The Helicone Alternative: No Proxy, No Latency Tax
Helicone routes your traffic through a proxy to log requests. Fluiq instruments the SDK directly, zero latency overhead, full agent trace trees, security scanning, and evals for every framework you use.
Free tier · No credit card · 2-minute setup
Feature comparison
How Fluiq and Helicone stack up across the features that matter in production.
Fluiq~ = partial support · - = not available
An honest take
We'll be straight. Here's where Helicone genuinely excels, and where Fluiq goes further.
Where Helicone shines
- Extremely fast setup, change one base_url and you get immediate request logging with no SDK changes.
- Works with any HTTP client that targets the OpenAI-compatible API, including non-Python environments.
- Clean dashboard with good cost analytics and user session tracking.
- Prompt templates and experimentation UI are well-designed for product teams.
Where Fluiq pulls ahead
- No proxy, Fluiq patches the SDK at the import level. There's no extra network hop, no TLS termination latency, no single point of failure in your request path.
- Full agent trace trees: Fluiq traces multi-step LangGraph, CrewAI, and Google ADK pipelines end-to-end, not just individual OpenAI calls.
- Built-in security: prompt injection blocking, PII redaction, jailbreak detection, and secret leak scanning, all server-side, not accessible to attackers via the public SDK.
- LLM-as-judge evals run inline on every production call, flagging hallucination and toxicity automatically.
- Trace-driven caching learns from your actual traffic patterns, not a generic proxy cache.
Switch from Helicone in minutes
Remove the base_url override and Helicone-Auth header. Restore the standard OpenAI client and add two Fluiq lines at the top.
Before, Helicone (proxy setup)
from openai import OpenAI
# Must override base_url and inject auth header
client = OpenAI(
base_url="https://oai.hconeai.com/v1",
default_headers={
"Helicone-Auth": "Bearer hc_...",
},
)
response = client.chat.completions.create(
model="gpt-4o",
messages=[{"role": "user", "content": query}],
)After, Fluiq (SDK-based)
import fluiq
fluiq.instrument(api_key="fl_...")
# Standard client, no base_url override, no extra headers
from openai import OpenAI
client = OpenAI()
response = client.chat.completions.create(
model="gpt-4o",
messages=[{"role": "user", "content": query}],
)Ready to switch?
Free tier. No credit card. Full observability, security, and evals on your first LLM call.
50,000 free traces / month · 1,000 evals / month · 14-day retention