Generative AI on Vertex AI rate limits

Google Cloud uses quotas to help ensure fairness and reduce spikes in resource use and availability. A quota restricts how much of a Google Cloud resource your Google Cloud project can use. Quotas apply to a range of resource types, including hardware, software, and network components. For example, quotas can restrict the number of API calls to a service, the number of load balancers used concurrently by your project, or the number of projects that you can create. Quotas protect the community of Google Cloud users by preventing the overloading of services. Quotas also help you to manage your own Google Cloud resources.

The Cloud Quotas system does the following:

• Monitors your consumption of Google Cloud products and services
• Restricts your consumption of those resources
• Provides a way to request changes to the quota value

In most cases, when you attempt to consume more of a resource than its quota allows, the system blocks access to the resource, and the task that you're trying to perform fails.

Quotas generally apply at the Google Cloud project level. Your use of a resource in one project doesn't affect your available quota in another project. Within a Google Cloud project, quotas are shared across all applications and IP addresses.

Rate limits

This table lists the rate limits that apply to the following models across all regions for the metric, generate_content_input_tokens_per_minute_per_base_model:

Base model	Tokens per minute
base_model: gemini-1.5-flash	4M (4,000,000)
base_model: gemini-1.5-pro	4M (4,000,000)

Quotas by region and model

The requests per minute (RPM) quota applies to a base model and all versions, identifiers, and tuned versions of that model. The following examples show how the RPM quota is applied:

• A request to the base model, gemini-1.0-pro, and a request to its stable version, gemini-1.0-pro-001, are counted as two requests toward the RPM quota of the base model, gemini-1.0-pro.
• A request to two versions of a base model, `gemini-1.0-pro-001` and `gemini-1.0-pro-002`, are counted as two requests toward the RPM quota of the base model, `gemini-1.0-pro`.
• A request to two versions of a base model, `gemini-1.0-pro-001` and a tuned version named `my-tuned-chat-model`, are counted as two requests toward the base model, `gemini-1.0-pro`.

The quotas apply to Generative AI on Vertex AI requests for a given Google Cloud project and supported region.

View the quotas in the Google Cloud console

To view the quotas in the Google Cloud console, do the following:

1. In the Google Cloud console, go to the IAM & Admin Quotas page.
2. Click View Quotas in Console.
3. In the Filter field, specify the dimension or metric.

Dimension (model identifier)	Metric (quota identifier for Gemini models)
base_model: gemini-1.5-flash base_model: gemini-1.5-pro	You can request adjustments in the following: aiplatform.googleapis.com/generate_content_requests_per_minute_per_project_per_base_model aiplatform.googleapis.com/generate_content_input_tokens_per_minute_per_base_model
All other models	You can adjust only one quota: aiplatform.googleapis.com/generate_content_requests_per_minute_per_project_per_base_model

View the quotas by region and by model

Choose a region to view the quota limits for each available model:

Increase the quotas

If you want to increase any of your quotas for Generative AI on Vertex AI, you can use the Google Cloud console to request a quota increase. To learn more about quotas, see Work with quotas.

RAG Engine quotas

Preview

RAG Engine is a Preview offering, subject to the "Pre-GA Offerings Terms" of the Google Cloud Service Specific Terms. Pre-GA products and features may have limited support, and changes to Pre-GA products and features may not be compatible with other Pre-GA versions. For more information, see the launch stage descriptions. Further, by using the Gemini API on Vertex AI, you agree to the Generative AI Preview terms and conditions (Preview Terms).

For each service to perform retrieval-augmented generation (RAG) using RAG Engine, the following quotas apply, with the quota measured as requests per minute (RPM).

Service	Quota	Metric
RAG Engine data management APIs	60 RPM	VertexRagDataService requests per minute per region
RetrievalContexts API	1,500 RPM	VertexRagService retrieve requests per minute per region
base_model: textembedding-gecko	1,500 RPM	Online prediction requests per base model per minute per region per base_model An additional filter for you to specify is base_model: textembedding-gecko

The following limits apply:

Service	Limit	Metric
Concurrent ImportRagFiles requests	3 RPM	VertexRagService concurrent import requests per region
Maximum number of files per ImportRagFiles request	10,000	VertexRagService import rag files requests per region

For more rate limits and quotas, see Generative AI on Vertex AI rate limits.

