Vector Databases

What is a vector database company?

A vector database company builds infrastructure that stores and searches embeddings — the high-dimensional numerical representations that AI models produce for text, images, audio, and other unstructured data. Instead of matching exact keywords, a vector database finds records that are semantically similar by comparing these embeddings with approximate nearest-neighbor (ANN) search, which is what powers retrieval-augmented generation (RAG), semantic search, recommendation engines, and long-term memory for AI agents.

This makes vector databases a foundational layer of the modern AI stack: almost every production RAG pipeline pairs a foundation model or generative AI model with a vector database that supplies it with relevant, up-to-date context at query time, since the model itself has no memory of a company's private documents.

Vector database vs. traditional database

A traditional relational or document database is built to retrieve rows that match an exact value or a defined range. A vector database is built to answer a different question — which of these million items is most similar to this one — which requires specialized indexing structures (such as HNSW or IVF) that trade a small amount of recall for orders-of-magnitude faster search at scale, plus hybrid filtering so a query can combine semantic similarity with traditional metadata filters such as date, category, or permissions in one pass.

Types of vector database companies

The market splits by deployment model and how much of the surrounding stack the vendor owns.

Fully-managed cloud vector databases

Pinecone popularized the fully-managed, serverless vector database category — teams send embeddings over an API and never manage index infrastructure themselves, trading control for operational simplicity.

Open-source vector databases

Weaviate, Qdrant, and Zilliz (creator of the widely-used open-source Milvus project) publish open-source cores that teams can self-host or run as a managed cloud service, giving buyers a choice between full control and a managed offering from the same vendor.

Embedded and serverless vector search

Chroma and LanceDB are developer-first, embeddable vector databases designed to run inside an application process or scale down between queries, aimed at teams that want vector search without operating a separate database cluster. Turbopuffer takes a serverless, object-storage-backed approach that separates compute from storage to keep large-scale vector search cost-efficient.

Large-scale enterprise search infrastructure

Vespa.ai, originally built at Yahoo for web-scale search and recommendation, combines vector search with traditional ranking and structured data at a scale few competitors have proven in production.

Multimodal and tensor-native databases

Activeloop's Deep Lake stores and streams multimodal data — embeddings alongside the original images, video, and audio they were generated from — aimed at teams training and evaluating models, not just serving retrieval queries.

Embedding and vector-compute layers

Superlinked sits a level above the databases above: it turns structured and unstructured data into the vector embeddings themselves, then plugs into existing vector or general-purpose databases, addressing the embedding-generation step that most vector databases assume is already done.

AI-native application search

Marqo packages vector search, embedding generation, and reranking into a single API aimed at teams building product search and discovery, rather than requiring buyers to assemble an embedding model and a vector database themselves.

How vector database companies charge

• Usage-based cloud pricing. Fully-managed vendors typically price on stored vectors, queries, or compute units, scaling with read/write volume.
• Open-source, self-hosted. The open-source cores behind Weaviate, Qdrant, and Milvus are free to run yourself; cost shows up as infrastructure and engineering time rather than a subscription.
• Managed cloud tier on top of open source. Several vendors sell a managed cloud version of their own open-source project, letting a team start self-hosted and migrate to a managed tier without changing databases.
• Storage-separated serverless pricing. Object-storage-backed vendors price storage and compute separately, which can be materially cheaper for large, infrequently-queried vector collections.

How to choose a vector database company

Benchmark claims vary enormously by dataset shape and hardware, so evaluate shortlisted vendors on these criteria against your own embeddings instead:

• Scale and latency on your data. Recall/latency trade-offs shift with vector dimensionality and collection size — test with your actual embedding model and volume, not a published benchmark.
• Hybrid search support. Confirm the database can combine vector similarity with metadata filters and, where needed, traditional keyword search in a single query.
• Deployment model. Decide up front whether you need fully-managed simplicity, open-source control, or a serverless storage-separated model for cost at scale.
• Multimodal requirements. If you need to store and query the original images, audio, or video alongside embeddings — not just the vectors — a tensor-native database is a better fit than a pure ANN store.
• Embedding generation. Decide whether you need a vendor that only stores and searches vectors you supply, or one (like Marqo or Superlinked) that also generates the embeddings.
• Ecosystem integration. Check native SDKs and integrations with the orchestration framework, foundation model provider, and generative AI stack you already use.

Most teams prototype against two or three vector databases with their own embeddings and query patterns before committing, since public benchmarks rarely reflect how an index performs against one company's specific data and traffic. The directory below lists the vector database companies building this layer of the AI stack so you can compare them in one place.

Discover leading companies in vector databases that provide specialized artificial intelligence solutions and services. Our directory features verified vendors with proven expertise in delivering AI-powered capabilities to businesses across industries.

Each listed company has been evaluated based on their technical capabilities, industry experience, and customer success stories. Compare providers to find the right partner for your AI initiatives. Explore our AI company directory to discover more categories and vendors.