Model deployment

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Billing methods

Before deployment, you can view the estimated hourly cost of different models in the Model Deployment console.

	Provisioned Throughput (PTU, Provisioned Throughput Unit) (High throughput; high performance)		Model Unit (Custom performance metrics; resource isolation)		Token-based usage (Pay-as-you-go after fine-tuning/effect validation)
Definition	A model deployment method that reserves platform resources to guarantee a specific TPM throughput capacity; no rate limiting within the guaranteed quota.		A model deployment method that configures computing power based on usage duration and the number of Model Units, with dedicated resources.		A model deployment method that uses the input Tokens and output Tokens generated per call as the usage metering basis.
Advantages	Provides stable throughput capacity, lower latency, and stronger resource certainty for high-load production environments. Compared with Token-based billing, TPS (Tokens generated per second) typically increases by approximately 1.5 to 2.0 times. Supports auto-renewal settings.		Performance metrics such as latency/throughput can be customized. Supports auto-renewal settings.		No charge when not in use.
Supported models	Some pre-configured models		Some pre-configured models and all fine-tuned models		Some models fine-tuned with LoRA
Use cases	Intelligent customer service for banking apps (stable traffic, requires guaranteed concurrent experience). Real-time content moderation for social platforms (requires stable processing of predictable pipeline tasks). Public cloud translation API (provides baseline service guarantees for standard package users).		E-commerce exclusive fine-tuned large models (deploy private models, manually scale up during major promotions). Pharmaceutical company molecular screening models (require dedicated resources for long-running tasks). Autonomous driving simulation (requires long-duration continuous computing).		Fine-tuned model effect validation
Billing diagram
Billing method	By usage duration and provisioned throughput Pay-as-you-go, daily package		By usage duration and number of Model Units Pay-as-you-go, monthly package		By model Token usage Pay-as-you-go
Scaling method	Self-service increase/decrease of throughput		Self-service increase/decrease of Model Units		Submit an application in the console and wait for manual review.
Product constraints	Prepaid billed daily. No early refund available. If usage within a unit time exceeds the purchased throughput, it is handled according to the overflow strategy selected at creation: auto-overflow switches to model invocation pay-as-you-go billing for that model, while using-only-PTU-capacity returns 429.		After a prepaid purchase, if you cancel early within the first month, the daily unit price (≈ monthly unit price / 30) will be billed at 1.2 times		Only supports some models after efficient fine-tuning (LoRA). Will be automatically released if not used within one month.

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Billing details

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Deployment methods

If you are prompted with insufficient permissions, refer to:What should I do if "insufficient permissions" is prompted during deployment?

Go to themodel deployment console.
Enter the service name, select a model and a billing method, keep other settings as default, and click OK.
When the deployment status isRunning, the model has been deployed successfully. Fees will be incurred after the model is successfully deployed.

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Deployment configuration

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Deployment list page

• Service name: The name of the deployment service. Click to view deployment details.
• Model name: The model used for deployment.
• Model Code: The unique identifier generated after the model is successfully deployed, used to specify the model when calling the API.
• Deployment status/Event status: Includes Pending deployment, Deploying, Running, Deployment failed, Going offline, Service paused, Stopped, Deleting, Subscription suspended/Overdue payment suspended, Resuming service, Running (Changing configuration), Running (Change failed), and other statuses.
• Billing method: The billing method of the current deployment service.
• Deployment details: Configuration information such as model unit type and replica count.
• Throttling details: Displays the throttling configuration of the current deployment service, such as RPM (requests per minute) and TPM (tokens per minute).
• Service time: Displays the creation time and expiration time of the deployment service.
• Operation: Depending on the deployment status and billing method, you can perform operations such as Update, Monitor, Scale, Renew, Take offline, Delete, and Try.

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Post-deployment calls

import os
import dashscope

messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "Who are you?"},
]
dashscope.base_http_api_url = 'https://dashscope-intl.aliyuncs.com/api/v1'
response = dashscope.Generation.call(
    # If you have not configured environment variables, replace the next line with your Bailian API Key: api_key="sk-xxx",
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    model="qwen3-max-xxx-xxx",  # Please replace with the code returned after the model is successfully deployed
    messages=messages,
    result_format="message",
    enable_thinking=False,
)
print(response)

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Scaling a Deployment Service

• Preset throughput (billed by duration): Click the Scaling button to self-service, manually adjust the number of instances. For detailed fee reduction and refund rules, please refer to: Refund rules for configuration downgrades.
• Model unit (billed by duration): Click the Scaling button to self-service, manually adjust the number of instances.

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Take a Deployment Service Offline

• Model unit prepaid: Click Deactivate and confirm.
• Postpaid: Click Delete and confirm.

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Other Operations

• Update: Update the model version of the deployed service, supporting full update or batch update (canary release).
• Delete: Pay-as-you-go services can be deleted directly to stop billing.
• Renew: Prepaid services can be renewed to extend the service time, and auto-renewal is supported.
• Buy capacity package: Purchase a capacity package for the preset throughput deployment.

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FAQ

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Can I upload and deploy my own models?

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What should I do if "insufficient permissions" is prompted during deployment?

1. If "Missing permission for this module" is displayed, please ensure that your account has the Model Deployment - Operation permission on the permission management page of the business space.
   
   If you cannot operate normally, please contact your organization or IT administrator to add the relevant permissions or check the permission issues on your behalf.
2. If the error "xx business space does not have permission to deploy the xx model" is reported during deployment, please go to the Business Space Management page of Model Studio to add the deployment permission of the corresponding model for the corresponding business space.

   API call error: Workspace xxx does not have deployment privilege for model xxxx.

   
   
   If insufficient permissions are prompted, please contact your organization or IT administrator to add the relevant permissions or operate on your behalf.