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.

Methodology · How verification works

Companies and providers

Claimed suppliers appear first so buyers can start with listings the company has taken ownership of. Payment does not buy this order.

  • Amazon Web Services

    Amazon Web Services is Amazon's cloud division selling compute, storage, Bedrock model hosting and related AI infrastructure. Public pages cover regional cloud services for builder

  • Cloudflare

    Cloudflare publishes Cloudflare DLP, Cloudflare Agents SDK MCP Support, and Cloudflare Workers AI as named AI products. Cloudflare is used for Agent integration / MCP and AI agents

  • Google

    Google is a US technology company that sells consumer and cloud AI products, including Gemini, Search, and related developer APIs on Google Cloud. Google publishes Imagen, Gemini f

  • LanceDB

    The multimodal lakehouse for AI, accelerating large-scale data curation and feature engineering so teams can build better models faster. LanceDB publishes LanceDB Enterprise as nam

  • 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

  • ObjectBox

    High-speed & lightweight database solution which securly stores your data privatly on-device and syncs it seamless to millions of devices. ObjectBox publishes ObjectBox Vector Data

  • 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

  • turbopuffer

    vector and full-text search built on object storage: fast, 10x cheaper, and extremely scalable. turbopuffer is used for Vector databases work. turbopuffer publishes product informa

  • 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

  • Weaviate

    Bring AI-native applications to life with less hallucination, data leakage, and vendor lock-in. Weaviate is used for Retrieval infrastructure and Vector databases work. Weaviate is

  • 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.

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.

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.