G

Google Gemini Enterprise Agent Platform

by Google Cloud

AI Agents & OrchestrationInfrastructure & CloudAI Models & APIsGovernance & Security

Google Cloud's full-stack platform for building, running, governing and optimising AI agents — the platform formerly called Vertex AI

Usage-based · Contact for pricing·Added May 6, 2026·Updated Aug 12, 2026
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THE DAILY BRIEF
Google Gemini Enterprise Agent Platform

by Google Cloud

AI Agents & OrchestrationInfrastructure & CloudAI Models & APIsGovernance & Security

Google Cloud's full-stack platform for building, running, governing and optimising AI agents — the platform formerly called Vertex AI

Usage-based · Contact for pricing

The Gemini Enterprise Agent Platform is Google Cloud's rebranded and expanded Vertex AI, announced in April 2026. It consolidates model selection, model building and agent building into one stack spanning build, scale, govern and optimise, with Agent Studio, the Agent Development Kit, Agent Runtime, Memory Bank, an Agent Gateway and 200-plus models for enterprises putting production agents in front of real users.

At a Glance

Category
AI Agents & Orchestration
Pricing
Usage-based, Contact for pricing
Target Market
CTOs, CIOs, Platform Engineering Leaders, Enterprise Developers, Data Scientists, Security Architects
Deployment
Cloud-only, API-based
Founded
2008
Headquarters
Mountain View, United States
Team Size
500+

Key Features

  • Agent Development Kit (ADK)
  • Agent Studio
  • Agent Runtime
  • Memory Bank and Agent Sessions
  • Agent Identity, Registry and Gateway
  • Agent threat and anomaly detection
  • Simulation, evaluation and optimiser
  • Model Garden

Capabilities

text generation
image generation
video generation
code generation
workflow automation
api access
audio generation
fine tuning
agent orchestration

Use Cases

  • Customer service agent modernisation
  • Regulated finance workflow automation
  • Agent-based commerce
  • Enterprise knowledge agents
  • Clinical eligibility triage
  • Governed agent rollout at scale

Ideal For

Best For

  • Enterprises already standardised on Google Cloud that need production agents governed with identity, policy and audit trails
  • Long-running agent workloads that must hold state for days, backed by Memory Bank for persistent context
  • Security-sensitive deployments needing prompt-injection and data-leakage controls enforced centrally through Agent Gateway
  • Multi-agent architectures where sub-agents coordinate, exposed through both a code-first SDK and a low-code visual builder
  • Teams that want model choice — 200-plus models including Gemini, Gemma and Anthropic Claude — behind one billing and governance surface

Not Ideal For

  • Organisations that need on-premises or air-gapped deployment, since this is a Google Cloud managed platform with no self-hosted edition
  • Small teams wanting a fixed monthly price — billing is consumption-based across compute, memory, storage and model tokens, which is hard to forecast before a workload exists
  • Multi-cloud shops seeking a portable control plane, as the governance, identity and observability layers are tied to Google Cloud
  • Buyers who want a settled product surface: the April 2026 rebrand from Vertex AI is still propagating through documentation, tooling and third-party integrations

Market Analysis

Enterprise-gradeHyperscaler platformGovernance-firstFull-stack agent platform

Pros

  • The most complete governance layer of any major agent platform — per-agent cryptographic identity, central registry, policy gateway, threat detection and audit trails
  • Genuine model choice through Model Garden's 200-plus models, including Anthropic Claude alongside Gemini and Gemma
  • Agent Runtime handles the hard operational cases: sub-second cold starts, agents that stay alive for days, and Memory Bank for persistent context
  • Backward compatible — SDKs, billing and APIs migrated without breaking existing Vertex AI deployments
  • Named production references across regulated and high-volume sectors including PayPal, Comcast, L'Oréal, Payhawk and Color Health

