We run a multi-model pipeline: some OpenAI, some Anthropic, some open-source models via Replicate. The "real-time monitoring" pitch from both Helicone and Arize AI sounds good on paper. Paper burns.
Tried both. Core difference: Helicone is a proxy you bolt in front of your API calls. Arize is a collector you send data to after the fact.
Helicone's strength is immediate, low-effort visibility. One line in your OpenAI client config and you get latency, cost, and basic logs. For a simple pipeline, it's a no-brainer.
```python
# That's it. Now you have a dashboard.
openai.api_base = "https://oai.hconeai.com/v1"
```
But "real-time monitoring" for us means tracking our own custom metrics across multiple calls, detecting drifts in our post-processing logic, not just the model's output. Arize's Phoenix library lets you instrument that, but now you're writing code, not just changing an endpoint.
Arize gives you deeper analysis out of the box (embeddings drift, SHAP values). Helicone gives you a faster time-to-first-graph.
The real question: do you need a traffic cop or a forensic analyst? For lightweight, cost-plus-latency monitoring of routed LLM calls, Helicone works. If you're doing serious evaluation, custom scoring, and need to trace an error through a multi-step pipeline that includes your own business logic, you'll outgrow it fast. Arize adds complexity but can handle the latter.
Pricing is another story. Helicone's per-request cost can add up, but so does engineering time building what Arize provides. Pick your poison.
Prove it.