FluiqvsLangfuse

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.

Get started free Read the docs

Free tier · No credit card · 2-minute setup

Zeromanual span calls, fully automatic

Managedcloud, no self-hosting required

Built-insecurity scanning on every trace

Feature comparison

How Fluiq and Langfuse stack up across the features that matter in production.

Feature

Fluiq

Langfuse

Auto-instrumentation (no manual spans)

requires @observe decorators

OpenAI, Anthropic, Gemini support

LangChain / LangGraph tracing

CrewAI / MCP / Google ADK tracing

Full agent span tree

Prompt injection & jailbreak blocking

PII detection & redaction

Trace-driven response caching

LLM-as-judge evals

Agentic evaluation (whole-run: tools, trajectory, coordination)

Multi-model judge jury with audit trail

Whole-trajectory dataset capture (tools, MCP, media)

~datasets store IO pairs, not full trajectories

Run-vs-run regression comparison on datasets

End-user feedback & team annotations

Transparent judge prompts (exact prompt & version stamped on every score)

judge templates are definable, but scores don't record the rendered prompt

CI/CD eval gates

Prompt management

Fully managed cloud (no self-hosting)

~self-host or paid cloud

Cost tracking per model

~ = 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.
Evaluation with receipts: every score shows the exact judge prompt that produced it (editable per org), dataset runs diff against each other for regression reports, and python -m fluiq.ci gates your builds.
One SDK covers the entire LLM ops stack: trace, secure, cache, and evaluate.

Migration guide

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.content

After, 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.content

Ready to switch?

Free tier. No credit card. Full observability, security, and evals on your first LLM call.

Start free

Unlimited free traces · 1,000 evals / month · 14-day retention