Best multimodal AI databases for 2027

What a multimodal AI database is

This guide is published by the vendor of one of the listed tools; criteria and sources are listed so you can check the ranking.

The job is a database an application can write rows into and query: typed media or blob columns, metadata, embeddings, and a way to serve those rows. A lake, a document parser, a dataset app, and a batch dataframe do adjacent work. They are ranked on a companion platforms page, not here.

How this list is ranked

Order: first a Postgres extension if the app already lives there, then a native-media lake, then an application schema, then vector engines and a managed retriever. Native media types are a criterion, not the only sort key. Last reviewed: October 2026.

• Native media types on the row, versus blob URLs or vectors-only with files elsewhere.
• Embeddings and indexes on the same rows the app queries.
• Incremental updates when a source file or field changes.
• A path to serve the app (SQL, HTTP, or an embedded library).
• An open-source license you can read, and a hosted option if the vendor publishes one.

What changed going into 2027

Embedding models that share one space across text, images, video, and audio changed what a multimodal row can mean. Vendor notes below were checked on 25 October 2026.

Google Gemini Embedding 2 entered public preview on 10 March 2026 and general availability on 22 April 2026. It maps text, images, video, audio, and PDFs into one embedding space. PDF support (up to 6 pages per file) is in the embeddings docs. Sources: https://blog.google/innovation-and-ai/models-and-research/gemini-models/gemini-embedding-2/ , https://blog.google/innovation-and-ai/models-and-research/gemini-models/gemini-embedding-2-generally-available/ , and https://ai.google.dev/gemini-api/docs/embeddings

Amazon Bedrock Knowledge Bases added multimodal retrieval with Amazon Nova Multimodal Embeddings (general availability announced 20 January 2026). Source: https://aws.amazon.com/blogs/machine-learning/introducing-multimodal-retrieval-for-amazon-bedrock-knowledge-bases/

Lance Blob V2 (11 March 2026) treats images, audio, video, and PDFs as first-class blob columns in the Lance lakehouse, with inline, packed, dedicated, and external-URI layouts. Source: https://www.lancedb.com/blog/lance-blob-v2

Milvus 3.0 (announced 16 July 2026) indexes vectors that remain in object storage and adds richer retrieval than nearest-neighbor alone. Source: https://zilliz.com/news/milvus-3-0-lake-native-vector-database

pgvector 0.8.7 was tagged 1 October 2026. 0.9.0 is listed as unreleased in the changelog. Source: https://github.com/pgvector/pgvector/blob/master/CHANGELOG.md

The databases, in rank order

Official sources were checked on 25 October 2026.

PostgreSQL with pgvector adds vector columns, exact or HNSW/IVFFlat search, and SQL joins with the rest of the row. Managed options include Supabase and Neon. Media usually lives in object storage you add. Docs: https://github.com/pgvector/pgvector , https://supabase.com/docs/guides/database/extensions/pgvector , https://neon.tech/docs/extensions/pgvector

LanceDB calls itself a multimodal lakehouse for AI: images, video, audio, text, embeddings, and features in one Lance table, embedded as OSS or as Enterprise. Processing and HTTP still sit in jobs you write unless you add them. Docs: https://docs.lancedb.com/

Weaviate is an open-source vector database of objects and named vectors, with hybrid BM25 plus dense search and multimodal vectorizer modules (CLIP locally; Google and others via API, including gemini-embedding-2 for near_media). Weaviate Cloud is the hosted option. Docs: https://docs.weaviate.io/weaviate/model-providers/transformers/embeddings-multimodal , https://docs.weaviate.io/weaviate/search/hybrid , https://docs.weaviate.io/weaviate/search/near-media

Qdrant is an open-source vector search engine written in Rust, with payload filters, dense and sparse vectors, and Qdrant Cloud. Rows are vectors plus payload, not typed video columns. Docs: https://qdrant.tech/documentation/

Milvus is an open-source vector database (Apache-2.0, LF AI & Data) for organizing and searching unstructured data such as text, images, and multi-modal information. Zilliz Cloud is the managed service. Docs: https://github.com/milvus-io/milvus/

Mixpeek is a managed retrieval API: it reads video, images, audio, and documents in your bucket and searches scenes, speech, and on-screen text over HTTP. It is not a SQL database you operate. Site: https://www.mixpeek.com/

Last reviewed

Facts on this page were checked against vendor documentation on 25 October 2026. Product surfaces move; follow the official links in Related.

