An integrated environment for building generative AI applications on Google Cloud. Its defining trait is that you can write a prompt in the browser, test it immediately, and then continue on the same rails through model tuning, evaluation, and production deployment ─ all while working with Google’s multimodal Gemini models and many others from the same screen. It was originally offered as “Vertex AI Studio”; since Google Cloud Next 2026 the platform as a whole has been renamed “Gemini Enterprise Agent Platform”, with Studio positioned inside it as the console for prompt experimentation and development. Being able to move from personal experimentation up to enterprise system integration on one foundation is the biggest difference from other AI tools.
Key Features
- A Studio where prompts can be tried instantly: A multimodal prompt screen that accepts text, images, audio, and video as input, letting you compare outputs while adjusting parameters such as temperature and token limits. Finished prompts can be exported as Python or curl code, so they port directly into an application
- Model Garden for choosing models in one place: Beyond the Gemini family, you can pick Google’s various foundation models as well as third-party and open-source models from a catalog and try them through the same interface. Swapping models to match use case and cost is straightforward
- Built-in tuning and evaluation: The platform itself provides tuning with your own data and evaluation features that compare output quality by metric. You can select a model based on numbers rather than a vague impression
- A full set for building agents: The low-code Agent Studio, the Agent Development Kit (ADK) for building in code, and Agent Engine for running in production all sit on the same platform, so a prototype agent can be carried through to operation
- Built on the Google Cloud foundation: IAM-based permission management, audit logs, region selection, and integration with other Google Cloud services such as BigQuery are assumed from the start, making it easier to fit into corporate security requirements and data platforms
- Express mode for trying it without billing: There is an entry path that lets you try Studio and agent building without enabling a billing account, which keeps the first step low
Pricing
| Category | Price | Details |
|---|---|---|
| Free trial | $0 (a $300 credit for new accounts, valid 90 days) | Credit usable across Google Cloud, including Agent Platform features |
| Express mode | $0 | An entry tier for trying Studio and agent building without enabling a billing account. Usage limits apply |
| Pay-as-you-go | Pay for what you use | No fixed monthly subscription; charges are based on per-model token rates, runtime, query counts, and similar |
| Enterprise agreement | Contact sales | Commitment discounts and support contracts for large-scale usage |
Token rates vary widely by model ─ from a few tens of cents per million tokens for lightweight models to more than ten times that for high-performance ones. If you run an agent continuously, runtime charges (vCPU-hours, memory-hours) and search query charges stack on top of token charges. The reliable approach is to fix your target model and expected request volume first, then check the official pricing page for an estimate.
Pricing is as of August 2026. Please check the official site for the latest pricing.
Pros & Cons
✅ Pros
- Prompt experimentation, tuning, evaluation, and production deployment can all proceed without switching environments
- Many models including Gemini can be compared on the same screen, making it easy to re-select based on use case and cost
- Controls required for enterprise use ─ IAM, audit logs, region selection ─ are in place from the outset
- Easy to connect with existing Google Cloud assets such as BigQuery and Cloud Storage
- Pay-as-you-go billing lets you start small and expand only as needed
⚠️ Cons
- Requires an understanding of Google Cloud projects, billing, and permissions, so the initial learning cost is higher than a chat tool
- Pay-as-you-go makes the ceiling hard to see, and unexpected usage can inflate costs. Setting budget alerts is effectively mandatory
- Features and service names are being refreshed quickly, so documentation and older articles may not match the current UI naming
- The setup is easily overkill for an individual who only wants text generation
- Some models and features are available only in limited regions
Comparison with Similar Services
| Item | Agent Platform (Vertex AI Studio) | Google AI Studio | Amazon Bedrock | Azure AI Foundry |
|---|---|---|---|---|
| Provider | Amazon Web Services | Microsoft | ||
| Main intended use | Integration into enterprise systems, agent operations | Fast prototyping for individuals and small teams | Managed use of foundation models | AI app development on Azure |
| Available models | Gemini plus many foundation and open-source models | Mainly Gemini | Multiple vendors such as Anthropic and Meta | OpenAI family plus multiple vendors |
| Tuning and evaluation | Supported within the platform | Limited | Supported | Supported |
| Billing | Pay-as-you-go (integrated with cloud billing) | Free tier plus pay-as-you-go API | Pay-as-you-go | Pay-as-you-go |
Even within Google, the split is clear: Google AI Studio for quick experiments, Agent Platform when operations and governance must be part of a business system.
Who Is It For
- Developers who want to take generative AI beyond a prototype and into internal systems or products
- Teams that want to compare several models and choose based on the balance of quality and cost
- Corporate IT departments that must satisfy governance requirements such as permission management, audit logs, and data residency
- Organizations that already have a data foundation on Google Cloud, such as BigQuery or Cloud Storage
- Engineers who want to assemble AI agents in both low-code and code forms
Summary
Agent Platform (Vertex AI Studio) is a generative AI development environment where many models, Gemini included, can be handled on one foundation from experimentation through production. The barrier to entry is higher than with chat-style AI tools, but its greatest value is that it can carry a project into a form that is actually operable in business, permission management, evaluation, and tuning included. Start by touching the Studio prompt screen with the free credit or Express mode, and once your target model and expected request volume are settled, move on to cost planning with the official pricing page in hand. Note also that the platform was renamed “Gemini Enterprise Agent Platform” during 2026, so pay attention to the publication date when researching information about it.