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To run Gemini on Vertex AI, use a billed Google Cloud project, enable the Vertex AI API, authenticate with Application Default Credentials (ADC) or an approved API-key flow, grant the caller Vertex AI User permissions, install the Google Gen AI SDK, set the project and location, and call models.generate_content with a currently supported Gemini model ID. The complete path is: create and configure the project, install an SDK, select a model in Model Garden, make a test request, then add quota, cost, security and lifecycle controls before production.
What you need before the first request
Vertex AI is Google Cloud’s managed platform for accessing Gemini and other generative models through authenticated regional endpoints, SDKs and REST. A request is billed to a Google Cloud project rather than to an anonymous web session, so the project, billing account, IAM and location all matter.
- A Google Cloud project with billing enabled.
- The Vertex AI API enabled in that project.
- The
gcloudCLI installed and initialized. - Credentials available to your local process or runtime.
roles/aiplatform.user, or a custom role containing equivalent permissions, for the identity making requests.- A supported Gemini model ID and a location that supports that model and your residency requirements.
The official Vertex AI quickstart is the authoritative setup reference. Treat model IDs, regions, prices, quotas and endpoint status as changeable configuration, not permanent constants.
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Use Model Garden to discover, test, customize, tune, evaluate and serve models. It includes Google models, partner models and open models. Google’s generative-AI overview says Model Garden contains more than 200 enterprise-ready models and describes Gemini support for multimodal text, image, video and audio processing, grounding with Google Search, Google Maps or private data, embeddings, tuning, evaluation, agents, image generation and video generation. The same overview describes a context window of up to 2 million tokens for Gemini; check the model page for the exact limit and feature combination you intend to use.
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Do not select a model solely by its marketing name. Compare the dimensions that affect your application:
| Decision axis | What to verify |
|---|---|
| Capability and modality | Text-only or multimodal input, output types, reasoning behavior, grounding and tool support. |
| Context and quality | Maximum context for the specific model version, instruction-following quality and any thinking or reasoning mode. |
| Latency and throughput | Expected response time, concurrency needs, quota behavior and whether reserved capacity is available. |
| Cost | Input and output token prices, thinking-token treatment and charges for grounding or other tools. |
| Location and residency | Whether the model is available in your selected region and whether that region satisfies your data-governance policy. |
| Lifecycle | Preview, GA, deprecation and shutdown dates in the model reference and release notes. |
| API compatibility | Whether your selected SDK version and API version support the model’s request and response features. |
For production reservations, use an explicit model-version ID rather than an alias. Google notes that Provisioned Throughput applies to requests sent to the reserved model and location; requests made indirectly through products such as Vertex AI Search or agent products are not guaranteed to use that reservation. Confirm the exact version in the supported-models reference.
Configure Google Cloud and authentication
- Select or create a project. In Google Cloud Console, choose the project that will own the API calls and billing. Verify that a billing account is attached.
- Enable Vertex AI. Enable the Vertex AI API for the project. If you cannot enable APIs, ask a project or organization administrator.
- Install and initialize gcloud. Install the Google Cloud CLI, then run
gcloud initand select the correct account and project. - Create local ADC. For local development, run
gcloud auth application-default login. The SDK can then obtain credentials through the standard Application Default Credentials chain. - Grant IAM. Ask an administrator for
roles/aiplatform.user, or an equivalent least-privilege custom role. A successful Google login without the required IAM permission still produces authorization errors. - Choose a location. The quickstart uses
global, but a regional endpoint may be required for latency, residency, quota or model-availability reasons. Confirm current support in the location and model documentation before deployment.
The quickstart also documents API-key choices, including Vertex AI express mode. Use the authentication method approved for your environment, never commit keys or credential files to source control, and separate development, staging and production identities.
Install the Google Gen AI SDK
Google’s current unified Gen AI SDK is the recommended starting point for Gemini calls. Install the package for the language used by your service:
| Language | Install command or dependency |
|---|---|
| Python | pip install --upgrade google-genai |
| Node.js | npm install @google/genai |
| Go | go get google.golang.org/genai |
| Java | Maven dependency com.google.genai:google-genai |
Keep the SDK version under dependency management and test upgrades against the model versions and API surface you actually use. Installation details and language examples are maintained in the Google Gen AI SDK overview.
