Whether model serving can actually work right now

GET /v1/ml/health

Reports whether the model-serving plane is genuinely usable: that the Kubernetes API answers, that the InferenceService CRD is actually served by this cluster, and that the cluster holds at least one serving runtime to run a model ON. It is a REAL probe, not status theatre — it makes a live call rather than reporting a flag set at boot.

200 only when everything checks out. Otherwise 503 CARRYING THE REPORT — which component failed, and the real error — and that body is the reason this is not a typed op: a typed op reaches a non-2xx by returning an error, and the envelope that produces would drop exactly the detail the probe exists to deliver.

The runtime count is reported as its own field and is a SEPARATE fact from the CRD being served: a cluster with the CRD but no runtime accepts a deploy and then never schedules it, so reporting only the CRD would answer 200 while every model hangs. A runtime list this service cannot read reports the read error instead of a count, because a missing grant is a broken probe and not an empty cluster.

It answers about the cluster, not about a tenant, so it takes no org and reveals no tenant data. A cluster with no kserve CRD reports degraded honestly rather than failing later at the first deploy.

Request

GET /v1/ml/health takes no parameters and no body — the credential is the whole request.

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 health

import { Configuration, MlApi } from 'hanzoai';

const api = new MlApi(new Configuration({ accessToken: process.env.HANZO_API_KEY }));
const { data } = await api.getMlHealth();

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).get_ml_health()

cfg := cloud.NewConfiguration()
cfg.AddDefaultHeader("Authorization", "Bearer "+os.Getenv("HANZO_API_KEY"))
client := cloud.NewAPIClient(cfg)

resp, _, err := client.MlAPI.GetMlHealth(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::get_ml_health(&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).getMlHealth();

curl https://api.hanzo.ai/v1/ml/health \
  -H "Authorization: Bearer $HANZO_API_KEY"

curl -X POST https://api.hanzo.ai/v1/mcp \
  -H "Authorization: Bearer $HANZO_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
       "jsonrpc": "2.0",
       "id": 1,
       "method": "tools/call",
       "params": {
         "name": "ml",
         "arguments": {
           "op": "get_ml_health",
           "input": {}
         }
       }
     }'

ML API · All Hanzo APIs · Interactive reference