The Langfuse Alternative: Zero Manual Spans, Security Included
Langfuse is a solid open-source observability platform. Fluiq auto-instruments the same frameworks with zero manual span calls, adds server-side security scanning, and runs fully managed, no infra to maintain.
Free tier · No credit card · 2-minute setup
Feature comparison
How Fluiq and Langfuse stack up across the features that matter in production.
Fluiq~ = partial support · - = not available
An honest take
We'll be straight. Here's where Langfuse genuinely excels, and where Fluiq goes further.
Where Langfuse shines
- Open-source and self-hostable, full control over data residency and no vendor lock-in.
- Active community with frequent releases and strong GitHub presence.
- Good Python and JS/TS SDKs with broad framework coverage via decorators.
- Transparent pricing and generous self-hosted option for budget-conscious teams.
Where Fluiq pulls ahead
- Zero manual spans, Fluiq patches the SDK at import time, so all LLM calls are traced without decorators or context management.
- Security layer baked in: prompt injection detection, jailbreak scoring, PII redaction, and secret leak prevention run server-side on every call.
- Trace-driven Redis caching: Fluiq mines your trace history and serves repeated prompts from cache, cutting API costs without code changes.
- Fully managed, no Docker Compose, no Postgres to maintain, no infrastructure cost to carry.
- One SDK covers the entire LLM ops stack: trace, secure, cache, and evaluate.
Switch from Langfuse in minutes
Replace langfuse.openai imports and @observe decorators. Fluiq instruments the standard SDK, no context managers needed.
Before, Langfuse
from langfuse.openai import openai # patched client
from langfuse.decorators import observe, langfuse_context
@observe()
def run_pipeline(query: str):
langfuse_context.update_current_observation(
input=query,
model="gpt-4o",
)
response = openai.chat.completions.create(
model="gpt-4o",
messages=[{"role": "user", "content": query}],
)
return response.choices[0].message.contentAfter, Fluiq
import fluiq
fluiq.instrument(api_key="fl_...")
# Use the official OpenAI client, no wrapped imports, no decorators
from openai import OpenAI
client = OpenAI()
def run_pipeline(query: str):
response = client.chat.completions.create(
model="gpt-4o",
messages=[{"role": "user", "content": query}],
)
return response.choices[0].message.contentReady 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