New VDF AI Enterprise is now available on AWS Marketplace
Enterprise AI Comparison

Vertex AI Agent Builder Alternative
for Sovereign Enterprise Agents

Google has folded Vertex AI Agent Builder into Gemini Enterprise Agent Platform: ADK for code, Agent Runtime for hosting, and new identity, registry and gateway controls, all consumed as Google Cloud services. VDF AI goes the other way and installs a governed agent platform on infrastructure you control, air-gapped sites included. Below, the two are compared on deployment, models, governance, residency, pricing and lock-in, with Google facts checked in September 2026.

Book a Demo Read the Verdict
QUICK VERDICT

The Short Version

Gemini Enterprise Agent Platform suits you if your data already lives in BigQuery and Google Cloud, your developers like a code-first toolkit with a managed runtime behind it, and Google Cloud regions satisfy your residency rules. The breadth of managed services, from Memory Bank to Agent Simulation, is hard to match.

VDF AI suits you if agents must run on your own servers, in a sovereign cloud or with no internet connection at all, if you want one router across several model vendors and local models, or if finance prefers an annual capacity figure to a monthly bill that tracks vCPU-hours and tokens. Our 2026 vendor landscape places both in the wider market.

Google Agent Platform
VDF AI
Best for
Cloud-native agent engineering on Google Cloud
Governed agents inside your own perimeter
Where agents run
Google Cloud regions; GDC for disconnected sites
Your datacenter, private or sovereign cloud, air-gapped, or VDF AI Cloud
Pricing model
Pay-as-you-go compute, memory, storage and tokens
Per user (Cloud) or annual capacity pool (on-prem)
Model catalogue
Model Garden, 200+ models
Multi-vendor routing plus local open-weight models
Managed operations
Google runs runtime, memory and scaling
You or a partner run the containers
Where evidence is stored
Your Google Cloud organisation
Infrastructure you own
Developer toolkit
Open-source ADK in Python and Go
Visual Networks canvas and REST APIs
PRICING & DEPLOYMENT

Agent Platform Pricing and Where Agents Run

Two very different ways of paying for the same agent workload.

Gemini Enterprise Agent Platform

Billing model checked September 2026 on Google’s Agent Platform pricing page

Agent ComputePer vCPU-hourRuntime, sandboxes, code execution and computer use; idle time between turns is not billed
Agent MemoryPer GiB-hourRAM allocated to running agents
Sessions & Memory BankStorage + opsGiB-months stored, plus metered read and write operations
Model tokensPer model SKUGemini, Claude and other Model Garden models billed on their own lines
Commitments1 or 3 yearsFlexible savings plans reduce the unit rates

A monthly free tier applies per account. Agent Gateway calls, and from later in 2026 Semantic Governance Policy checks, are also converted into Agent Compute hours, so spend tracks traffic, tool calls and memory activity together.

VDF AI Pricing

Both mechanics are set out on our pricing page

Managed CloudPer userStarter, Professional and Enterprise Cloud plans
On-premisesAnnual poolCapacity Licensing sized up front, with unlimited users
What countsTokens and inbound transactions; a bundle binds on whichever is reached first
What never countsOrchestration, routing, policy checks, audit logging and retrieval from an index
Billed separatelyExternal model-provider charges and the hardware that serves local models

Google meters agent infrastructure by the hour and the model by the token. VDF AI fixes the platform cost for a year and keeps hardware and any external model spend visible as their own lines.

Where the real trade-off sits

Google operates the runtime, scaling and patching for you, and that has genuine value: long-running agents, advertised sub-second cold starts and managed memory are not things most teams want to build themselves. The price of that convenience is location. Agent Runtime, Sessions and Memory Bank are offered in Google Cloud regions, and a disconnected site needs a separate Google Distributed Cloud agreement on Google-supplied hardware. VDF AI inverts the arrangement: your team or a partner runs the containers, and the platform goes wherever your data already sits. Our on-premises deployment page shows what that install involves.

GOVERNANCE

Identity, Policy & Residency

Both vendors now treat agent identity and tool policy as first-class; the question is where the evidence lives.

