I've been running Helicone in production for monitoring our LLM calls for a year and a half now. Everyone loves to talk about dashboards and cost tracking, so I'll skip that. Let's talk about the raw, practical bits that actually matter when you're trying to debug a midnight outage.
First, the good: The proxy itself is solid. It's just a Go binary. It doesn't fall over. We've routed a stupid amount of tokens through it and the overhead is minimal, which is the main thing I needed. The caching feature is a genuine cost-saver. Implementing request deduplication for batch jobs cut our GPT-4 bill noticeably. The code is straightforward:
```javascript
// Just the header, does what it says.
"Helicone-Cache-Enabled": "true"
```
Now, the rough edges. Their support is... slow. Not non-existent, but you're not getting a quick answer. I opened a ticket about bucket-based cost allocation behaving weirdly with Azure OpenAI and it took five days for a real response. The documentation has gaps. You'll figure it out by reading the source or experimenting, but don't expect hand-holding.
My biggest gripe is with some of the newer "AI-powered" features. The prompt validation and scoring feels half-baked. We tried the 'anomaly detection' for a month. It either flagged obvious non-issues or missed real regressions. We went back to writing our own checks in the pipeline. It seems like they're chasing feature checkboxes instead of hardening the core.
**Summary after 18 months:**
* **Proxy & Core Features (Caching, Logging):** 8/10. Reliable and does the job.
* **Dashboard & Cost Tracking:** 7/10. Useful, but the tagging system could be more flexible.
* **Advanced Features (Anomaly, Scoring):** 4/10. Not production-ready for complex workloads.
* **Support & Docs:** 5/10. Functional, but be prepared to dig on your own.
It's a good cost and observability sink. But if you're thinking about it for their AI-native analysis tools, run your own proof-of-concept first. You might be disappointed.
-- bb
-- bb
You're absolutely right about the support pace. I had a similar ticket about log sampling for high-volume projects that sat for a week before a useful reply came through. It feels like they're stretched thin as they build out those newer AI features.
The "AI-powered" scoring bit you mentioned is my exact frustration too. It's clever in theory, but for production debugging, I need structured, queryable data - not a black-box score. I've completely ignored that panel and built my own validation checks off the raw request/response logs instead. It's more work upfront, but at least I understand the rules.
That said, the reliability of the core proxy is why we tolerate it. When everything else is on fire, at least the data is flowing. Have you found any workarounds for the Azure cost allocation issue, or are you just tracking that separately for now?
Measure twice, automate once.
Exactly. The proxy reliability is the main sell. Everything else feels like a side project.
> bucket-based cost allocation behaving weirdly with Azure OpenAI
Ran into this too. Their model cost mapping defaults are wrong for Azure's naming scheme. We had to manually override the cost per token in the settings. The docs don't mention it, but you can patch it via the API. Still, shouldn't be necessary.
Have you tried pulling logs directly from their Postgres warehouse? We gave up on their UI for debugging and just query the tables. Faster than waiting for their support.
I've taken the same approach with direct Postgres queries, but it comes with its own maintenance overhead. You're right that their table structure is stable, but we've had to build internal tooling to map their schema changes and handle time zone conversions for the timestamp fields, which aren't in UTC by default.
For the Azure cost mapping, the API patch works but isn't scalable across deployments. We wrote a small service that syncs our Azure model registry with Helicone's configuration weekly. It's brittle, though, and ties us more tightly to their API than I'd like.
The real issue is that querying the warehouse directly makes you realize how much aggregational logic is missing from their UI. You can get latency percentiles and error rates, but correlating model performance with specific user segments requires joining four separate views.
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