Batch requests

The quotas and limits for batch requests are the same across all regions.

Concurrent batch requests

The following table lists the quotas for the number of concurrent batch requests:

Quota	Value
aiplatform.googleapis.com/textembedding_gecko_concurrent_batch_prediction_jobs	4
aiplatform.googleapis.com/gemini_pro_concurrent_batch_prediction_jobs	4
aiplatform.googleapis.com/gemini_flash_concurrent_batch_prediction_jobs	4

If the number of tasks submitted exceeds the allocated quota, the tasks are placed in a queue and processed when the quota capacity becomes available.

Batch request limits

The following table lists the size limit of each batch text generation request.

Model	Limit
gemini-1.5-pro	50k records
gemini-1.5-flash	150k records
gemini-1.0-pro	150k records
gemini-1.0-pro-vision	50k records

Custom-trained model quotas

The following quotas apply to Generative AI on Vertex AI tuned models for a given project and region:

Quota	Value
Restricted image training TPU V3 pod cores per region * supported Region - europe-west4	64
Restricted image training Nvidia A100 80GB GPUs per region * supported Region - us-central1 * supported Region - us-east4	8 2

* Tuning scenarios have accelerator reservations in specific regions. Quotas for tuning are supported and must be requested in specific regions.

Text embedding limits

Each text embedding model request can have up to 250 input texts (generating 1 embedding per input text) and 20,000 tokens per request. Only the first 2,048 tokens in each input text is used to compute the embeddings.

Gen AI evaluation service service quotas

The Gen AI evaluation service uses gemini-1.5-pro as a judge model, and uses mechanisms to ensure consistent and objective evaluation for model-based metrics. A single evaluation request for a model-based metric might result in multiple underlying requests to the Gen AI evaluation service. Each model's quota is calculated on a per-project basis, which means that any requests directed to gemini-1.5-pro for model inference and model-based evaluation contribute to the quota. Different model quotas are set differently. The quota for the Gen AI evaluation service and the quota for the underlying autorater model are shown in the table.

Request quota	Default quota
Gen AI evaluation service requests per minute	1,000 requests per project per region
Online prediction requests per minute for base_model: gemini-1.5-pro	See Quotas by region and model.

If you receive an error related to quotas while using the Gen AI evaluation service, you might need to file a quota increase request. See View and Manage Quotas for more information.

Limit	Value
Gen AI evaluation service request timeout	60 seconds

First-time users of the Gen AI evaluation service within a new project might experience an initial setup delay generally up to two minutes. This is a one-time process. If your first request fails, wait a few minutes and then retry. Subsequent evaluation requests typically complete within 60 seconds. The maximum input and output tokens are limited for the model-based metrics as per the model used as the autorater. See Model information | Generative AI on Vertex AI | Google Cloud for limits for relevant models.

Pipeline evaluation quotas

If you receive an error related to quotas while using the evaluation pipelines service, you might need to file a quota increase request. See View and Manage Quotas for more information. The evaluation pipelines service uses Vertex AI Pipelines to run PipelineJobs. See relevant quotas for Vertex AI Pipelines. The following are general quota recommendations:

Service	Quota	Recommendation
Vertex AI API	Concurrent LLM batch prediction jobs per region	Pointwise: 1 * num_concurrent_pipelines Pairwise: 2 * num_concurrent_pipelines
Vertex AI API	Evaluation requests per minute per region	1000 * num_concurrent_pipelines

Additionally, when calculating model-based evaluation metrics, the autorater might hit quota issues. The relevant quota depends on which autorater was used:

Tasks	Quota	Base model	Recommendation
summarization question_answering	Online prediction requests per base model per minute per region per base_model	text-bison	60 * num_concurrent_pipelines

Vertex AI Pipelines

Each tuning job uses Vertex AI Pipelines. For more information, see Vertex AI Pipelines quotas and limits.

Vertex AI Reasoning Engine

The following quotas and limits apply to Vertex AI Reasoning Engine for a given project in each region.

Quota	Value
Create/Delete/Update Reasoning Engine per minute	10
Query Reasoning Engine per minute	60
Maximum number of Reasoning Engine resources	100

What's next

• To learn more about dynamic shared quota, see Dynamic shared quota.
• To learn about quotas and limits for Vertex AI, see Vertex AI quotas and limits.
• To learn more about Google Cloud quotas and limits, see Understand quota values and system limits.