Agent identity
GoogleAgent Identity issues every agent a unique cryptographic ID tied to authorization policies
VDF AIAgents act only through per-role tool grants, and every tool call they make is written to the audit trail
Tool access policy
GoogleAgent Gateway (GA June 2026) applies IAM access policies, with a dry-run mode, to agent, tool and MCP traffic
VDF AIThe MCP tool registry makes each grant explicit, assigned per role by an administrator
Prompt and content screening
GoogleModel Armor guardrails on prompts and responses that pass through Agent Gateway
VDF AIPolicy evaluated on every request before a model runs, with allow and deny lists in the router
Audit trail
GoogleAgent Observability traces plus Cloud Trace and Cloud Logging inside your project
VDF AIA Vault record per run, with decision receipts binding prompt, sources, model, tools and outcome
Data residency
GoogleRuntime, Sessions and Memory Bank in listed regions, among them Frankfurt, Belgium, Netherlands, Zurich, Milan and Madrid; CMEK needs a single-region key
VDF AIResidency follows the install: on-prem, sovereign cloud or air-gapped keeps each run inside your boundary
Regulatory evidence
GoogleAn Agent Security dashboard in Security Command Center maps agents, models and vulnerabilities
VDF AIEU AI Act or sector audit packs assembled from the Vault, with controls mapped to DORA, NIS2, GDPR and ISO 42001
BUILDING AGENTS

How Agents Get Built

Google starts from code; VDF AI starts from a governed canvas.

Google’s Toolchain

  • Agent Development Kit — open-source Python and Go toolkit, now with graph-based networks of sub-agents
  • Agent Studio — low-code design surface that can export its logic into ADK
  • Agent Runtime — managed hosting for ADK, LangChain, LangGraph, AG2 or LlamaIndex agents, or any container meeting the runtime contract
  • Memory Bank and Sessions — managed long-term memory and conversation state
  • Agent2Agent support — A2A agents deploy to Agent Runtime and can be registered in Gemini Enterprise
  • Hosting boundary — the runtime services are consumed from Google Cloud regions
  • Cost surface — compute, memory, storage operations and tokens are each metered

VDF AI’s Workspace

  • VDF AI Networks — a visual canvas with Human Approval, MCP Action and Router nodes
  • VDF AI Agents — a five-step builder that runs from basics and model choice through tools, prompt and review
  • Agent Skills — reusable procedures that nodes inherit without widening tool permissions
  • SEEMR router — learns which model to call from quality, latency, failure and energy signals (how it works)
  • Connectors — Slack, Jira, GitHub, GitBook, Confluence, Notion, Microsoft 365 and Google Drive
  • Scale is yours to size — you plan the cluster, or choose VDF AI Cloud for a hosted option
  • Container delivery — Docker Compose for pilots, Kubernetes for production, and an offline install path

Nothing forces a single choice: Gemini can stay one of the routed providers while regulated work moves inside your walls.

ARCHITECTURE

Platform Architecture

What each stack is made of, layer by layer.

Google Agent Platform

Managed agent platform on Google Cloud

  • Model Garden — Gemini, Gemma, Claude and more than 200 models
  • ADK + Agent Studio — code-first and low-code building
  • Agent Runtime — hosting, sandboxes and code execution
  • Memory Bank + Sessions — persistent context and history
  • Agent Identity, Registry, Gateway — the governance plane
  • Gemini Enterprise app — employee front end, formerly Google Agentspace

Each layer is a managed service. That removes operations work and ties the design to Google Cloud identity, logging and networking.

VDF AI

Self-deployable governed agent platform

  • VDF AI Networks — orchestration canvas with approval nodes
  • VDF AI Agents — builder, Agent Skills and MCP tool registry
  • VDF AI Router — SEEMR routing, budgets, failover and air-gap mode
  • MCP gateway — a tool server inside your perimeter
  • Vault — decision receipts and evidence packs
  • VDF AI Chat — the front end your employees use

Every layer ships as containers you run, so one design fits a rack, a sovereign cloud or a disconnected enclave. The platform overview covers each layer.

DEPLOYMENT

Deployment & Data Location

Where the runtime, the memory and the audit trail physically sit. The same questions for AWS are answered in our AgentCore comparison.

DimensionGoogleVDF AI
Public cloudGoogle Cloud regions in the Americas, Europe, Asia-Pacific and Middle EastVDF AI Cloud, or VDF AI installed in a cloud account you own
Your own datacenterGDC connected on your hardware, with Gemini Flash in previewOn your servers via Docker Compose or Kubernetes
Fully disconnectedGDC air-gapped on Google-supplied hardware, run by Google, a partner or bothAir-gapped, with images mirrored to your internal registry
EU residencyEU regions for Runtime, Sessions and Memory Bank; eu multi-region endpointsSet by where you install it, including EU-only sites
Encryption keysCMEK with single-region Cloud KMS keys; not on global endpointsSecrets held in your own secrets manager
Day-to-day operationsGoogle runs the managed runtimeYour team or a partner, backed by VDF AI support
UpgradesRolled out by Google as the service evolvesContainer releases pulled and rolled out on your schedule

GDC details from Google’s GDC air-gapped page and its Next 2026 announcement; region list from the agent locations table, verified September 2026.

FAIR PLAY

When to Choose Google

In several common situations Google’s agent platform is the stronger pick.

