Vector databases companies, products & suppliers
Databases specialised for storing and querying embeddings. Jobs: embedding storage; ann query; metadata filters on vectors.
What are Vector databases?
Databases specialised for storing and querying embeddings. Jobs: embedding storage; ann query; metadata filters on vectors.
What problems does it solve?
Relational stores are a poor fit for nearest-neighbour retrieval at scale.
Typical business use cases
- Embedding storage
- ANN query
- Metadata filters on vectors
Important capabilities
- Index types
- Filtering
- HA
What buyers should evaluate
- Tenancy of vectors
- ACL on metadata
- Backup of embeddings
Risks and governance considerations
Confidential embeddings replicated to a region you did not choose.
Procurement checklist
- Region
- Encryption
- ACL
Relevant AI Trustmark assurance
AI Trustmark independent findings appear only when an assessment or certificate exists. Category membership does not imply verification.
Products and providers
Open a product profile for its catalogue details, or use the provider link for the company profile. Claimed listings appear first, then by name. Payment never changes the order.
Amazon S3 Vectors is purpose-built vector storage in Amazon S3 for AI agents, inference and semantic search. It provides dedicated APIs to store and query up to billions of vectors
By Cloudflare
Cloudflare Vectorize is Cloudflare's platform, from Cloudflare, Inc. Published at developers.cloudflare.com/vectorize. Directory filing: Vector databases. Vendor legal name on this
By Google
Vector Search is Google Cloud's managed vector search engine, now documented as part of Gemini Enterprise Agent Platform (formerly Vertex AI). It is built on Google Research's ScaN
By LanceDB
The multimodal lakehouse for AI, accelerating large-scale data curation and feature engineering so teams can build better models faster. LanceDB Enterprise is LanceDB's platform, f
By MyScale
Explore MyScale, the next-gen AI database fusing vector search with SQL analytics to deliver a streamlined, fully-managed, and high-performance experience. Unlock insights from mas
By ObjectBox
High-speed & lightweight database solution which securly stores your data privatly on-device and syncs it seamless to millions of devices. ObjectBox Vector Database is ObjectBox's
By Pinecone
Pinecone is the trusted AI knowledge company. Its AI knowledge platform—Database, Nexus, and Marketplace—powers accurate, fast, cost-effective AI for 10,000+ customers and 1M devel
By turbopuffer
vector and full-text search built on object storage: fast, 10x cheaper, and extremely scalable. turbopuffer is turbopuffer's platform, from turbopuffer Inc. Published at turbopuffe
By Upstash
Serverless Redis, Vector and Search databases with low latency and pay-as-you-go pricing. Upstash is the data platform for modern and AI applications — create a Redis database in s
By Weaviate
Bring AI-native applications to life with less hallucination, data leakage, and vendor lock-in. Weaviate is Weaviate's platform, from Weaviate B.V. Published at weaviate.io/develop
By Zilliz
Zilliz offers a fully managed Vector Lakebase powered by Milvus, unifying real-time vector search, lake-scale discovery, and AI data operations. Available across AWS, Google Cloud,
Products
Claimed products appear first. Ranking packs and payment do not change this list.
- Amazon S3 Vectors· Amazon Web Services
- Cloudflare Vectorize· Cloudflare
- LanceDB Enterprise· LanceDB
- Milvus· Zilliz
- MyScale Cloud· MyScale
- ObjectBox Vector Database· ObjectBox
- Pinecone Vector Database· Pinecone
- turbopuffer· turbopuffer
- Upstash Vector· Upstash
- Vertex AI Vector Search· Google
Also used in this category
These products have a different primary category so they do not compete for the same ranking queries. They are listed here because buyers still encounter them in this job.
- Pinecone Semantic Search· Pinecone
- Weaviate· Weaviate
Related categories
Relevant procurement and assurance guides
Frequently asked questions
What is Vector databases?
Databases specialised for storing and querying embeddings. Jobs: embedding storage; ann query; metadata filters on vectors.
What should not be listed as Vector databases?
Products whose buyer job is retrieval application stacks, embedding models, or general search products. Those belong on their own category page so search queries are not split.
Has AI Trustmark independently assessed every Vector databases supplier?
No. A category listing is descriptive. Independent assessment is shown only on company or product pages that carry Trustmark evidence.
What are Vector databases?
Databases specialised for storing and querying embeddings. Jobs: embedding storage; ann query; metadata filters on vectors.
What model or provider changes should a buyer insist on being told about?
Material change usually includes a new model family, new region, new subprocessor, new write-capable tool, or a change that affects logging, privacy or human oversight. Those changes should trigger evidence refresh rather than a silent release.
How should buyers verify where AI customer data is processed?
Ask for the named processing locations, cloud regions and any subprocessors that see prompts, files or outputs. A directory listing is not evidence of residency. Independent assessment records the locations that were in scope on the assessment date.
Does a TrustMark on one product cover the rest of the company?
No. Independent assessment is scoped to the named organisation and, where relevant, the named product. Category pages list suppliers as a topic label. They do not imply that every listed company has been assessed.
What incident-handling evidence is useful for AI suppliers?
Buyers should see how AI-specific failures are detected, contained and notified — including unsafe outputs, data leakage and unauthorised agent actions. An incident policy that never mentions models, prompts or tools is incomplete for this class of product.