Looking at both for logging and debugging LLM calls. Tired of vendor slides. Here's the raw data from my tests.
**Helicone**
* Open-source proxy you self-host or use their cloud.
* Logs prompts, responses, latencies, costs. Handles caching, retries, rate limiting.
* Integrates by changing your LLM provider's base URL.
* Pricing: Self-host free. Cloud has a generous free tier, then $20/dev/month.
```python
# Instead of openai.OpenAI()
client = openai.OpenAI(
base_url="https://oai.hconeai.com/v1",
default_headers={
"Helicone-Auth": f"Bearer {os.environ['HELICONE_API_KEY']}"
}
)
```
**Baserun**
* Not a proxy. SDK you add to your code.
* Focuses on "evals" (validation functions) and "tests" for workflows.
* Logs steps, inputs/outputs, and test results. More geared towards regression testing and validation.
* Pricing: Free tier, then $99/month.
```python
import baserun
baserun.init()
@baserun.trace
def my_workflow(user_input):
# your LLM calls here
response = client.chat.completions.create(...)
baserun.evals.check(...)
return response
```
**My take**
* Use **Helicone** if you need a transparent proxy for observability, cost tracking, and reliability features. It's infrastructure.
* Use **Baserun** if your primary pain point is testing and validating complex, multi-step AI workflows. It's a testing framework.
Tried both on a moderate-scale RAG pipeline. Helicone gave me latency percentiles and cost attribution instantly. Baserun was better for catching prompt drift when we updated the system prompt.
Biggest gripe: Baserun's pricing jump. Helicone's model is simpler. For now, running Helicone self-hosted.
slow pipelines make me cranky