Best vector databases for LLM apps (2026) | Dashpick

Full ranking

1. #1

   Pinecone

   Fully managed vectors with strong docs and quick starts—default when you want serverless ops and predictable APIs over running your own cluster.

   Average score: 7.6/10

   • Great when you’d rather pay SaaS than hire vector SREs
   • Pricing can climb with namespaces and queries—model your growth
   • Hybrid filtering improves constantly—verify against your metadata schema

   Detailed scores by criterion(expand)

   Criterion	Score
   Query latency	8/10
   Scale & sharding	8/10
   Developer experience	9/10
   Total cost	6/10
   Hybrid / filters	7/10

2. #2

   Weaviate

   Open-core engine with GraphQL-style APIs and modules—flexible when you want hybrid search patterns and optional self-hosting.

   Average score: 7.8/10

   • Module ecosystem for rerankers and encoders
   • Self-host path appeals to regulated industries
   • Ops complexity rises with cluster size—plan capacity early

   Detailed scores by criterion(expand)

   Criterion	Score
   Query latency	7/10
   Scale & sharding	8/10
   Developer experience	8/10
   Total cost	7/10
   Hybrid / filters	9/10

3. #3

   Qdrant

   Rust-based engine known for efficient filtering and solid cloud/self-host options—popular with teams that want performance without mega-vendor lock-in.

   Average score: 8/10

   • Strong price/performance story for many RAG prototypes
   • Client libraries mature across languages
   • Validate backup and upgrade playbooks if self-hosting

   Detailed scores by criterion(expand)

   Criterion	Score
   Query latency	8/10
   Scale & sharding	8/10
   Developer experience	8/10
   Total cost	8/10
   Hybrid / filters	8/10

4. #4

   Milvus

   Distributed vector database for huge corpora—when billion-scale and batch indexing dominate your architecture reviews.

   Average score: 7.8/10

   • Aim at teams with platform engineers who enjoy tuning clusters
   • Overkill for tiny RAG demos—complexity has a tax
   • Pairs with mature Kubernetes patterns in larger orgs

   Detailed scores by criterion(expand)

   Criterion	Score
   Query latency	7/10
   Scale & sharding	10/10
   Developer experience	7/10
   Total cost	7/10
   Hybrid / filters	8/10

5. #5

   pgvector

   Postgres extension for vectors—best when you already bet on SQL, transactions, and joining embeddings to relational truth.

   Average score: 8.4/10

   • One database for users, permissions, and vectors simplifies many apps
   • Need tuning and indexing strategy for large embedding tables
   • Familiar backup/restore story for DBAs

   Detailed scores by criterion(expand)

   Criterion	Score
   Query latency	7/10
   Scale & sharding	7/10
   Developer experience	9/10
   Total cost	9/10
   Hybrid / filters	10/10

6. #6

   Redis Vector

   Low-latency similarity inside Redis—great when you already run Redis for caching/session and want colocated vector search.

   Average score: 7.8/10

   • Excellent for hot, small-ish indexes next to application state
   • Memory costs bite at large dimensions—watch footprint
   • Not a full analytics warehouse—pair with the right persistence tier

   See comparisons

   • Redis vs Memcached

   Detailed scores by criterion(expand)

   Criterion	Score
   Query latency	9/10
   Scale & sharding	7/10
   Developer experience	8/10
   Total cost	7/10
   Hybrid / filters	8/10

7. #7

   Chroma

   Developer-friendly embedded/server options for fast prototypes—less about massive production scale than getting RAG running in an afternoon.

   Average score: 7.6/10

   • Great teaching tool and hackathon default
   • Stress-test before betting multi-tenant production loads on it
   • Community moves quickly—pin versions

   Detailed scores by criterion(expand)

   Criterion	Score
   Query latency	7/10
   Scale & sharding	6/10
   Developer experience	9/10
   Total cost	9/10
   Hybrid / filters	7/10

8. #8

   LanceDB

   Embedded columnar vector store tuned for lakehouse-style data—interesting when your vectors live next to Parquet on object storage.

   Average score: 7.8/10

   • Nice fit for analytics-heavy teams already on object stores
   • Different ops model than classic servers—read their deployment guides
   • Evaluate concurrency needs vs embedded assumptions

   Detailed scores by criterion(expand)

   Criterion	Score
   Query latency	8/10
   Scale & sharding	7/10
   Developer experience	7/10
   Total cost	9/10
   Hybrid / filters	8/10