Deploying a model
To deploy a model, follow the Deploy a model guide.Modifying a model configuration
Select the model and open the Configuration tab. You can modify:- Display name
- Hugging Face token
- Readiness level
- Scaling policy
- Worker timeout
- Pricing plan
- Pipeline-specific parameters
Managing a model
Select the model and open the Manage tab.Redeploying a model
Click Redeploy to redeploy with the current configuration. Redeploy after updating configuration, model weights, or tokenizer/processor files on the Hugging Face repository.Deleting a model
Click Delete to remove the model and all associated resources.Deleting a model is irreversible. For Always ready or Super fast deployments, all weights and prepared files are permanently removed from our infrastructure.
Analytics
Open the Analytics tab to view insights over a selected time range:- Number of requests
- Cost
- Instance count over time
- Tokens processed (for LLMs)
- Response time
- Pipeline-specific metrics
Logs
Open the Logs tab for errors, warnings, and operational events. Check logs first when debugging deployment or inference issues — see Troubleshooting.Model lifecycle
Every deployed model moves through defined lifecycle stages:Building
The model is being deployed on SynapsAI Cloud.- Weights are fetched from Hugging Face.
- Quantization, optimization, or validation may run.
- Inference is not available during this stage.
Sleeping
No instance is currently loaded.- The model is deployed but not active.
- You can send inference requests — the platform automatically loads an instance in response.
- Load time depends on your readiness level.
Ready
At least one instance is loaded and serving inference immediately — no load delay on incoming requests.Failed
Deployment failed.- This may occur due to invalid configuration, missing files, incompatible format, or internal errors.
- Resolve the issue and redeploy. If the problem persists, contact support.