Cons

  • Google's naming is actively confusing: Gemini Enterprise (seat-priced), the Gemini Enterprise Agent Platform (consumption-priced, formerly Vertex AI) and the Gemini API are three different things frequently conflated in budgeting
  • The rebrand created real churn in the ecosystem — a public models.dev issue documents that renaming the google-vertex provider key would break every downstream project keyed on it, including LangChain integrations, leaving developers choosing between current branding and compatibility
  • Consumption billing across compute, memory, storage and per-model tokens makes cost forecasting hard before a workload actually exists
  • No on-premises or air-gapped option, and the governance and observability layers do not travel to other clouds
  • The stack is broad — twenty-plus named components across build, scale, govern and optimise — which is a real learning and platform-ownership cost for smaller teams
  • Documentation and product URLs are still migrating between cloud.google.com and docs.cloud.google.com, so links and guides go stale quickly

Pricing

Monthly free tier

$0

  • First 50 vCPU-hours of agent compute per month
  • First 100 GiB-hours of agent memory
  • First 1 GiB-month of agent storage

Consumption (pay-as-you-go)

From $0.085 per vCPU-hour of agent compute

  • Agent compute metered per vCPU-hour
  • Stored session events and memories at $0.25 per 1,000
  • Model tokens billed separately by model tier
  • New Google Cloud accounts typically receive a $300 / 90-day trial credit

This platform is billed by consumption, not by seat, and that distinction is the single most common source of budgeting confusion because Google ships a separate seat-priced product with a near-identical name. Agent compute — the vCPU time behind Memory Bank, Sessions and the Skill Registry — runs about $0.085 per vCPU-hour, stored session events and memories cost $0.25 per 1,000, and model tokens are billed on top at wildly different rates depending on tier, from roughly $0.10-$0.50 per million input tokens on flash-class models to $1.25-$12.50 per million on Pro-class, with output tokens several times the input rate. Every consumption resource carries a monthly free allowance — the first 50 vCPU-hours of compute, 100 GiB-hours of memory and 1 GiB-month of storage are free — and new Google Cloud accounts generally get a $300 / 90-day trial credit that applies here. Gemini Enterprise, the seat-priced agent and enterprise-search product, is a different SKU starting around $21 per seat per month for Business and $30-plus for Standard and Plus; do not budget the two as one line item.

Security & Compliance

soc2
gdpr
hipaa
iso27001
sso
data residency

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The Gemini Enterprise Agent Platform is Google Cloud's rebranded and expanded Vertex AI, announced in April 2026. It consolidates model selection, model building and agent building into one stack spanning build, scale, govern and optimise, with Agent Studio, the Agent Development Kit, Agent Runtime, Memory Bank, an Agent Gateway and 200-plus models for enterprises putting production agents in front of real users.

The Gemini Enterprise Agent Platform is Google Cloud's enterprise agent stack, announced on 22 April 2026 at Cloud Next as the rebranding and expansion of Vertex AI. It is organised into four layers. Build offers Agent Studio, a low-code visual builder, alongside the Agent Development Kit, an open-source code-first framework supporting graph-based sub-agent networks, multimodal streaming and sandboxed bash execution; Agent Garden supplies pre-built templates for code modernisation, financial analysis and invoice processing, with batch and event-driven agents running through BigQuery and Pub/Sub. Scale provides Agent Runtime with sub-second cold starts and support for long-running agents that hold state for days, Agent Sandbox for safely executing model-generated code, Memory Bank for persistent curated long-term context, Agent Sessions, and bidirectional WebSocket streaming. Govern is the layer that most distinguishes it: Agent Identity issues a unique cryptographic ID to every agent with auditable action trails, Agent Registry indexes approved agents, tools and skills, and Agent Gateway enforces consistent policy with Model Armor protections against prompt injection and data leakage, backed by agent anomaly and threat detection and a security dashboard powered by Security Command Center. Optimise closes the loop with agent simulation, continuous evaluation against live traffic, observability tracing and an optimiser that clusters real-world failures into instruction fixes. Model Garden exposes more than 200 models including Gemini 3.1 Pro, Gemini 3.1 Flash Image, Lyria 3, Gemma 4 and Anthropic's Claude family. Google migrated all SDKs, billing and APIs without breaking changes, and names Comcast, PayPal, L'Oréal, Payhawk, Color Health, Geotab, Burns & McDonnell and Gurunavi as production customers.

