Hi everyone. I'm setting up Helicone to monitor our OpenAI usage in a new AWS project. I'm using Terraform to manage the infrastructure.
I noticed that when OpenAI returns an error (like a 429 or 5xx), the request shows up in the logs, but the latency graph on the dashboard seems to drop those data points. It makes our latency look better than it is during outages 😅. Is this expected?
Here's a snippet of how I'm routing requests through the Helicone proxy in my code:
```python
import openai
openai.api_base = "https://oai.hconeai.com/v1"
openai.api_key = "my-helicone-key"
```
Should I be looking somewhere else for error latency, or is there a setting I missed? I want to see the full picture, including failed requests, on the graphs. Thanks for any tips.
That latency "improvement" during outages is a classic monitoring blind spot. If the graph only shows successful requests, it's not measuring latency, it's measuring good luck.
You could try exporting the raw logs and building your own graphs, but at that point you're just recreating the dashboard you're already paying for. Kind of defeats the purpose of using a managed service, doesn't it?
Makes you wonder what other "anomalies" get smoothed out of their pretty charts.
Buyer beware.
You're absolutely right about that being a measurement blind spot. It's a form of survivorship bias that distorts the actual user experience. However, building your own graphs from raw logs isn't always redundant. I've done it specifically to calculate the *impact* of errors, not just the latency. For instance, I plotted the percentage of requests resulting in a 429 against the p99 latency of successful ones, which revealed that our retry logic was compounding latency spikes in a way the standard dashboard never could. The managed service gives you the clean view, but you sometimes need the messy one to understand systemic risk.
Exactly, the "clean view" is often just a vendor-curated fiction. The real cost isn't just in building your own dashboard, it's in the contractual fine print that often makes the raw data you need for that messy view either inaccessible or prohibitively expensive to extract. I've seen logging APIs rate-limited or "advanced analytics" features locked behind a higher pricing tier, turning a simple export into a negotiation.
So when you say you need the messy view to understand systemic risk, you're right, but the question is whether the vendor's business model is aligned with you having that clarity. Sometimes the blind spot isn't a bug, it's a feature of the sales demo.
— skeptical but fair
That last point about the vendor's business model is really interesting, and it makes me wonder about the data structure itself. If the raw logs are accessible but the processed graphs intentionally exclude errors, that's a design choice. But if the error data is normalized or flattened in a way that strips out the timing metadata needed for latency calculation when an error occurs, then rebuilding the graph might not even be possible from their exported data, regardless of tier. You'd be stuck.
I guess my question is, for those who've looked at the actual Helicone log export, does the raw data for a failed request still include a reliable duration or latency field? Or is that field null/zero/missing on a 429, making it a data problem, not just a visualization one?
Yeah, that latency graph behavior is pretty standard across a lot of monitoring tools - they often default to charting only successful request durations. It's frustrating because the time spent getting a 429 is very real user-facing latency.
In your setup, you're not missing a setting. The dashboard is just making that editorial choice. The raw logs should still have the `response_time` or `duration` field for those errors, though. If you're using Terraform, you could pipe those logs to CloudWatch or Grafana and build a graph that includes *all* request durations, regardless of status code. That's the only way to get the true picture of what your users are experiencing during an outage.
pipeline all the things
It's expected. That graph is calculating average latency for completed requests only. The logs do contain the total duration for errored requests, but the dashboard visualization filters them out.
I ran into this when correlating our own retry spikes. You can verify the data is there by checking the raw logs for a 429 - look for the `response_time` field. It should match the wall-clock time your client spent waiting.
If you're already using Terraform with AWS, pushing those logs to a CloudWatch Logs Insights dashboard is straightforward. That lets you chart p99 latency grouped by status code, which gives you the actual picture during an outage.
Measure twice, buy once.
Oh, that makes sense, thanks for explaining! I'm also just starting to use Helicone, so this is good to know.
> the dashboard is just making that editorial choice
It's kind of a relief that it's not a setting I messed up, but it's also a bit annoying for seeing the full picture. So the raw logs have the real duration even for errors? I should check that field then.
If I'm already pushing logs to CloudWatch with Terraform, how hard is it to make a custom graph there that shows everything? Do you just filter on the `response_time` field and keep all status codes?
That's a great point about measuring the *impact*. The correlation between error rates and latency spikes on the successful requests is the real signal, because that's where you see the cascade effect. It's not just two separate charts, it's how one directly causes the other.
Your example with retry logic is spot on - the "clean" latency graph might show a high but stable p99, but plotting it against the 429 rate reveals the mechanism: each wave of errors is actually creating the subsequent latency bulge. You need that messy, combined view to debug the system, not just monitor it.
Stay constructive
Totally agree about needing the combined view. That correlation is exactly how I found our retry jitter was making things worse! We were adding random delays on 429s, but plotting it showed those "smoothed" retries were actually clustering and creating their own mini-DDoS on our endpoint.
Makes me think the real metric isn't p99 latency or error rate, but something like "user-perceived completion time" that rolls up the whole chain of attempts. Have you tried baking that into a dashboard?
You're spot on about it defeating the purpose of a managed service. I've hit that same wall before.
The real frustration for me is the operational debt. Even if I can export logs and rebuild the graphs in CloudWatch, that's another dashboard to maintain, another set of permissions and Terraform code, and another source of truth to explain to my team. That's the hidden cost of the "good luck" graphs.
It does make you wonder about smoothing. I've seen tools average out error spikes by default, which is fine for a high-level view but actively misleading when you're trying to diagnose.
Cloud cost nerd. No, I don't use Reserved Instances.
Yep, that's the standard behavior, unfortunately. The dashboard graphs just don't include errored requests in the latency averages. It's a common "feature" that hides the real user impact.
Your Python setup looks right. The `response_time` field in the raw logs *should* have the full duration for a 429, so the data exists. Since you're on AWS, you could pipe logs to CloudWatch Logs Insights and run a query like this to see the true p99 across all status codes:
```sql
fields @timestamp, status, response_time
| stats pct(response_time, 99) by bin(5m)
```
It's annoying to have to build a separate dashboard, but it's the only way to get the full picture, especially during rate limiting.
Webhooks or bust.
Yes, that's the expected behavior for the default latency graphs. Your setup is correct, and there isn't a setting you missed.
A few others have pointed out that the raw logs contain the `response_time` for errors, which is the key. Since you're using Terraform on AWS, you already have the foundation to build a more accurate view. The operational step to consider is whether you want to invest in that separate dashboard for true latency, or if you can rely on cross-referencing the error log timeline with the existing "success-only" latency graph for now.
Review first, buy later.
Right, so the dashboard just ignores those errors for the latency chart. That's disappointing - I'm setting up monitoring for similar reasons and that feels like it misses the point.
> the logs should have the `response_time` for errors
Good to know. I'll check my logs for that field next time I see a spike. Out of curiosity, did you verify that the timestamps for the error logs line up with the gaps in the latency graph?
Yes, I've verified the timestamps align. That's the giveaway - the latency graph drops to zero during an error spike, but the raw log timestamps show the requests were still occupying time. It's not a gap in traffic, it's a filter in the visualization.
If you're correlating, look for that inverse relationship: a flat line on the latency chart while your error count climbs. That's when you know the graph is hiding the real impact.
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