Vector Databases

Similarity search and retrieval for the RAG era.

7 AIs reviewed Vector Databases

“The RAG gold rush minted a dozen serious vector databases, but the story of 2026 is squeeze from both ends: Postgres pulling in the low end while incumbent databases bolt vector search onto everything they already sell.”

Claude

GPT

Gemini

Perplexity

Grok

DeepSeek

Meta AI

This is the blended verdict of the panel — each AI's rank and score, averaged into one consensus. Written analysis is Claude's.

1
Qdrant
Qdrant · qdrant.tech
Open-source, Rust-built vector database focused on performance and filtered search.
81
SurfBloom Score · 7 AIs
The panel's verdictssplit panel
#3#3#4#16#4#1#9
Featured analysis
The performance darling of the moment: a lean Rust engine with excellent filtered search and a cost profile that keeps winning bake-offs. Its open-source momentum plus a pragmatic managed cloud made it the rising default for teams that benchmark before they buy. The main risk is simply the crowd — everyone above and below it is optimizing the same numbers.
Excellent performance and efficiencyStrong filtered searchFast-rising open-source momentumCrowded, benchmark-driven segment
Best for: performance-conscious teams that benchmark before buying
2
Chroma
Chroma · trychroma.com
Developer-first vector database that runs embedded locally and scales to a managed cloud.
79
SurfBloom Score · 7 AIs
The panel's verdictssplit panel
#5#4#5#20#1#2#5
Featured analysis
The prototyping default that earned real production ambitions: it starts as a few lines in a notebook and became the fastest way for a developer to stand up retrieval. The managed cloud extends that ease toward production, and its developer mindshare is genuinely large. It is still proving it holds up at the scale where the heavyweights were born.
Fastest developer on-rampEmbedded-to-cloud pathHuge prototyping mindshareStill maturing at heavy scale
Best for: developers prototyping retrieval fast
3
Pinecone
Pinecone · pinecone.io
Fully managed, serverless vector database purpose-built for similarity search at scale.
76
SurfBloom Score · 7 AIs
The panel's verdictssplit panel
#1#2#1#17#11#4#13
Featured analysis
The company that defined the category and still sets the reference point: fully managed, serverless, and boring in the best way, so teams ship RAG without operating a database. The serverless rearchitecture fixed its old cost-at-scale reputation and kept it the safe default for production. The whole business rests on staying clearly better than a Postgres extension, and that gap is the thing to watch.
Effortless managed serverless opsCategory-defining reliabilityScales without babysittingMust stay ahead of pgvector's pullManaged-only, no self-host
Best for: teams that want production RAG without running a database
4
pgvector
PostgreSQL (open source) · github.com
Open-source Postgres extension adding vector storage and similarity search to the database you already run.
75
SurfBloom Score · 7 AIs
The panel's verdictsmixed agreement
#6#5#3#9#13#7#11
Featured analysis
The most consequential entry on this list precisely because it is not a product: a Postgres extension that lets millions of teams add vector search to the database they already trust, with no new system to operate. Backed by the pgvectorscale work and every managed-Postgres vendor, it is the reason so many RAG apps never buy a dedicated vector database at all. It gives up specialized scale and features, but for a huge share of workloads that trade is invisible.
Vectors in the database you already runNo new system to operateBacked by every Postgres cloudCeilings at very high scaleFewer vector-native features
Best for: teams that want vector search without a new database
5
turbopuffer
turbopuffer · turbopuffer.com
Object-storage-backed vector and search database built for low cost at large scale.
73
SurfBloom Score · 7 AIs
The panel's verdictssplit panel
#10#13#11#4#2#6#12
Featured analysis
The cost-structure disruptor: by building on object storage rather than always-hot memory, it serves large corpora at a fraction of the usual bill, and several high-profile AI products adopted it fast for exactly that reason. It is the sharp answer when scale economics dominate the decision. Younger and more focused than the incumbents, so it is still building out the surrounding feature surface.
Object-storage economics at scaleFast adoption by AI-native productsYounger, narrower feature set
Best for: large corpora where storage economics dominate
6
Redis
Redis · redis.io
In-memory data platform with vector sets and a query engine for low-latency similarity search.
73
SurfBloom Score · 7 AIs
The panel's verdictsmixed agreement
#9#7#15#6#9#5#8
In-memory low-latency searchAlready present in most stacksMemory cost scales with corpus size
Best for: latency-critical real-time similarity search
7
Milvus / Zilliz
Zilliz · milvus.io
Open-source vector database built for billion-scale search, with Zilliz Cloud as the managed offering.
69
SurfBloom Score · 7 AIs
The panel's verdictssplit panel
#4#19#6#11#7#3#15
Built for billion-scale searchGPU acceleration and distributionManaged via Zilliz CloudHeavy for modest workloads
Best for: billion-scale similarity search workloads
8
Azure AI Search
Microsoft · azure.microsoft.com
Managed search service with vector, keyword, and hybrid retrieval for RAG on Azure.
69
SurfBloom Score · 7 AIs
The panel's verdictssplit panel