Ideal Buyer

A platform or AI engineering leader at a Google Cloud enterprise who has to move agents from prototype into production with identity, policy enforcement, evaluation and audit built into the runtime rather than bolted on.

Key Benefit

One governed stack from agent authoring through deployment, observability and threat detection, with 200-plus models available and existing Vertex AI workloads running unchanged.

At a Glance

Category
AI Agents & Orchestration
Pricing
Usage-based, Contact for pricing
Target Market
CTOs, CIOs, Platform Engineering Leaders, Enterprise Developers, Data Scientists, Security Architects
Deployment
Cloud-only, API-based
Founded
2008
Headquarters
Mountain View, United States
Team Size
500+

Key Features

  • Agent Development Kit (ADK)

    Open-source code-first framework for graph-based sub-agent networks, multimodal streaming and sandboxed bash execution in agent workspaces

  • Agent Studio

    Low-code visual builder that lets non-specialists assemble agents and hand off cleanly to code-first development later

  • Agent Runtime

    Managed execution with sub-second cold starts, seconds-to-provision agents, and support for multi-day long-running workflows

  • Memory Bank and Agent Sessions

    Curates persistent long-term memory from conversations and maps session history to internal databases or CRM records

  • Agent Identity, Registry and Gateway

    Cryptographic IDs per agent, a central registry of approved assets, and policy enforcement with Model Armor injection defences

  • Agent threat and anomaly detection

    Statistical models plus LLM-as-judge flag unusual reasoning, with real-time malicious-activity detection via Security Command Center

  • Simulation, evaluation and optimiser

    Tests agents against synthetic interactions, scores live traffic continuously, and clusters real failures into instruction improvements

  • Model Garden

    Access to 200-plus models including Gemini 3.1 Pro, Gemini 3.1 Flash Image, Lyria 3, Gemma 4 and Anthropic Claude

Capabilities

text generation
image generation
video generation
code generation
workflow automation
api access
audio generation
fine tuning
agent orchestration

Use Cases

  • Customer service agent modernisation

    Comcast rebuilt Xfinity Assistant on ADK with a multi-agent troubleshooting architecture running on Agent Runtime

  • Regulated finance workflow automation

    Payhawk's Financial Controller Agent uses Memory Bank and cut expense submission time by more than half

  • Agent-based commerce

    PayPal builds multi-agent workflows on ADK and the Agent Payment Protocol for secure agent-initiated transactions

  • Enterprise knowledge agents

    Burns & McDonnell combines deterministic rules with probabilistic reasoning in an internal knowledge agent built on ADK

  • Clinical eligibility triage

    Color Health's Virtual Cancer Clinic assistant screens breast cancer eligibility using ADK and Agent Runtime

  • Governed agent rollout at scale

    Platform teams register every internal agent and enforce one security policy across environments through Agent Gateway

Ideal For

Best For

  • Enterprises already standardised on Google Cloud that need production agents governed with identity, policy and audit trails
  • Long-running agent workloads that must hold state for days, backed by Memory Bank for persistent context
  • Security-sensitive deployments needing prompt-injection and data-leakage controls enforced centrally through Agent Gateway
  • Multi-agent architectures where sub-agents coordinate, exposed through both a code-first SDK and a low-code visual builder
  • Teams that want model choice — 200-plus models including Gemini, Gemma and Anthropic Claude — behind one billing and governance surface

