Google Vertex AI Search
A platform for building intelligent search systems across websites and organizational content
- Vendor
- Google Cloud
- Also known as
- google-vertex-ai-search
Available worldwide · Popular in: US
What is Google Vertex AI Search?
A Google-managed cloud solution that enables rapid development of search engines powered by generative AI and retrieval-augmented generation, supporting indexing and searching across websites and enterprise data.
Google Vertex AI Search pricing
We don't have Google Vertex AI Search's full plan breakdown yet (its pricing page resisted automated reading). Here's what we could confirm. Always check live pricing for exact numbers.
Usage-based pricing for tools, storage, compute, and cloud resources. New customers receive $300 in free credits.
What Google Vertex AI Search does
The capabilities that matter for rag tools, normalised so it lines up with every alternative. “-” means we haven't confirmed it, not that it's missing.
- Hybrid retrieval
- -
- Reranking integration
- -
- Multi hop agentic retrieval
- -
- Embedding provider agnostic
- -
- LLM provider agnostic
- ✓
- Built in rag eval metrics
- -
- Citation attribution
- -
- Multimodal rag
- ✓
- Managed ingestion pipeline
- ✓
- Open source
- -
Platform & deployment
Independently observed- Web
- Cloud / SaaS
Integrations (9)
Independently observed- Google Cloud Storage
- Google Cloud Logging
- Google Cloud Monitoring
- BigQuery
- Cloud Data Fusion
- Cloud Dataflow
- Cloud Composer
- Service Directory
- Gemini•
Google Vertex AI Search FAQ
Common questions about Google Vertex AI Search, answered from independent, dated evidence.
What is Google Vertex AI Search?
A comprehensive development platform that enables teams to rapidly create and scale autonomous agents powered by multiple AI model options, with built-in governance, enterprise data grounding, and integration capabilities. It is indexed under RAG Tools.
Source: https://cloud.google.com/vertex-ai-search-and-conversation
Is Google Vertex AI Search free?
Google Vertex AI Search offers a free tier, so you can start without paying. Pricing changes often, so verify at source before relying on it.
Source: https://cloud.google.com/vertex-ai-search-and-conversation
What platforms does Google Vertex AI Search support?
We have confirmed browser-based access to Google Vertex AI Search. That is the extent of what we could verify from public sources, so it may well offer desktop or mobile clients we have not indexed.
Source: https://cloud.google.com/vertex-ai-search-and-conversation
Can Google Vertex AI Search be self-hosted?
We have only confirmed a cloud / SaaS deployment for Google Vertex AI Search, so it appears to be vendor-hosted. If a self-hosted option exists we have not found it documented publicly.
Source: https://cloud.google.com/vertex-ai-search-and-conversation
What does Google Vertex AI Search integrate with?
We have confirmed 8 integrations for Google Vertex AI Search, including Google Cloud Storage, Google Cloud Logging, Google Cloud Monitoring, BigQuery, Cloud Data Fusion, Cloud Dataflow, Cloud Composer and Service Directory. This is what we could verify from public sources, so the vendor may support others we have not indexed.
Source: https://cloud.google.com/vertex-ai-search-and-conversation
Where is Google Vertex AI Search available?
Google Vertex AI Search is available worldwide. Its primary market is the United States.
Source: https://cloud.google.com/vertex-ai-search-and-conversation
Google Vertex AI Search alternatives
Other rag tools we track, ranked by the same independent score.
- LightRAGA lightweight framework that combines knowledge graphs and vector embeddings for efficient information retrievalmedium · 53%
- RagieA managed AI context platform for agents, assistants, and applicationsmedium · 73%
- Amazon Bedrock Knowledge BasesA managed service connecting AI applications to enterprise data with automatic indexing and retrievalmedium · 74%
- PathwayA streaming data framework for building real-time AI and ML applicationslow · 35%
- Ragaslow · 32%
- RAGFlowAn open-source platform combining retrieval-augmented generation with AI agent capabilities to provide contextual support for language modelslow · 9%
Compare Google Vertex AI Search
Side by side against other rag tools, attribute by attribute, with a source on every value.
The vioscaleAI score: one lens on the evidence
Not user reviews and not a paid placement: a confidence-weighted blend of the independent signals below (adoption, activity, security posture, and more), which you can sort and re-weight yourself. Vendors can correct their listing but can never move their rank, and stars are weighted low as a vanity metric. It is one way to read the evidence for Google Vertex AI Search, not the verdict.
| Signal | Score | Weight | Contribution | Evidence |
|---|---|---|---|---|
| Price level | 80 | 0.04 | 3.3 | ✓ |
| Capabilities | 58 | 0.04 | 2.2 | ✓ |
| Pricing transparency | 25 | 0.05 | 1.4 | ✓ |
| Integrations | 27 | 0.04 | 1.2 | ✓ |
| Reliability | 0 | 0.07 | 0.0 | - |
| Security posture | 0 | 0.12 | 0.0 | - |
Computed . Re-weight it by intent, or see the full method.
All data & sourcesshow ↓
Every value we hold, with its source, retrieval date, and confidence. This is the evidence behind the score: don't trust it, verify it.
Content
| Attribute | Value | Evidence |
|---|---|---|
| Faq | 6 items | mediumsource · 2026-09-10 · 58% |
Features
| Attribute | Value | Evidence |
|---|---|---|
| Capabilities | Multimodal rag, Llm provider agnostic, Managed ingestion pipeline | mediumsource · 2026-09-29 · 60% |
Integrations
| Attribute | Value | Evidence |
|---|---|---|
| Count | 8 | mediumsource · 2026-08-21 · 60% |
Market
| Attribute | Value | Evidence |
|---|---|---|
| Availability | PrimaryMarkets: … · AvailabilityScope: global · AvailableCountries: … · NotAvailableCountries: … | mediumsource · 2026-09-06 · 50% |
Pricing
Is Google Vertex AI Search the right choice for you?
Tell us the job, the constraints and what you weigh most, and we will rank Google Vertex AI Search against the rest of the rag tools we index, using the same dated evidence weighted your way.
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