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Vertex AI Vector Search

cloud.google.com

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Spec score 7.0 #4 of 29 in AI Model Explainability Software

What it is

Vertex AI Vector Search is a cloud vector search engine for semantic search, recommendations, and generative AI applications. It uses Google Research’s ScaNN algorithm and supports dense embeddings, sparse embeddings, and hybrid search combining semantic and keyword methods. Numeric and text attribute filters can restrict results. Indexes can be deployed to public or private endpoints, including Private Service Connect, which provides private access from multiple user VPC networks. Documented integrations include RAG Engine, Cloud Storage, Cloud Run, TensorFlow Recommenders, and LlamaIndex. Google documents Python and Java clients, a Python SDK, and REST workflows. Security controls include VPC Service Controls and customer-managed encryption keys. Listed uses include retrieval-augmented generation, chatbots, recommendations, and multimodal search. Pricing is usage based: serving uses node-hour charges, with separate build, update, and streaming charges. Build and update data costs $3.00/GiB; streaming inserts cost $0.45/GiB.

Spec sheet

Purpose
Vector Search is a vector search engine using Google Research’s ScaNN algorithm for search, recommendations, and generative AI applications.Read 8 Oct 2026 · cloud.google.com
Integrations
Google documents use of Vector Search with RAG Engine, Cloud Storage, Cloud Run, TensorFlow Recommenders, and LlamaIndex.Read 8 Oct 2026 · cloud.google.com
Security
Vector Search supports VPC Service Controls and customer-managed encryption keys, among other Google Cloud security controls.Read 8 Oct 2026 · cloud.google.com
Compliance
Google’s compliance scope page lists Vector Search under Gemini Enterprise Agent Platform as under DISA review for DoD IL4 and IL5.Read 8 Oct 2026 · docs.cloud.google.com
Support
Google provides support guidance for Vector Search and directs users to include diagnostic details when reporting problems.Read 8 Oct 2026 · docs.cloud.google.com
APIs and SDKs
The documentation links Python and Java clients, the Python SDK, and REST API workflows.Read 8 Oct 2026 · cloud.google.com
Endpoints
Users can deploy an index to a public or private endpoint, including Private Service Connect configurations.Read 8 Oct 2026 · docs.cloud.google.com
Filtering
Searches can be restricted using numeric filtering and text attribute filtering.Read 8 Oct 2026 · docs.cloud.google.com
Intended use
Google describes use cases including semantic search, recommendations, retrieval-augmented generation, chatbots, and multimodal search.Read 8 Oct 2026 · cloud.google.com
Limits
Per region and project, quotas include 5 concurrent index creations, 5 concurrent index updates, 50 deployed index nodes, and 100 indexes.Read 8 Oct 2026 · docs.cloud.google.com
Private connectivity
Private Service Connect supports private access to serving backends from multiple user VPC networks.Read 8 Oct 2026 · docs.cloud.google.com
Search modes
It supports dense embeddings, sparse embeddings, and hybrid search combining semantic and keyword search.Read 8 Oct 2026 · docs.cloud.google.com
Trial credits
The overview page states that new Google Cloud customers can receive $300 in free credits to try Google Cloud products.Read 8 Oct 2026 · cloud.google.com

Plans and prices

Vertex AI Vector Search Price not published Usage based; serving is charged per node hour, with separate index build, update, and streaming update charges. price varies by machine type and region · build/update data processed: $3.00/GiB in all regions · streaming inserts: $0.45/GiB Read 8 Oct 2026 · cloud.google.com

Every Vertex AI Vector Search plan and price →

Strong points and limits

Strong points

  • Dense, sparse, and hybrid search
  • Numeric and text filtering
  • Public or private index endpoints
  • Private Service Connect support
  • Python and Java clients

Limits

  • Usage-based pricing has no listed serving rate
  • Regional quotas limit indexes and nodes
  • Cloud deployment is required

Who it suits

It suits teams building semantic or hybrid search, recommendation, retrieval-augmented generation, chatbot, and multimodal applications on Google Cloud. Developers can use Python, Java, or REST workflows.

Verdict

Vertex AI Vector Search provides multiple vector modes, filtering, private connectivity, and documented integration paths. Costs depend on serving, machine type, region, and data-processing operations.

Where Specifiction places it

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