Google is the right call when…

  • Your analytics estate already lives in BigQuery, and agents should act on it through batch and event-driven runs on Pub/Sub.
  • Developers prefer to write agents in code and want the open-source ADK with managed deployment close at hand.
  • You would rather Google ran memory, sessions, sandboxes and scaling than size a cluster yourself.
  • Google Cloud regions, CMEK and VPC Service Controls already meet your residency and exfiltration requirements.
  • Employees will reach agents through the Gemini Enterprise app next to Google Workspace or Microsoft 365 content.
  • A classified or disconnected programme is ready to procure GDC air-gapped hardware and operations.
Where Google is genuinely strong
Model Garden breadth

More than 200 models, from Gemini to Claude to Gemma, available on one platform and billed through your Google Cloud account, with evaluation and tuning alongside.

Managed runtime at scale

Agents that keep state for days, per-second billing and no charge for idle time between turns, all without a cluster to patch.

Open developer tooling

ADK is Apache-2.0 on GitHub, and the A2A protocol lets agents built elsewhere join the same registry.

A supported disconnected option

Gemini on GDC air-gapped gives sovereign buyers a Google-backed model endpoint that needs no link to Google Cloud or the public internet.

DECISION SIGNALS

When VDF AI Is the Better Fit

Signals that an agent programme needs to leave the hyperscaler boundary.

Data cannot enter a public cloud

Legal or a regulator has ruled that prompts, retrieved documents and agent memory must stay on infrastructure you own. VDF AI puts the full stack there, audit Vault included.

Air-gapped on hardware you already own

GDC air-gapped runs on purpose-built hardware that Google supplies. When the disconnected site already has servers and GPUs, VDF AI mirrors its images into your internal registry and runs offline on them.

Several model vendors, one policy

Risk wants Claude for some tasks, a local Llama or Mistral model for sensitive ones and Gemini for the rest. The VDF AI Router applies one allow list, one budget and one audit trail across all of them.

Finance wants a fixed annual number

Agent spend on a metered cloud moves with vCPU-hours, memory, storage operations and tokens. A capacity pool agreed up front, with unlimited users, turns that into one budget line.

A supervisor will ask for proof

Decision receipts that bind prompt, sources, model, tools and outcome, kept in a Vault you host, answer an examiner without exporting anything from a vendor’s logging service.

Analysts design the workflows

Networks gives business analysts a canvas whose Human Approval and MCP Action nodes security has already reviewed, while engineers keep full API access to the same flows.

COEXISTENCE

Running Both During a Transition

Most enterprises will keep some agents on Google. This sequence limits rework.

1
Classify by data boundary

Sort today’s agents by data sensitivity and residency obligation. Those touching public or low-risk data can stay on Agent Runtime; anything headed for a restricted enclave becomes a VDF AI candidate.

2
Put one router in front of the models

Register Gemini as an approved external provider in the VDF AI Router next to your local models. From then on, policy decides per request which data may reach Google and which stays inside.

3
Rebuild the regulated workflows

Move the workflows with the strictest oversight needs into VDF AI Networks, adding Human Approval nodes wherever a person must sign off and an MCP Action node for each governed tool.

4
Consolidate the evidence

Point audit reviews at the VDF AI Vault for everything inside your perimeter, and keep Google’s Agent Observability for agents that remain on Agent Platform until they are retired or re-homed.

Plan the Split With Us
FULL COMPARISON

Side-by-Side Comparison

Google capabilities checked in September 2026 against cloud.google.com and docs.cloud.google.com.

CapabilityVDF AIGoogle Agent Platform
Product scopeGoverned agent platform: build, orchestrate, route, auditAgent building and runtime, model hosting, training and MLOps
Current namingVDF AI Agents, Networks, Router and ChatGemini Enterprise Agent Platform, formerly Vertex AI; Agent Engine is now Agent Runtime
PricingPer user (Cloud) or annual capacity with unlimited users (on-prem)Pay-as-you-go compute, memory, storage and tokens; savings plans available
DeploymentManaged cloud, private cloud, on-prem, sovereign, air-gappedGoogle Cloud regions; GDC connected and GDC air-gapped
HardwareYour servers, or any Linux VM in any cloudGoogle Cloud, or Google-supplied racks for GDC air-gapped
Model choiceOpenAI, Anthropic, Gemini, Mistral, Llama and self-hosted modelsModel Garden with 200+ first-party, partner and open models
Model routingSEEMR per request on quality, cost, latency, energy and policyModel Garden endpoints; a GDC AI gateway routing on cost, latency and accuracy was announced in April 2026
Code-first SDKREST APIs, plus a Python SDK for the routerOpen-source ADK for Python and Go
Long-term memoryLiving knowledge vault fed by execution historyMemory Bank with memory profiles
Identity and tool policyPer-role MCP tool grants, human approval gates, an audit trail of tool callsAgent Identity, Agent Registry and Agent Gateway with IAM policies
Audit evidenceVault decision receipts and an EU AI Act evidence packAgent Observability traces, Cloud Logging, Security Command Center
ProtocolsMCP tool registry and MCP gatewayMCP tools and servers; A2A agents deploy and register
Employee front endVDF AI ChatGemini Enterprise app, formerly Google Agentspace

Sources, verified September 2026: Agent Platform launch post · name-change table · Agent Runtime docs · release notes · Gemini on GDC · Agentspace rename note. Unit rates change often, so check Google’s price list before you budget.