Multimodal AI databases ranked for a user-facing app (last reviewed: Oct 2026)

Where Pixeltable fits

This page is published by Pixeltable, one of the tools listed. Pixeltable ranks third here: typed Image, Video, Audio, and Document columns, embedding indexes on those rows, and FastAPIRouter in the same file (PyPI 0.7.20, Apache-2.0, Python 3.11+; pip install -U 'pixeltable[serve]'; pxt CLI; TableModel schema; Pixeltable Cloud). It is not a vector database and not a labeling tool. Pick PostgreSQL plus pgvector when Postgres already holds the app. Pick LanceDB when Lance layout and ANN are the product. Pick Weaviate, Qdrant, or Milvus when that engine is the serving contract. Pick Mixpeek when you want a managed retriever over a bucket. A wider platforms list (lakes, parsers, dataframes) is Best multimodal data platforms. For agents, see Backend for AI agents. Docs: https://docs.pixeltable.com/platform/type-system and https://docs.pixeltable.com/cloud

import pixeltable as pxt
from pixeltable.functions.document import document_splitter
from pixeltable.functions.huggingface import sentence_transformer
from pixeltable.serving import FastAPIRouter

TableModel = pxt.model_base()
embed = sentence_transformer.using(
    model_id='sentence-transformers/all-MiniLM-L6-v2'
)

class Docs(TableModel, name='docs'):
    document: pxt.Document
    title: pxt.String

class Chunks(
    TableModel,
    name='chunks',
    base=Docs,
    iterator=document_splitter(
        document=Docs.document, separators='sentence,token_limit', limit=512
    ),
):
    __indexes__ = [pxt.EmbeddingIndex(text, string_embed=embed)]

@pxt.query
def search_docs(query: str, limit: int = 5):
    sim = Chunks.text.similarity(string=query)
    return (
        Chunks.order_by(sim, asc=False)
        .limit(limit)
        .select(Chunks.text, Chunks.title, sim)
    )

api = FastAPIRouter(name='docs')
api.add_insert_route(Docs, path='/docs', inputs=[Docs.document, Docs.title])
api.add_query_route(path='/search', query=search_docs)

Questions

Do I need a separate vector database?

You need nearest-neighbor search. That can live as an index on the table that holds the media, or as a dedicated vector database. Keep a separate engine when hosted ANN, hybrid fusion, or a query SLA is the contract. Pixeltable declares EmbeddingIndex on the source table; it does not replace Pinecone or Milvus at those jobs.

Postgres plus pgvector, or a multimodal database?

If Postgres already holds the app, pgvector is the usual first choice: SQL joins, mature ops, Supabase or Neon as hosts. You still store files somewhere and keep embeddings in sync. A multimodal database belongs when typed media, transforms, and the index should share one schema. See Pixeltable vs Supabase and Pixeltable vs Neon.

Where do the video files live?

In a typed video column they stay attached to the row (bytes in a media store or object storage the engine points at). In a vectors-only database the files usually live in a bucket, and the row holds an embedding plus a URL. Lance stores blobs in the Lance table. Mixpeek reads the bucket you already have.

Is this list biased?

Yes, in the sense that Pixeltable publishes it and ranks itself third. The criteria are listed above. Competitors sit above Pixeltable where they fit better: Postgres for teams already there, LanceDB for the lake. Check the source URLs.

Which is best for agents?

Agents need a durable store they can write and a tool they can call. A one-file schema with HTTP routes is one shape; a managed retrieval API is another; Postgres plus tools is a third. See Backend for AI agents. This page ranks databases, not agent frameworks.