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This page introduces the vector databases supported on Vertex AI RAG Engine.
You can also see how to connect a vector database (vector store) to your RAG
corpus.
Vector databases play a crucial role in enabling retrieval for RAG applications.
Vector databases offer a specialized way to store and query vector embeddings,
which are mathematical representations of text or other data that capture
semantic meaning and relationships. Vector embeddings allow RAG systems to
quickly and accurately find the most relevant information within a vast
knowledge base, even when dealing with complex or nuanced queries. When combined
with an embedding model, vector databases can help overcome the limitations of
LLMs, and provide more accurate, relevant, and comprehensive responses.
Supported vector databases
When creating a RAG corpus, Vertex AI RAG Engine offers the
enterprise-ready RagManagedDb as the default vector database, which requires
no additional provisioning or managing.
RagManagedDb offers both KNN and ANN search options and
allows switching to a basic tier for some quick prototyping and experimentation.
To learn more about choosing a retrieval strategy on RagManagedDb or for
updating the tier, see Use RagManagedDb with
RAG. For
Vertex AI RAG Engine to automatically create and manage the
vector database for you, see Create a RAG
corpus.
In addition to the default RagManagedDb, Vertex AI RAG Engine
lets you provision and use your vector database within your RAG corpus. In this
case, you are responsible for the lifecycle and scalability of your vector
database.
Compare vector database options
This table lists your choices of vector databases that are supported within
Vertex AI RAG Engine and provides links to pages that explain how
to use the vector databases within your RAG corpus.
Vector database
Benefits
Best for
Disadvantages
Supported distance metrics
Search type
Launch stage
RagManagedDb (default) is a regionally-distributed scalable database service that offers very high consistency and high availability and can be used for a vector search.
easy simple fast quick
No setup required.
Good for enterprise-scale and small-scale use cases.
Very high consistency.
High availability.
Low latency.
Excellent for transactional workloads.
CMEK enabled.
Generating high-volume documents.
Building enterprise-scale RAG.
Developing a quick proof of concept.
Providing low provisioning and maintenance overhead.
Using with chat bots.
Building RAG applications.
For optimal recall, the ANN feature requires that the index
be rebuilt after major changes to your data.
cosine
KNN (default) and ANN
Preview
Vector Search is the vector database service within Vertex AI that's optimized for machine-learning tasks.
Integrates with other Google Cloud services.
Scalability and reliability are supported by Google Cloud infrastructure.
Uses pay-as-you-go pricing.
Generating high-volume documents.
Building enterprise-scale RAG.
Managing vector database infrastructure.
Existing Google Cloud customers or anyone looking to use multiple Google Cloud services.
Updates aren't reflected immediately.
Vendor lock-in with Google Cloud.
Could be more expensive depending on your use cases.
cosine
dot-product
ANN
Generally available
Vertex AI Feature Store
is a managed service for organizing, storing, and serving machine-learning features.
Integrates with Vertex AI and other Google Cloud services.
Scalability and reliability are supported by Google Cloud infrastructure.
Leverages existing BigQuery infrastructure.
Generating high-volume documents.
Building enterprise-scale RAG.
Managing vector database infrastructure.
Existing Google Cloud customers or customers looking to use multiple Google Cloud services.
Changes are only available in the online store after a manual synchronization is performed.
Vendor lock-in with Google Cloud.
cosine
dot-product
L2 squared
ANN
Preview
Weaviate
is an open-source vector database that's flexible and modular.
Supports various data types and offers built-in graph capabilities.
Provides open source and a vibrant community.
Highly flexible and customizable.
Supports diverse data types and modules for different modalities, such as text and images.
Can choose among Cloud providers, such as Google Cloud, AWS, and Azure.
Generating high-volume documents.
Building enterprise-scale RAG.
Managing vector database infrastructure.
Existing Weaviate customers.
Updates aren't reflected immediately.
Can be more complex to set up and manage.
Performance can vary depending on the configuration.
cosine
dot-product
L2 squared
hamming
manhattan
ANN + Hybrid search support
Preview
Pinecone
is a fully-managed cloud-native vector database designed for a high-performance similarity search.
Get started quickly.
Excellent scalability and performance.
Focus on vector search with advanced features like filtering and a metadata search.
Can choose among Cloud providers, such as Google Cloud, AWS, and Azure.
Generating high-volume documents.
Building enterprise-scale RAG.
Managing vector database infrastructure.
Existing Pinecone customers.
Updates aren't reflected immediately.
Can be more expensive than other options.
Quotas and limits restrict scale and performance.
Limited control over the underlying infrastructure.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Hard to understand","hardToUnderstand","thumb-down"],["Incorrect information or sample code","incorrectInformationOrSampleCode","thumb-down"],["Missing the information/samples I need","missingTheInformationSamplesINeed","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2025-08-27 UTC."],[],[],null,["# Vector database choices in Vertex AI RAG Engine\n\n| The [VPC-SC security controls](/vertex-ai/generative-ai/docs/security-controls) and\n| CMEK are supported by Vertex AI RAG Engine. Data residency and AXT security controls aren't\n| supported.\n\nThis page introduces the vector databases supported on Vertex AI RAG Engine.\nYou can also see how to connect a vector database (vector store) to your RAG\ncorpus.\n\nVector databases play a crucial role in enabling retrieval for RAG applications.\nVector databases offer a specialized way to store and query vector embeddings,\nwhich are mathematical representations of text or other data that capture\nsemantic meaning and relationships. Vector embeddings allow RAG systems to\nquickly and accurately find the most relevant information within a vast\nknowledge base, even when dealing with complex or nuanced queries. When combined\nwith an embedding model, vector databases can help overcome the limitations of\nLLMs, and provide more accurate, relevant, and comprehensive responses.\n\nSupported vector databases\n--------------------------\n\nWhen creating a RAG corpus, Vertex AI RAG Engine offers the\nenterprise-ready `RagManagedDb` as the default vector database, which requires\nno additional provisioning or managing.\n`RagManagedDb` offers both KNN and ANN search options and\nallows switching to a basic tier for some quick prototyping and experimentation.\nTo learn more about choosing a retrieval strategy on `RagManagedDb` or for\nupdating the tier, see [Use `RagManagedDb` with\nRAG](/vertex-ai/generative-ai/docs/rag-engine/use-ragmanageddb-with-rag). For\nVertex AI RAG Engine to automatically create and manage the\nvector database for you, see [Create a RAG\ncorpus](/vertex-ai/generative-ai/docs/model-reference/rag-api#create-a-rag-corpus-params-api).\n\nIn addition to the default `RagManagedDb`, Vertex AI RAG Engine\nlets you provision and use your vector database within your RAG corpus. In this\ncase, you are responsible for the lifecycle and scalability of your vector\ndatabase.\n\nCompare vector database options\n-------------------------------\n\nThis table lists your choices of vector databases that are supported within\nVertex AI RAG Engine and provides links to pages that explain how\nto use the vector databases within your RAG corpus. \n\nWhat's next\n-----------\n\n- To create a RAG corpus, see [Create a RAG corpus\n example](/vertex-ai/generative-ai/docs/model-reference/rag-api#create-a-rag-corpus-example-api).\n- To list all of the RAG corpora, see [List RAG corpora\n example](/vertex-ai/generative-ai/docs/model-reference/rag-api#list-rag-corpora-example-api)."]]