Log LLM and Workflow Performance Metrics

Performance isn’t just about speed. It’s about ensuring your AI workflows consistently deliver results without slowdowns or hidden failures. By tracking how long your LLM takes to respond, how often it fails, and how many tasks it completes, you turn raw execution into actionable insights.

Why This Matters

Knowing what your LLM or automation workflow produces is important, but knowing how well and how quickly it performs can be just as critical in production.

Performance metrics give you visibility into:

• Latency – how long your LLM takes to respond.
• Throughput – how many workflows or prompts are processed over time.
• Error rate trends – spotting failing workflows before they snowball.

Without these metrics, it’s easy to miss bottlenecks or degraded performance until users complain.

What to Do

1. Identify Key Metrics: Track latency, throughput, error rates, and task completion times for both LLM queries and workflow executions.
2. Instrument Your Code: Add timers and counters in your application or automation workflows (e.g., N8N, OpenWebUI, Ollama).
3. Log Consistently: Store metrics in a central location such as Prometheus, InfluxDB, or a log aggregator like Graylog or Splunk.
4. Set Alerts: Configure thresholds for performance degradation so you’re notified before it impacts users.
5. Review Regularly: Analyze trends and optimize workflows or models based on observed performance data.

Production Tip

When logging prompt and response data, include timestamps, duration, and status codes. This lets you create performance dashboards or set up alerts when your AI slows down or starts failing.

Code Example

Here’s a Python snippet that logs latency and status for an LLM call inside a workflow:

import time
import logging
import json

# Configure logging to a file
logging.basicConfig(
    filename='metrics.log',
    level=logging.INFO,
    format='%(message)s'
)

def log_performance(prompt, response, duration, status):
    log_data = {
        "timestamp": time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime()),
        "prompt_length": len(prompt),
        "response_length": len(response),
        "duration_seconds": duration,
        "status": status
    }
    logging.info(json.dumps(log_data))

# Simulated LLM call
prompt = "Summarize this text about AI production readiness."
start_time = time.time()

response = call_llm_or_workflow(prompt)

end_time = time.time()
duration = end_time - start_time

log_performance(prompt, response, duration, status="success")

Example Output

If you configure an alert in Graylog for LLM error spikes, you might receive something like:

{
  "timestamp": "2025-08-12T20:21:33Z",
  "prompt_length": 52,
  "response_length": 38,
  "duration_seconds": 1.202,
  "status": "success"
}

Going Further

Once you have these metrics:

• Push them to Prometheus, Grafana, or DataDog for visualization.
• Set up alerts for latency spikes or sustained error rates.
• Compare metrics across different models or workflow designs to optimize performance.

Final Thought

You can’t improve what you can’t measure. Logging performance metrics early in your AI project gives you the data to make smarter scaling decisions and to spot issues before they impact end users. This makes your AI project more production ready.