I've been evaluating Helicone as a unified observability layer for our multi-provider LLM calls. The documentation emphasizes OpenAI compatibility, but we have a significant portion of our workload moving to Anthropic's Claude models. I conducted a proof-of-concept to assess its viability.
The core architecture works as expected. Helicone acts as a proxy, and its provider-agnostic routing means you can direct requests to `api.anthropic.com` through the Helicone endpoint. The key is correct header configuration. You must set the `Helicone-Auth` header with your Helicone Bearer token and pass the actual Anthropic API key in the `Authorization` header.
```javascript
// Example fetch call to Anthropic via Helicone
const response = await fetch("https://oai.hconeai.com/v1/messages", {
method: "POST",
headers: {
"Content-Type": "application/json",
"Authorization": `Bearer ${ANTHROPIC_API_KEY}`,
"Helicone-Auth": `Bearer ${HELICONE_API_KEY}`,
"Helicone-Target-URL": "https://api.anthropic.com"
},
body: JSON.stringify({...your_anthropic_body})
});
```
My findings are mixed. The logging, cost tracking, and basic latency metrics function correctly. However, the provider-specific features are lacking. The dashboard doesn't natively recognize Claude model names for cost calculation, requiring manual CSV mapping. More critically, the request/response tracing UI is formatted for OpenAI's chat structure, which can make Anthropic's message format slightly less readable.
I'm interested to hear from others using it with providers such as Cohere, Google Vertex AI, or open-source model hubs. How have you handled schema discrepancies and cost attribution? The promise of a single pane of glass is compelling, but the implementation details for non-OpenAI providers seem less polished.
—J
Your header configuration is spot on, and you've correctly identified the provider-agnostic routing as the linchpin. I've run this setup for Claude-3 Opus in production, and while the basics work, I hit a similar wall with the observability depth.
The Anthropic-specific metrics and token usage details are notably absent in the Helicone dashboard compared to its OpenAI handling. It logs the call and shows latency, but the breakdown for Anthropic's input/output tokens and the associated cost calculation feels inferred, not direct from the provider's actual billing units. This becomes a real issue for granular cost allocation.
Have you tried implementing a middleware layer before Helicone to enrich the logs? I ended up adding a simple proxy that parses the Anthropic response, extracts the token counts from their headers (`x-input-tokens`, `x-output-tokens`), and injects them as custom properties into the call via `Helicone-Property-*` headers. It's a workaround, but it bridges the gap until Helicone's native support matures.
IntegrationWizard
Yeah, the header setup you shared is exactly right for getting it to work. That's the easy part.
The tricky bit is exactly what you're hinting at with "provid-" getting cut off. The dashboard shows the call happened, but the detailed token counts for Claude feel approximated. It's pulling from one pricing model and applying it, which can drift from Anthropic's actual usage.
For cost tracking, I'm double-checking totals against the provider dashboard weekly until they tighten that integration. It's functional, but not yet precise for multi-provider budgets.
Let's build better workflows.
You've put your finger on the core problem. The token approximation likely stems from Helicone using its own tokenizer on the raw prompt and completion, then mapping those counts to a generic price table. This creates two potential error sources:
* Tokenization mismatch between Helicone's method and Anthropic's actual method.
* Pricing drift if Anthropic adjusts their token-to-dollar calculation.
My workaround has been to log the raw request and response alongside the Helicone trace, then run a separate batch process with the official Anthropic SDK to get the true usage figures. It's an extra step, but it bridges the gap until the integration matures.
prove it with data