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POST
/
guardian
/
monitors
/
{monitor_id}
/
inferences
curl -X POST https://api.rotavision.com/v1/guardian/monitors/mon_abc123/inferences \
  -H "Authorization: Bearer rv_live_..." \
  -H "Content-Type: application/json" \
  -d '{
    "input_data": {
      "user_id": "u123",
      "category": "electronics",
      "price_range": "mid",
      "session_duration": 245
    },
    "prediction": {
      "product_ids": ["p456", "p789", "p012"],
      "scores": [0.92, 0.87, 0.81]
    },
    "latency_ms": 45,
    "metadata": {
      "user_segment": "premium",
      "region": "north",
      "platform": "mobile"
    }
  }'

{
  "id": "inf_xyz789",
  "monitor_id": "mon_abc123",
  "logged_at": "2026-02-01T10:30:00Z"
}

​
Request

​
monitor_id
string
required
The monitor ID to log to.
​
input_data
object
Input features for the inference. Used for data drift detection.
​
prediction
any
required
The model’s prediction output.
​
actual
any
Ground truth label (if available). Used for accuracy monitoring.
​
latency_ms
number
Inference latency in milliseconds.
​
error
object
Error details if the inference failed.
​
metadata
object
Additional metadata for segmentation and analysis.
​
timestamp
string
ISO 8601 timestamp. Defaults to current time.
curl -X POST https://api.rotavision.com/v1/guardian/monitors/mon_abc123/inferences \
  -H "Authorization: Bearer rv_live_..." \
  -H "Content-Type: application/json" \
  -d '{
    "input_data": {
      "user_id": "u123",
      "category": "electronics",
      "price_range": "mid",
      "session_duration": 245
    },
    "prediction": {
      "product_ids": ["p456", "p789", "p012"],
      "scores": [0.92, 0.87, 0.81]
    },
    "latency_ms": 45,
    "metadata": {
      "user_segment": "premium",
      "region": "north",
      "platform": "mobile"
    }
  }'

{
  "id": "inf_xyz789",
  "monitor_id": "mon_abc123",
  "logged_at": "2026-02-01T10:30:00Z"
}

​
Batch Logging

For high-throughput scenarios, use the batch endpoint: 
curl -X POST https://api.rotavision.com/v1/guardian/monitors/mon_abc123/inferences/batch \
  -H "Authorization: Bearer rv_live_..." \
  -H "Content-Type: application/json" \
  -d '{
    "inferences": [
      {
        "input_data": {...},
        "prediction": {...},
        "latency_ms": 45,
        "timestamp": "2026-02-01T10:30:00Z"
      },
      {
        "input_data": {...},
        "prediction": {...},
        "latency_ms": 52,
        "timestamp": "2026-02-01T10:30:01Z"
      }
    ]
  }'
Batch endpoint accepts up to 1,000 inferences per request. For very high throughput, consider using async logging with a queue.

​
Async Logging

For minimal latency impact on your serving path: 
from rotavision import Rotavision
from rotavision.logging import AsyncLogger

# Create async logger (uses background thread)
logger = AsyncLogger(
    api_key="rv_live_...",
    monitor_id="mon_abc123",
    batch_size=100,
    flush_interval_ms=1000
)

# In your serving code - returns immediately
logger.log(
    input_data=features,
    prediction=prediction,
    latency_ms=latency
)

# Ensure flushing on shutdown
logger.flush()