Operations
Fine-Tuning and Deployment
Fine-tuning and deployment are separate from ordinary model invocation and can introduce training, hosting, scaling, and monitoring costs.
Who this is for
Customers who need custom model behavior or hosted deployments.
Configuration reference
Values to confirm before setup
Deployment model value
Use the deployed model code after deployment
Invocation routes
OpenAI-compatible, DashScope, or Assistant SDK where supported
Cost areas
Training, deployment, provisioned throughput, model units
Setup flow
Practical steps
- 01Confirm whether prompt engineering is insufficient.
- 02Prepare training data and review permissions.
- 03Estimate training and deployment cost separately.
- 04Deploy to the target region.
- 05Use the deployed model code in API calls.
- 06Monitor usage and scale/downline when needed.
When to propose it
Fine-tuning should be proposed only when the customer has repeatable data and a measurable quality target. It is not a shortcut for unclear requirements.
Common mistakes
Check these before escalating
- Training data rights must be reviewed.
- Deployment can create ongoing hosting cost.
- The deployed model code, not the base model name, may be required for calls.
Related guides
Security and Compliance Checklist
Security review covers key ownership, permissions, transport, data location, privacy, training-data commitments, and customer approval.
Usage Monitoring and Cost Control
Production buyers need visibility into call volume, token consumption, success rate, quota remaining, and monthly replenishment.
Billing and Pricing Structure
A trustworthy quote separates official model usage, Token Plan subscription, shared quota, payment costs, taxes, and ModelSmarter service fees.