Buying snapshot
Scroll sideways to see all columns. With a keyboard, focus this table and use the arrow keys.
| Decision point | What Google states |
|---|---|
Product names | Google's product page calls it Gemini platform. The Python SDK still installs as google-cloud-aiplatform. |
Getting started | Select or create a Google Cloud project, enable billing, enable the Gemini Enterprise Agent Platform API and set up authentication. The Python SDK supports Python 3.10 or later. |
Prompt design | Agent Studio lets you design, test and manage prompts for Gemini models using natural language, code, images or video. You can also test Gemini models with an API key. |
Encryption keys | A customer-managed encryption key set when you initialize the SDK is applied to all platform resources. |
Our take
Choose Gemini Enterprise Agent Platform when your data already lives in Google Cloud, especially BigQuery, and you want agents, model choice and machine learning operations on one platform. Google says costs vary heavily by model and scale, so use the new-customer credits for a proof of concept and measure real token and agent usage before you sign a Provisioned Throughput commitment.
Evaluate Gemini Enterprise Agent Platform
Comparisons
Where it stands out
Wide model choice in one catalog. Model Garden offers more than 200 Google and third-party models and tools, including Gemini 4 Argon, Anthropic's Claude Model Family and open models such as Gemma. Tuning options and a Model Evaluation service help you compare and adapt them.
Agents from code to production. Build agents with the Agent Development Kit (ADK), deploy them to Agent Engine from the Python SDK, then register, manage and govern them. Google Antigravity adds a desktop app and CLI for steering several agents that run whole workflows at the same time.
Machine learning next to BigQuery. Notebooks in Colab Enterprise or Workbench are natively integrated with BigQuery. Custom training takes your own framework, code and hyperparameter tuning, and MLOps tools cover pipelines, a model registry, a feature store and monitoring for input skew and drift.
Product screenshot
Trade-offs to weigh
The SDK is changing under you. The generative AI modules vertexai.generative_models, language_models, vision_models, tuning and caching were deprecated as of June 24, 2025, with removal set for June 24, 2026; Google points to the Google Gen AI SDK instead. The agent_engines module will be renamed runtimes in a major release, not before July 31, 2026, and Google advises pinning the SDK below 2.0.0 to avoid unexpected updates.
Grounded answers come with Search Suggestions. If you ground responses with Google Search but do not want to display Search Suggestions in your own application, that choice is subject to alternate pricing, and you must ask your Google Cloud account team for a waiver.
Pricing and plan conditions
Billing is usage-based: you pay for the tools, storage, compute and Cloud resources you use. Model use is charged by tokens under standard pay-as-you-go, discounted Flex PayGo for latency-tolerant jobs, premium Priority PayGo for higher reliability, or Provisioned Throughput subscriptions for guaranteed throughput. Other agent infrastructure is metered as Agent Compute (vCPU), Agent Memory (RAM) and Agent Storage (GiB). Prices are in US dollars; if you pay in another currency, the prices on Cloud Platform SKUs apply.
Scroll sideways to see all columns. With a keyboard, focus this table and use the arrow keys.
