms) low-latency reads |
High-recall metadata filters | Batteries-included docs Dashboard & RAG primers |
✅ Zero-ops, autoscaling ✅ Cross-region replication |
⚠️ Usage-based cost ⚠️ Closed-source, no self-host |
| pgvector | PostgreSQL License | Tens of millions Inherits ACID, PITR |
SQL WHERE clauses Joins with GIS/JSONB |
Works via any Postgres driver | ✅ Keep vectors with relational data ✅ Simplicity |
⚠️ Table bloat under writes ⚠️ IVFFlat tuning required |
| LanceDB | Apache-2, Local & Cloud | Columnar "Lance" format Sub-ms lookups |
SQL-like filters ANN & brute-force |
Python/TypeScript SDKs Graph-RAG template |
✅ Multimodal Lakehouse vision ✅ Search → EDA → Training |
⚠️ Younger community ⚠️ Fewer 3rd-party integrations |
| Vespa | Apache-2 & Vespa Cloud | Proven at Yahoo/Perplexity <100ms at K QPS |
Native BM25 + dense Tensor ranking |
YQL, REST & gRPC LangChain/LlamaIndex |
✅ Unified ranking (text, tensor) ✅ Real-time in-cluster inference |
⚠️ Steep ops footprint (Java) ⚠️ Learning curve for config |
| Vald | Apache-2 (Helm charts) | NGT index; auto-index Scales to billions on K8s |
gRPC hooks for custom filters | Go/Java/Python/Node SDKs Grafana dashboards |
✅ Kubernetes-first ✅ Auto-scaling, backups |
⚠️ Smaller community ⚠️ Limited hybrid search |
| Elasticsearch | Elastic License & Cloud | Lucene 9.9 HNSW Sub-10ms p50 (10M vec) |
BM25 + vector hybrid Rich bool/geo filters |
Huge ecosystem; ESQL Kibana, LangChain plugin |
✅ "All-in-one" solution ✅ Mature RBAC & observability |
⚠️ SSPL-like license ⚠️ Can be resource-heavy |
| Redis | BSD-3 OSS & Enterprise | In-memory KNN 62% higher QPS than peers |
RediSearch KNN Mix with JSON/full-text |
Clients in all languages LangChain, llama-cpp |
✅ Lowest latency (μs–ms) ✅ Ideal for real-time RAG |
⚠️ Memory-bound (unless Flash) ⚠️ Some features Enterprise-only |
Feel free to benchmark with your own embeddings and workload to pick the best fit!