Not Ideal For

  • Organisations that need on-premises or air-gapped deployment, since this is a Google Cloud managed platform with no self-hosted edition
  • Small teams wanting a fixed monthly price — billing is consumption-based across compute, memory, storage and model tokens, which is hard to forecast before a workload exists
  • Multi-cloud shops seeking a portable control plane, as the governance, identity and observability layers are tied to Google Cloud
  • Buyers who want a settled product surface: the April 2026 rebrand from Vertex AI is still propagating through documentation, tooling and third-party integrations

Integrations

SDK Available
SDK:Python

Deployment

On-Premise

Market Analysis

Enterprise-gradeHyperscaler platformGovernance-firstFull-stack agent platform

Pros

  • The most complete governance layer of any major agent platform — per-agent cryptographic identity, central registry, policy gateway, threat detection and audit trails
  • Genuine model choice through Model Garden's 200-plus models, including Anthropic Claude alongside Gemini and Gemma
  • Agent Runtime handles the hard operational cases: sub-second cold starts, agents that stay alive for days, and Memory Bank for persistent context
  • Backward compatible — SDKs, billing and APIs migrated without breaking existing Vertex AI deployments
  • Named production references across regulated and high-volume sectors including PayPal, Comcast, L'Oréal, Payhawk and Color Health

Cons

  • Google's naming is actively confusing: Gemini Enterprise (seat-priced), the Gemini Enterprise Agent Platform (consumption-priced, formerly Vertex AI) and the Gemini API are three different things frequently conflated in budgeting
  • The rebrand created real churn in the ecosystem — a public models.dev issue documents that renaming the google-vertex provider key would break every downstream project keyed on it, including LangChain integrations, leaving developers choosing between current branding and compatibility
  • Consumption billing across compute, memory, storage and per-model tokens makes cost forecasting hard before a workload actually exists
  • No on-premises or air-gapped option, and the governance and observability layers do not travel to other clouds
  • The stack is broad — twenty-plus named components across build, scale, govern and optimise — which is a real learning and platform-ownership cost for smaller teams
  • Documentation and product URLs are still migrating between cloud.google.com and docs.cloud.google.com, so links and guides go stale quickly

Pricing

Free Trial Available

Monthly free tier

$0

  • First 50 vCPU-hours of agent compute per month
  • First 100 GiB-hours of agent memory
  • First 1 GiB-month of agent storage

Consumption (pay-as-you-go)

From $0.085 per vCPU-hour of agent compute

  • Agent compute metered per vCPU-hour
  • Stored session events and memories at $0.25 per 1,000
  • Model tokens billed separately by model tier
  • New Google Cloud accounts typically receive a $300 / 90-day trial credit

This platform is billed by consumption, not by seat, and that distinction is the single most common source of budgeting confusion because Google ships a separate seat-priced product with a near-identical name. Agent compute — the vCPU time behind Memory Bank, Sessions and the Skill Registry — runs about $0.085 per vCPU-hour, stored session events and memories cost $0.25 per 1,000, and model tokens are billed on top at wildly different rates depending on tier, from roughly $0.10-$0.50 per million input tokens on flash-class models to $1.25-$12.50 per million on Pro-class, with output tokens several times the input rate. Every consumption resource carries a monthly free allowance — the first 50 vCPU-hours of compute, 100 GiB-hours of memory and 1 GiB-month of storage are free — and new Google Cloud accounts generally get a $300 / 90-day trial credit that applies here. Gemini Enterprise, the seat-priced agent and enterprise-search product, is a different SKU starting around $21 per seat per month for Business and $30-plus for Standard and Plus; do not budget the two as one line item.

Security & Compliance

soc2
gdpr
hipaa
iso27001
sso
data residency

Connect

Sources

This page was written from 6 sources, 3 on domains other than cloud.google.com.

  1. 1.cloud.google.comintroducing gemini enterprise agent platformvendor
  2. 2.cloud.google.comgemini enterprise agent platformvendor
  3. 3.github.com1857
  4. 4.medium.comvertex ai is dead long live gemini enterprise agent platform
  5. 5.hpcwire.comgoogle unveils gemini enterprise agent platform
  6. 6.cloud.google.comofferingsvendor
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