I've been setting up Helicone for a few teams now to get a handle on LLM costs and performance. The default dashboards are a great start, but for any real incident response or user-specific debugging, you need to slice the data by who's actually making the calls. Tagging requests by internal user ID is the way.
Here’s the pattern I’ve settled on for the on-call crew. You’ll be passing a custom property through the Helicone headers. The key is to do it consistently across all your integration methods.
**If you're using the OpenAI SDK with the Helicone wrapper, it looks like this:**
```python
from helicone.openai_proxy import openai
openai.proxy_api_key = "your_helicone_key"
openai.headers = {
"Helicone-Property-UserID": "internal_user_12345"
}
# Your normal chat.completions.create call then inherits this
response = openai.ChatCompletion.create(...)
```
**For direct HTTP requests,** just add the header manually:
```
Helicone-Property-UserID: internal_user_12345
```
Once this is flowing, the real power comes from Grafana. You can build a dashboard that breaks down latency, cost, and error rate by that UserID tag. It turns a vague "the API is slow" alert into "user ID 12345 is experiencing high latency on streamed completions." Makes triage a lot faster.
A couple of pitfalls I’ve seen:
* Make sure your user ID is a consistent format (UUID, internal system ID). Don’t use PII like email.
* The property will be available in Helicone's logs and can be filtered on in their web UI, but for dynamic alerting, you’ll want to export to your own Prometheus/Grafana stack.
* Remember to add this header at the very start of your request chain, ideally in your application's middleware or core API client.
Has anyone else set up user-based segmentation? Curious if you’re using it for rate limiting insights or for correlating LLM errors with specific user actions.
zzz
Sleep is for the weak
Your header approach is spot on for basic tagging. Where this gets interesting is when you need that user ID to persist through a full distributed trace. If your LLM call is part of a broader workflow, you'll want that Helicone-Property-UserID to be derived from your root span's attributes in your primary APM, like Datadog or OpenTelemetry.
I've seen teams add a middleware that extracts the user context from the incoming request trace and automatically appends it to all downstream Helicone headers. Otherwise, you're duplicating the logic in every service that touches an LLM. The correlation lets you jump from a slow user request in Grafana directly to the specific trace showing the slow model call in your APM tool.
Have you considered setting up a derived field in your metrics pipeline to join this LLM cost data back to your core application performance metrics?
Your header pattern is fine for a small team, but how does it hold up at ten thousand RPS? I've seen that global `openai.headers` approach cause bleed between requests when async workers aren't properly isolated.
Also, are you scrubbing those IDs before they hit your metrics? "internal_user_12345" looks a lot like a production database key. If that's the case, you're now logging PII in your monitoring tool, which creates a whole new compliance headache your Grafana dashboard won't solve.
Thanks for the example, that's really clear. A quick question, though - you mentioned the wrapper is for the "on-call crew." Is this method also meant for tagging *every* request in production, or is it more for debugging specific sessions? I'm trying to figure out the best way to make sure all calls get tagged without adding too much overhead.
Ask me in a year
That header pattern is a solid operational baseline for correlation. The critical step you're glossing over is establishing a canonical source for the user ID itself. Relying on each service team to format and pass a correct ID leads to fragmentation - you'll see "user_12345", "u12345", and "12345" in your dashboards, which breaks grouping.
You need a central library or service context object that enforces the ID format. This ensures the tag is consistent whether the call originates from a backend job, a webhook, or an async worker, making your Grafana breakdowns actually reliable.
Another layer of complexity to maintain and eventually break. That global header dict will get stomped on by the next engineer who doesn't know the magic convention. Seen it happen.
Just add the tag in your existing APM span attributes. If you're already tracing, you already have the user context somewhere. Now you have to keep two systems in sync. Why?
And Grafana breakdowns are nice until you have ten thousand unique IDs. Then your queries timeout.
If it ain't broke, don't 'upgrade' it.
You're right about the global dict getting stomped on, it's a real risk in a shared codebase. I've switched to using a request-scoped context or a thread-local for that exact reason.
But I disagree about just using APM span attributes. If your primary goal is cost analysis per user in Helicone/Grafana, you need the tag *in* the LLM call's metadata. Relying on a separate trace correlation adds another query layer and often fails if teams use different sampling rates.
For the ten thousand unique IDs point, that's more about your dashboard strategy. You shouldn't be plotting ten thousand lines. You aggregate by top-N users by cost or latency, and maybe log the full list to object storage for the rare forensic dive.
Benchmarking my way to better decisions