FAQ

Frequently Asked Questions

Questions architects raise when weighing a Vertex AI Agent Builder alternative.

Not under that name. In April 2026 Google launched Gemini Enterprise Agent Platform as the evolution of Vertex AI, and the old Agent Builder product URL now redirects to it. The building blocks carried over with new labels: Vertex AI Agent Engine is now Agent Runtime, Memory Bank and Sessions keep their jobs, and Model Garden sits under the new platform name. Google says future Vertex AI roadmap work ships only through Agent Platform. Verified September 2026 against Google’s published name-change table.

Pay as you go. Agent Runtime and its sandboxes are metered in vCPU-hours and GiB-hours of memory, while Sessions, Memory Bank and the skill registry add storage plus read and write operations. Model tokens are billed separately under each model’s own SKU, and one-year or three-year savings plans lower the unit rates. VDF AI uses per-user plans on its managed Cloud and an annual Capacity Licensing pool on-premises, where users are unlimited and orchestration, routing, governance and audit never draw on capacity. With VDF AI, external model charges and the hardware that serves local models remain separate lines.

Partly, and it depends on the layer. Google Distributed Cloud air-gapped is a fully disconnected offering on Google-supplied hardware, and Gemini models have been generally available on it since August 2025. At Next 2026 Google also announced a sovereign agentic architecture for GDC built with third-party providers on Kubernetes. The managed agent services on Google Cloud (Agent Runtime, Memory Bank, Agent Gateway) are listed by cloud region, so confirm with Google which of them your GDC release includes. VDF AI installs its whole agent stack on hardware you already operate.

Google’s Model Garden lists more than 200 first-party, partner and open models, including Gemini, Gemma and Anthropic’s Claude family, consumed inside Google Cloud. VDF AI routes across OpenAI, Anthropic, Gemini, Mistral, Llama and models you host yourself, scoring each call on quality, cost, latency and policy through the SEEMR router. In air-gap mode the router is restricted to local models, so no request is sent to an external endpoint.

It covers the same goal by a different route. ADK is an open-source, code-first toolkit (Apache-2.0, Python and Go), and Agent Runtime is a managed Google Cloud service for hosting what you build with it. VDF AI is a complete platform you deploy yourself, with a visual Networks canvas, an agent workspace, an MCP tool registry, approval gates and an audit Vault. A team with a large ADK codebase can keep it on Google and place new regulated workloads on VDF AI.

Google has invested heavily here. Agent Identity gives each agent a cryptographic ID, Agent Registry indexes approved agents and tools, and Agent Gateway (generally available since June 2026) enforces IAM access policies and Model Armor screening on agent traffic. Those controls live in your Google Cloud organisation. VDF AI keeps the equivalent evidence in your own environment: a registry with an owner and risk class per agent, per-role tool grants, human approval gates with named reviewers, and a Vault of decision receipts that feeds an EU AI Act evidence pack.

Yes. Gemini is one of the providers VDF AI can route to, alongside OpenAI, Anthropic, Mistral, Llama and self-hosted open-weight models. Router policy decides which workloads may leave your network at all: allow and deny lists, budgets and rate limits apply before a call is made, and each decision is logged with its reason. A team can therefore keep Gemini for approved, low-sensitivity work while regulated data stays on local models.

Agent code written with ADK or LangGraph can be containerised and moved, but Memory Bank, Sessions, Agent Identity, IAM policies and Cloud Logging traces are Google Cloud services, and rebuilding them elsewhere is the real switching cost. VDF AI runs as containers on Docker Compose or any conformant Kubernetes, on premises or in any cloud, and its router abstracts the model vendor. The dependency moves from a hyperscaler to a software vendor, which is worth weighing honestly.
Explore further

Related resources

If the decision is between a hyperscaler's managed agent services and a platform you run yourself, these pages go deeper on deployment, routing and governance.

Planning Agents Beyond Google Cloud?

Bring one workload that is not allowed to leave your perimeter. We will map it onto VDF AI Networks, the router and the Vault, and show which parts can keep calling Gemini under policy.

Compare Deployment Options Book an Architecture Review