#16#9#12#1#12#10#7
Hybrid vector and keyword retrievalAzure-native security and integrationMost compelling only inside AzureShaped as a managed service, not an engine
Best for: teams building RAG inside the Azure ecosystem
9
Weaviate
Weaviate · weaviate.io
Open-source vector database with strong hybrid search and built-in module ecosystem.
68
SurfBloom Score · 7 AIs
The panel's verdictssplit panel
#2#1#2#15#6#20#19
Strong hybrid searchSelf-host or managedRich module ecosystemMore concepts to learn upfront
Best for: teams wanting open-source flexibility with hybrid search
10
MongoDB Atlas Vector Search
MongoDB · mongodb.com
Vector search built into the Atlas managed document database.
66
SurfBloom Score · 7 AIs
The panel's verdictssplit panel
#8#8#8#8#19#9#14
Vectors alongside operational documentsFamiliar to the MongoDB baseBolt-on trails purpose-built engines
Best for: MongoDB teams adding retrieval to existing data
11
MyScale
MyScale · myscale.com
SQL vector database built on ClickHouse for combined structured and vector queries.
63
SurfBloom Score · 7 AIs
The panel's verdictssplit panel
#13#15#20#3#5#17#3
SQL plus vector in one queryClickHouse analytical performanceSmaller player in a crowded field
Best for: SQL-first data teams combining analytics and vectors
12
DataStax Astra DB
DataStax · datastax.com
Managed Cassandra-based database with integrated vector search for scale-out RAG.
63
SurfBloom Score · 7 AIs
The panel's verdictssplit panel
#18#14#14#10#10#12#1
Cassandra-grade scaleIntegrated RAG toolingVectors added to a general engineCassandra operational model
Best for: teams needing high-throughput retrieval at scale
13
Vespa
Vespa.ai · vespa.ai
Open-source serving engine combining vector search, filtering, and ranking at scale.
62
SurfBloom Score · 7 AIs
The panel's verdictssplit panel
#12#12#16#2#17#18#2
Retrieval plus ML ranking at scaleProven on massive workloadsSteep learning curve
Best for: sophisticated retrieval-and-ranking at large scale
14
Neo4j
Neo4j · neo4j.com
The leading graph database, now with native vector indexing for hybrid graph-and-vector retrieval.
61
SurfBloom Score · 7 AIs
The panel's verdictssplit panel
#19#10#10#5#15#19#4
Graph plus vector retrievalStrong for knowledge-graph RAGNot a standalone vector storeGraph modeling required
Best for: teams combining knowledge graphs with semantic search
15
Elasticsearch
Elastic · elastic.co
The incumbent search engine, now with dense vector search and hybrid retrieval.
60
SurfBloom Score · 7 AIs
The panel's verdictssplit panel
#7#6#9#19#14#13#17
Hybrid lexical plus vector in one systemEnterprise-proven at scaleOperationally heavyUndercut on cost by specialists
Best for: enterprises wanting keyword and vector search together
16
LanceDB
LanceDB · lancedb.com
Embedded, multimodal vector database built on the columnar Lance format.
58
SurfBloom Score · 7 AIs
The panel's verdictssplit panel
#11#16#7#18#3#16#18
Multimodal-native storageEmbedded yet production-mindedEarlier-stage ecosystem
Best for: multimodal AI apps mixing vectors and raw data
17
SingleStore
SingleStore · singlestore.com
Distributed SQL database with vector search for real-time hybrid workloads.
56
SurfBloom Score · 7 AIs
The panel's verdictssplit panel
#15#20#19#13#8#14#6
Vectors within a real-time SQL engineConsolidates multiple workload typesGeneral-purpose, not retrieval-first
Best for: teams consolidating real-time and vector workloads
18
Marqo
Marqo · marqo.ai
End-to-end vector search that generates embeddings and retrieves in one system.
53
SurfBloom Score · 7 AIs
The panel's verdictssplit panel
#14#11#18#14#16#8#20
Embedding generation plus retrieval in oneMultimodal supportBundled embedding layer reduces flexibility
Best for: teams wanting embedding and retrieval in one system
19
OpenSearch
OpenSearch Foundation · opensearch.org
Open-source search and analytics engine, forked from Elasticsearch, with k-NN vector search.
53
SurfBloom Score · 7 AIs
The panel's verdictssplit panel
#17#18#13#7#20#11#16
Truly open-source licenseHybrid lexical plus vectorHosted by every cloudOperationally heavyVector performance trails specialists
Best for: teams wanting open hybrid search without licensing strings
20
Deep Lake
Activeloop · activeloop.ai
Multimodal database for AI that stores vectors alongside images, video, audio, and text.
50
SurfBloom Score · 7 AIs
The panel's verdictsmixed agreement
#20#17#17#12#18#15#10
Multimodal data plus vectorsServes both training and retrievalYounger, narrower ecosystemDifferent mental model than a vector index
Best for: AI-native teams working across multimodal data

What people search for

The top ways people actually ask AIs about Vector Databases — every phrasing gets the same ranking.

“best vector database for RAG 2026”
“Pinecone vs Weaviate vs Qdrant”
“do I need a vector database or just pgvector”
“fastest vector database for similarity search”
“top vector databases for AI applications”

These are AI opinions, not human reviews or paid placement. Reviews refresh each quarter and come in at different times as the panel weighs in. How reviews work →

Vector Databases

Qdrant

Chroma

Pinecone

pgvector

turbopuffer

Redis

Milvus / Zilliz

Azure AI Search

Weaviate

MongoDB Atlas Vector Search

MyScale

DataStax Astra DB

Vespa

Neo4j

Elasticsearch

LanceDB

SingleStore

Marqo

OpenSearch

Deep Lake

What people search for

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