Overview
Vertex AI is Google Cloud's end-to-end machine learning and generative AI platform, positioned as a one-stop foundation for enterprises to complete the entire process of "training → deployment → operations → governance" for AI models. It serves as the official enterprise entry point for the Gemini model series, complementing the developer-oriented Google AI Studio.
Core capabilities cover three major areas: Enterprise-grade Gemini 3.1 Pro / Flash invocation (including 1M token long context), Model Garden (integrating 200+ open-source and third-party models, including Llama, Mistral, Claude, and Anthropic's Bedrock equivalents), and a complete MLOps toolchain (Pipelines, Feature Store, Model Monitoring, Prediction, Training). In recent years, it has added specialized generative AI capabilities such as Agent Builder, RAG Engine, and Grounding with Google Search, making it the de facto standard platform for enterprise AI applications within the Gemini ecosystem.
🔥 Major Brand Upgrade on 2026/4/22: At the Google Cloud Next 2026 conference (4/22-24), Vertex AI was officially upgraded to the Gemini Enterprise Agent Platform—not just a simple name change, but a strategic leap for Google Cloud AI from "Model as a Service (MaaS)" to "Agent as a Platform (AaaP)." API endpoints remain compatible, but the console entry point has been redirected. Key new capabilities include: Agent Builder (enterprise-grade Agent construction), Gen AI SDK (replacing the old Vertex AI SDK), multi-agent orchestration, enterprise governance, and scaling. It is Google's complete enterprise Agent solution competing with OpenAI Enterprise + Claude Enterprise + Coze.
On the data layer, AlloyDB's ScaNN index now supports scaling beyond 10 billion vectors (preview), enabled by a new four-level tree architecture that reduces query complexity from O(N^1/2) to O(N^1/4). In internal testing, this architecture achieves p95 latency of no more than 51 milliseconds and a recall rate of 95% at the 10 billion vector scale. The feature can be deployed using the quickstart guide, and new users can enjoy a 30-day free trial (2026/8/20).
Key Features
- Enterprise-grade Gemini 3.1 Pro / Flash: 1M context, native multimodal, VPC SC private network, compliance auditing, all included
- Model Garden 200+: Integrates 200+ open-source and third-party models including Llama, Mistral, Claude, Gemma, Imagen, Veo
- Agent Builder: Low-code construction of enterprise-grade AI Agents, supporting tool invocation and multi-step orchestration
- RAG Engine + Vector Search: Managed RAG and vector search, deeply integrated with BigQuery / Cloud Storage; AlloyDB ScaNN index scales beyond 10 billion vectors with a four-level tree architecture (preview), p95 latency no more than 51 ms and 95% recall at that scale
- Grounding with Google Search: Model responses access Google Search in real-time, significantly reducing hallucinations
- Complete MLOps Toolchain: Pipelines / Feature Store / Model Monitoring / Training / Prediction, all in one
- Fine-tuning + Supervised / RLHF: Supports supervised fine-tuning, RLHF, and continued pre-training; weights belong to the customer
Use Cases
- Enterprises heavily using the GCP ecosystem (BigQuery / GKE / Cloud Storage)
- Production scenarios requiring Gemini 3.1 Pro + 1M context + private network
- Large-scale AI projects requiring end-to-end MLOps from data to model
- Enterprises needing multi-model comparison and selection (try all at once with Model Garden)
- Search-enhanced (Grounding) products
- Vector search workloads scaling to 10 billion vectors and beyond
Pros
- Gemini 3.1 Pro is top-tier on benchmarks like MMLU-Pro / GPQA; Vertex is the only official enterprise entry point
- Model Garden provides access to 200+ models at once, offering the highest model selection flexibility across all clouds
- MLOps maturity is comprehensively leading among the three major clouds
- Seamless integration with BigQuery / GCS data lakes
- Grounding with Google Search is a unique capability
- Compliance covers SOC / ISO / HIPAA / FedRAMP
- AlloyDB ScaNN index scales beyond 10 billion vectors with p95 latency no more than 51 ms and 95% recall
Pricing
Billed per model by token / training hour / prediction node. Reference: Gemini 3.1 Pro input ~$1.25/M, output ~$10/M; Gemini 3.1 Flash input $0.075/M, output $0.30/M; Model Garden models follow third-party pricing (Claude / Llama etc. each have their own pricing). Fine-tuning is billed by training token. New users receive $300 in GCP credits. AlloyDB ScaNN vector search offers new users a 30-day free trial.
Summary
Vertex AI is the only official channel for enterprises wanting to use Gemini in production—and the platform with the most complete MLOps capabilities and the most flexible model selection among the three major clouds. If your team is on GCP, use it directly; if on AWS / Azure, Bedrock / Azure OpenAI are more suitable. For lightweight development, start with Google AI Studio; switch to Vertex AI for production.
Version History
- How AlloyDB ScaNN scales vector search to 10 billion vectors (2026-08-20): AlloyDB's ScaNN index now supports scaling beyond 10 billion vectors, enabled by a new four-level tree architecture (preview), which reduces query complexity from O(N^1/2) to O(N^1/4). In internal testing, this architecture achieves p95 latency of no more than 51 milliseconds and a recall rate of 95% at the 10 billion vector scale. This feature can be deployed using the quickstart guide, and new users can enjoy a 30-day free trial.