Set the Vertex AI environment variables
Before starting the application, set the project, location and Vertex AI switch. The quickstart uses these names:
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export GOOGLE_CLOUD_PROJECT="YOUR_PROJECT_ID"
export GOOGLE_CLOUD_LOCATION="global"
export GOOGLE_GENAI_USE_VERTEXAI=TrueOn Windows PowerShell, use $env:GOOGLE_CLOUD_PROJECT="YOUR_PROJECT_ID", $env:GOOGLE_CLOUD_LOCATION="global" and $env:GOOGLE_GENAI_USE_VERTEXAI="True". Replace global only after checking that the chosen model supports the desired location. Avoid embedding project IDs, locations or credentials in source when deployment configuration can provide them.
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Make the minimal Python request
This is the official quickstart pattern using the stable Vertex AI API version. Replace the model string after checking the current model reference.
from google import genai
from google.genai.types import HttpOptions
client = genai.Client(http_options=HttpOptions(api_version="v1"))
response = client.models.generate_content(
model="gemini-2.5-flash",
contents="How does AI work?",
)
print(response.text)Run the script in the same environment where ADC and the three variables are configured. A successful response prints generated text. The model ID in this example is a documented quickstart example, not a promise that it will remain available indefinitely; check supported models before copying it into a production configuration.
contents can represent the prompt or supported multimodal content for the selected model. Keep the first test deliberately small so authentication, endpoint selection and permissions are isolated from application-specific prompt and payload problems.
Use Node.js, Go, Java or REST
The same project, location, credentials and model-selection rules apply in every language. The SDK overview and quickstart provide complete language-specific examples; do not mix authentication settings from one SDK with another without checking its current documentation.
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For a direct REST call, obtain a short-lived bearer token with gcloud auth print-access-token and post a JSON generateContent request to this endpoint pattern:
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https://${API_ENDPOINT}/v1/projects/${GOOGLE_CLOUD_PROJECT}/locations/${GOOGLE_CLOUD_LOCATION}/publishers/google/models/${MODEL_ID}:generateContentSend the bearer token in the HTTP Authorization header. Substitute the endpoint, project, location and model values at runtime; do not paste an access token into code or logs. The quickstart shows the request body and complete Go, Node.js and Java forms.
Understand pricing before increasing traffic
Vertex AI charges by model and token direction, with additional charges possible for tools or grounding. The following figures are listed in USD on Google’s pricing page and identified there as Google Cloud 2025 prices:
| Model and usage | Published price | Qualification |
|---|---|---|
| Gemini 2.5 Pro input | $1.25 per 1 million tokens | Input up to 200,000 tokens. |
| Gemini 2.5 Pro text output | $10 per 1 million tokens | Text output rate. |
| Gemini 2.5 Flash input | $0.15 per 1 million tokens | Input rate. |
| Gemini 2.5 Flash non-thinking text output | $0.60 per 1 million tokens | Non-thinking text output rate. |
These are not universal or permanent prices. Model variants, thinking behavior, cached content, grounding, region and currency treatment can change the bill. Check the current Vertex AI generative-AI pricing page immediately before budgeting or publishing a cost estimate. In your service, record model ID, input-token count, output-token count and tool usage so billing anomalies can be explained.
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Handle quotas, capacity and HTTP 429 responses
Dynamic Shared Quota is the default pay-as-you-go capacity model and has no predefined usage limit. That does not mean unlimited instantaneous throughput: temporary exhaustion of the shared pool can return HTTP 429. A 429 can therefore indicate capacity pressure rather than an invalid prompt.
- Retry transient 429 responses with exponential backoff and jitter.
- Set a maximum retry count and an overall deadline so a request cannot hang indefinitely.
- Honor a server-provided retry delay when one is returned.
- Limit client concurrency and queue work rather than creating an unbounded burst.
- Log status, model, location, attempt number and latency, but never log credentials or sensitive prompt content unless your policy explicitly allows it.
- Measure success rate, token volume and tail latency separately for each model and region.
When predictable capacity is more important than pay-as-you-go elasticity, Provisioned Throughput reserves a fixed amount of throughput for a chosen model and location. Reservation planning must use the exact model version and endpoint path that production sends; indirect calls through other Vertex AI products are not guaranteed to consume the reservation. Read the current throughput and quota guidance before purchasing or sizing capacity.
