🆕 Async inference by environment
Use this endpoint to call the model associated with the specified environment asynchronously.
Parameters
The ID of the model you want to call.
The name of the model’s environment you want to call.
Headers
Your Baseten API key, formatted with prefix Api-Key (e.g. {"Authorization": "Api-Key abcd1234.abcd1234"}).
Body
There is a 256 KiB size limit to /async_predict request payloads.
JSON-serializable model input.
webhook_endpoint is empty, your model must save prediction outputs so they can be accessed later. URL of the webhook endpoint. We require that webhook endpoints use HTTPS.
Priority of the request. A lower value corresponds to a higher priority (e.g. requests with priority 0 are scheduled before requests of priority 1).
priority is between 0 and 2, inclusive.
Maximum time a request will spend in the queue before expiring.
max_time_in_queue_seconds must be between 10 seconds and 12 hours, inclusive.
Exponential backoff parameters used to retry the model predict request.
Response
The ID of the async request.
Rate limits
Two types of rate limits apply when making async requests:
-
Calls to the
/async_predictendpoint are limited to 200 requests per second. -
Each organization is limited to 50,000
QUEUEDorIN_PROGRESSasync requests, summed across all deployments.
If either limit is exceeded, subsequent /async_predict requests will receive a 429 status code.
To avoid hitting these rate limits, we advise:
- Implementing a backpressure mechanism, such as calling
/async_predictwith exponential backoff in response to 429 errors. - Monitoring the async queue size metric. If your model is accumulating a backlog of requests, consider increasing the number of requests your model can process at once by increasing the number of max replicas or the concurrency target in your autoscaling settings.