How do I monitor latency and errors for a model API?

To monitor latency and errors for a model API, track metrics at the request level and make them visible in logs, dashboards, and alerts.

1) Measure the right latency metrics

Collect these for every request:

• End-to-end latency: total time from request received to response returned
• Model inference latency: time spent inside the model call
• Queue/wait time: time waiting before execution
• Time to first token and tokens/sec if it’s a streaming LLM API
• P50 / P95 / P99 latency: percentiles are more useful than averages

2) Track errors by type

Record:

• HTTP status codes: 4xx, 5xx
• Timeouts
• Rate limit errors
• Validation/input errors
• Upstream/model errors
• Retries and retry exhaustion
• Cancelled requests

Also keep error rate = errors / total requests over time.

3) Add structured logging

Log each request with fields like:

• request_id / trace_id
• timestamp
• model name/version
• latency_ms
• status code
• error type/message
• input/output token counts
• user/org identifier if relevant
• retry count

Use JSON logs so they’re easy to search and aggregate.

4) Export metrics to a monitoring system

Common options:

• Prometheus + Grafana
• Datadog
• New Relic
• CloudWatch / Stackdriver / Azure Monitor

Recommended metrics:

• request_latency_ms histogram
• request_count
• error_count
• timeout_count
• rate_limit_count
• retry_count

5) Use distributed tracing

If your API calls other services, use OpenTelemetry to trace:

• client → gateway → app → model provider → database/cache This helps identify where latency is introduced.

6) Set alerts

Alert on:

• P95 latency above threshold
• error rate above threshold
• timeout spikes
• sudden drop in throughput
• provider-specific failures
• retries increasing unusually

Example thresholds:

• P95 latency > 2s for 5 minutes
• error rate > 2% for 10 minutes
• timeout rate > 1% for 5 minutes

7) Monitor model-specific signals

For model APIs, also watch:

• token usage
• context length
• truncation rate
• streaming disconnects
• safety/filter rejections
• cache hit rate
• cold starts if using serverless/hosted inference

8) Create a simple dashboard

A good dashboard includes:

• Requests per minute
• Success/error rate
• Latency percentiles
• Top error types
• Latency by model/version
• Latency by region
• Token usage trends

9) Sample implementation pattern

In code, wrap the API call with timing and error capture:

import time
import logging

logger = logging.getLogger(__name__)

def call_model():
    start = time.time()
    try:
        result = model_client.generate(...)
        latency_ms = (time.time() - start) * 1000
        logger.info("model_request", extra={
            "status": "success",
            "latency_ms": latency_ms,
            "model": "my-model",
        })
        return result
    except Exception as e:
        latency_ms = (time.time() - start) * 1000
        logger.exception("model_request_failed", extra={
            "status": "error",
            "latency_ms": latency_ms,
            "model": "my-model",
            "error_type": type(e).__name__,
        })
        raise

10) Practical checklist

• Add request timing
• Log all failures with error type
• Emit metrics histograms/counters
• Add tracing with request IDs
• Build dashboards for latency and error rate
• Set alerts
• Track model/version/token metrics

If you want, I can also give you:

1. a Prometheus/OpenTelemetry example,
2. a Python/FastAPI middleware, or
3. a Datadog dashboard setup.