Hot take: You don't need a paid tool for basic LLM logging

Hey everyone! I'm just starting to look into monitoring for our first LLM-powered feature at work. I've seen a lot of fancy, expensive platforms advertised, but my team lead said we should keep it simple to start.

For basic logging, couldn't we just write our own wrapper? Something like this for Python:

```python
import json
import logging
from datetime import datetime
from openai import OpenAI

client = OpenAI()

def logged_completion(**kwargs):
start_time = datetime.now()
response = client.chat.completions.create(**kwargs)
end_time = datetime.now()

log_entry = {
"timestamp": start_time.isoformat(),
"model": kwargs.get("model"),
"prompt": kwargs.get("messages"),
"response": response.choices[0].message.content,
"latency_ms": (end_time - start_time).total_seconds() * 1000,
"usage": dict(response.usage)
}
logging.info(json.dumps(log_entry))
return response

# Use it instead of the direct call
response = logged_completion(
model="gpt-4o-mini",
messages=[{"role": "user", "content": "Hello!"}]
)
```

Then we could pipe these JSON logs to our existing logging stack (like Loki or even CloudWatch). Would this cover the basics for debugging and cost tracking? What crucial things would this simple approach miss compared to the paid tools? Thanks in advance for helping a newbie out! 😊