Run inference against one of your deployed models
Sends the request body to the named model's predictor and answers the predictor's reply — its status code, its body bytes and its Content-Type, all…
POST /v1/ml/models/{name}/predict
| Address | https://api.hanzo.ai/v1/ml/models/{name}/predict |
| Method | POST |
| Operation | post_ml_models_by_name_predict |
| Auth | Authorization: Bearer $HANZO_API_KEY |
Sends the request body to the named model's predictor and answers the predictor's reply — its status code, its body bytes and its Content-Type, all unchanged. This is the inference call itself, not a description of one.
VERBATIM IS THE CONTRACT, and it is why this route is not a typed op: a model-side error has to surface as the model's own error, not as this layer's paraphrase of it. The body shape is the kserve v2 inference protocol's, which means the runtime decides it, not this API. The v2 model name defaults to the resource name — kserve's single-model convention — and a multi-model runtime selects one with the model query parameter.
A model that exists but has no serving address yet answers 503 'not ready' rather than a confusing connection error: deployed is not the same as serving. Scoped to the caller's own tenant namespace from the validated org and project, so a name another tenant owns is simply a 404. The predictor's response body is read up to a fixed ceiling.
Request
1 field.
| Field | In | Type | Required | Description |
|---|---|---|---|---|
name | path | string | yes |
Response
The document declares no response body for this operation. It answers 200 on success and the platform error shape on failure — see Errors.
Examples
hanzo ml models predict <name>import { Configuration, MlApi } from 'hanzoai';
const api = new MlApi(new Configuration({ accessToken: process.env.HANZO_API_KEY }));
const { data } = await api.postMlModelsByNamePredict({ name: 'name' });from hanzoai.cloud import ApiClient, Configuration
from hanzoai.cloud.api import MlApi
client = ApiClient(Configuration(access_token=os.environ["HANZO_API_KEY"]))
result = MlApi(client).post_ml_models_by_name_predict(name='name')cfg := cloud.NewConfiguration()
cfg.AddDefaultHeader("Authorization", "Bearer "+os.Getenv("HANZO_API_KEY"))
client := cloud.NewAPIClient(cfg)
resp, _, err := client.MlAPI.PostMlModelsByNamePredict(context.Background()).Execute()
if err != nil {
return err
}use hanzo_cloud::apis::{configuration::Configuration, ml_api};
let mut cfg = Configuration::new();
cfg.bearer_access_token = std::env::var("HANZO_API_KEY").ok();
let result = ml_api::post_ml_models_by_name_predict(&cfg, Default::default()).await?;import ai.hanzo.cloud.ApiClient;
import ai.hanzo.cloud.api.MlApi;
ApiClient client = new ApiClient();
client.setRequestInterceptor(b -> b.header("Authorization", "Bearer " + System.getenv("HANZO_API_KEY")));
var result = new MlApi(client).postMlModelsByNamePredict();curl -X POST https://api.hanzo.ai/v1/ml/models/<name>/predict \
-H "Authorization: Bearer $HANZO_API_KEY"The door reaches ml through the ml tool, which names its 7 operations with its own verbs — this one among them, under a name only the door declares. describe explains any of them:
curl -X POST https://api.hanzo.ai/v1/mcp \
-H "Content-Type: application/json" \
-d '{
"jsonrpc": "2.0",
"id": 1,
"method": "tools/call",
"params": {
"name": "describe",
"arguments": {
"op": "get_ml_health"
}
}
}'How is this guide?