Add optional code execution carefully
Code execution is an optional tool that can be enabled in a generateContent request. Gemini can generate and run Python iteratively, which is useful for calculations and data transformations. The service does not support file I/O and has a maximum runtime of 30 seconds. Treat generated code and its output as untrusted: validate results, constrain what your application accepts, and do not assume access to your files, network or secrets. Feature details are in the code execution API reference.
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Secure a production integration
Use least privilege and isolated identities
Give each workload only the Vertex AI permissions it needs. Use separate service accounts or workload identities for environments, rotate any API keys, and keep ADC files and tokens out of repositories, container images, issue trackers and build logs.
Choose location deliberately
Location affects latency, availability, quota pools and data-residency obligations. A global endpoint may simplify experimentation, while a regional endpoint can be necessary for governance or predictable placement. Verify current location support for the exact model and modality before rollout; availability can differ between preview and GA versions.
Pin versions when behavior must be stable
Aliases are convenient for experimentation but can move as Google updates models. Pin an explicit model-version ID for regression-tested production behavior and for Provisioned Throughput. Maintain a controlled upgrade process that evaluates quality, latency, cost and safety before changing the pin.
Control payloads and outputs
Set application-level limits for prompt size, attachment size, response length, timeout and retries. Validate structured output before storing or executing it, redact sensitive data where required, and preserve request metadata needed for incident diagnosis without retaining content unnecessarily.
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Preview and GA endpoints, prices and model support change. Google’s June 2025 release notes announced Gemini 2.5 Pro and Flash becoming GA and preview endpoint shutdowns after July 15, 2025. That example illustrates why a previously working endpoint can become unavailable after a release window. Before shipping, confirm all of the following against current Google Cloud pages:
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- API version and SDK release.
- Supported region or global endpoint.
- Input and output pricing, including tool or grounding charges.
- Quota limits, Dynamic Shared Quota behavior and any Provisioned Throughput requirement.
- Preview, GA, deprecation and shutdown notices.
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Troubleshoot the first failed call
Permission denied or unauthenticated
Confirm that ADC was created for the account or runtime identity actually executing the program, that the project in GOOGLE_CLOUD_PROJECT is the billed project, and that the identity has roles/aiplatform.user or equivalent permissions. Re-run gcloud auth application-default login for local development and check organization policies with an administrator.
Not found or invalid model
Check spelling, publisher path, API version, location and lifecycle status. A model available in one region or API version may not be available in another. Copy the current ID from the supported-models page rather than relying on an old code sample.
Quota exceeded or 429
Determine whether the response is temporary shared-capacity exhaustion or a configured quota limit. Apply bounded exponential backoff, reduce concurrency and inspect usage by model and region. If the workload needs a guaranteed rate, evaluate Provisioned Throughput.
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Compare recorded input and output tokens with the pricing page for the exact model and date, then check whether thinking, grounding, code execution or other tools added billable usage. Pricing pages are the source of truth for current rates.
A repeatable deployment checklist
- Create a billed project and enable Vertex AI.
- Set up ADC locally or a workload identity in deployment; grant least-privilege IAM.
- Choose a supported location and verify residency requirements.
- Install and pin a tested Google Gen AI SDK version.
- Select and test a model in Model Garden; record its exact version and lifecycle status.
- Set
GOOGLE_CLOUD_PROJECT,GOOGLE_CLOUD_LOCATIONandGOOGLE_GENAI_USE_VERTEXAI=True. - Run the minimal
generate_contentrequest, then add your real prompt and modalities. - Instrument token usage, latency, errors and retries; redact secrets and sensitive content.
- Implement bounded backoff for 429 responses and a concurrency limit.
- Recheck pricing, quotas, locations, SDK compatibility and release notes before production promotion.
Frequently Asked Questions
Can I call Gemini on Vertex AI without a Google Cloud project?
No. Vertex AI requests are associated with a Google Cloud project, and the quickstart requires a project with billing enabled and the Vertex AI API turned on.
Is an API key required?
No. Application Default Credentials are the documented local-development route. The quickstart also documents API-key options, including Vertex AI express mode; choose the method that matches your security and deployment policy.
Why does a valid model return 429?
Dynamic Shared Quota can temporarily exhaust its shared capacity even without a fixed usage limit. Use bounded exponential backoff with jitter, reduce bursts, and consider Provisioned Throughput for predictable capacity.
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Use an explicit model-version ID when reproducibility, regression testing or Provisioned Throughput matters. Aliases are more convenient but can move as models are updated.
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