| Item | Published rate or basis | Conditions |
|---|---|---|
Text, chat and code generation | Starting at $0.0001 per 1,000 characters | Charged on every 1,000 characters of input (prompt) and of output (response). |
Imagen image generation | Starting at $0.0001 | Based on image input, character input or custom training pricing. |
Open and partner models (examples) | Per 1M tokens: gpt-oss-120b $0.09 input, $0.36 output; Llama 4 Maverick $0.35 input, $1.15 output; Mistral Medium 3 $0.40 input, $2.00 output. | Batch rates per 1M tokens: gpt-oss-120b $0.045 input, $0.18 output; Llama 4 Maverick $0.175 input, $0.575 output. |
Grounding with Google Search | Gemini 2.0 Flash includes up to 1,500 grounded requests per day at no additional charge; beyond that, $35 per 1,000 requests, up to 1 million requests per day. | Billed only for requests whose results contain at least one grounding support URL from the web; standard Gemini model usage fees also apply. |
Web Grounding for enterprise | $45 per 1,000 requests, up to 1 million requests per day, starting May 5, 2025. | For both grounding options, contact your account team above 1 million requests per day. Both rates apply to modality-based pricing. |
Custom model training | Contact sales | Based on machine type per hour, region and any accelerators; get an estimate from sales or the pricing calculator. |
Notebooks | Compute Engine and Cloud Storage rates | Management fees apply on top, based on region, instances, notebooks and managed notebooks used. |
Pipelines | Starting at $0.03 per pipeline run | Based on execution charge, resources used and any additional service fees. |
Vector Search | Based on data size, queries per second and number of nodes | Covers serving and building. |
Document retrieval (Agent Search) | About $4.00 per 1,000 queries for standard semantic or keyword retrieval | Charged per 1,000 successful queries, plus storage for indexed data billed per GiB per month. |
Scroll sideways to see all columns. With a keyboard, focus this table and use the arrow keys.
| Commitment | Global endpoints | Non-global endpoints |
|---|---|---|
1 week | $7.14 | $7.854 |
1 month | $3.698630137 | $4.068493151 |
3 months | $3.287671233 | $3.616438356 |
1 year | $2.739726027 | $3.01369863 |
Non-global endpoints: non-global prices apply to generally available Gemini 3 and later families of all Google models starting on July 1, 2026; before July 1, 2026, global endpoint prices apply to non-global endpoints.
Free credits: eligible new users get a $300 Google Cloud free trial credit valid for 90 days, which can go toward agent runtime compute, active memory and model queries on the platform.
Tuned models: a tuned model endpoint has the same prediction price as its base model.
Public signals
About these signals
Verified factual signals from public sources. They indicate observable activity or interest, not total adoption, product quality, or cost.
153 GitHub commits 90d910 GitHub stars0 vulnerabilities across 2 packages
See all signals from 7 sources
npm · @google-cloud/aiplatform@7.7.0 · PyPI · google-cloud-aiplatform@2.6.0
Alternatives to Gemini Enterprise Agent Platform
The reviewed substitutes for Gemini Enterprise Agent Platform among the ML platforms, and what would make each one the better answer.
Other approaches
A different approach to the same problem. Each substitutes only for the workload named beside it.
- ClearML
- ClearML is used rather than Vertex AI for self-hosted MLOps workloads requiring integrated experiment, dataset, and compute orchestration. **ClearML** is an open-source MLOps platform combining experiment tracking, pipeline orchestration, dataset versioning, model deployment, and compute orchestration.
- Databricks
- Databricks and Gemini Enterprise Agent Platform both train, register and serve machine learning models. Databricks keeps data engineering and ML in one lakehouse system, while Gemini Enterprise Agent Platform is Google Cloud's separate managed service for training, AutoML, model serving and AI agents.Applies to: Whether machine learning runs on the data platform or on a separate managed ML service.
- Domino Data Lab
- Vertex AI replaces Domino Data Lab for Google Cloud-native custom training, prediction, and pipeline workloads. We recommend Vertex AI over Domino Data Lab for teams that need managed Google Cloud training, prediction, and pipeline operations with visible service-level pricing inputs.
- Amazon SageMaker
- Amazon SageMaker is chosen instead of Vertex AI for managed ML workloads aligned to Amazon cloud infrastructure. **Amazon SageMaker** is a fully managed service for building, training, and deploying machine learning models at scale.
- Azure Machine Learning
- Enterprise ML platform for the full machine learning lifecycle — data prep, model training, deployment, and MLOps with responsible AI built in.Applies to: The full machine learning lifecycle on a single cloud's managed platform.
- MLflow
- MLflow and Gemini Enterprise Agent Platform can both log experiments and register models. Google's platform includes tracking as one part of a managed service for training, tuning and serving models on Google Cloud; MLflow is an open-source tracker and registry that stays the same wherever the models run.Applies to: Deciding whether experiment tracking comes from the ML platform or a dedicated tool.