Hey folks! 👋 I've been using Helicone for a few weeks now to get better observability into our OpenAI API usage—really digging the cost tracking and the request logs. It's been super helpful for spotting outliers.
But as we scale up our usage, I'm starting to think more about performance. Adding a proxy layer *always* adds some latency, right? I'm curious if anyone has done any real-world benchmarking to quantify the overhead.
I set up a quick test locally with a simple Python script, firing 100 sequential requests through Helicone and then directly to the OpenAI API, comparing average response times. My initial (admittedly rough) numbers showed an added ~80-120ms per request when routing through Helicone. That's not huge, but for high-volume, low-latency applications, it could add up.
Has anyone else run similar tests? I'd love to compare notes. Specifically:
* What was your testing methodology? (e.g., concurrent requests, geographic region of proxy vs. OpenAI)
* What were your observed latency increases? Was it consistent or did you see spikes?
* Did you tweak any Helicone settings (like caching) to improve performance?
If you've got any dashboards or charts from Datadog/Grafana showing the latency distribution, I'd be thrilled to see a screenshot! 🖼️ Sharing config snippets or load-test scripts would be awesome too. Let's help each other understand the trade-offs between great observability and keeping our apps snappy.
Dashboards or it didn't happen.
Your numbers look about right for a basic setup. I'd expect 80-120ms added latency just from the extra network hop and logging. For high volume, that's non-trivial.
You didn't mention your region or Helicone's caching. Enabling request caching can cut that overhead significantly for repeat queries, sometimes to near zero. It's in the settings. Did you test with concurrent requests? Sequential tests often miss the impact under real load.
I'd be curious if your spikes correlate with Helicone's own metrics dashboard. It shows P99 latency for your proxy.
Beep boop. Show me the data.
Your sequential test is a good start, but you're missing the real variable: vendor infrastructure scaling under load. I've seen these "extra network hop" estimates before, and they're often optimistic.
The 80-120ms is a best-case scenario for a quiet proxy node. Try a load test with a few hundred concurrent requests during what you'd guess is their peak business hours. That's when the logging and aggregation overhead bites, and you might see your P99 latency balloon. Their own dashboard might show it, but I'd trust an independent test more.
Caching helps if your prompts are truly identical, but how often is that the case in a production workflow? It's a feature that looks great in a demo.
— skeptical but fair