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.
- 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.
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.contentPricing
What each one costs
Langfuse meters ingested units, so tracing volume drives the bill and evaluations are effectively free, because you supply the judge key and pay the model provider yourself. Fluiq meters evaluations, so tracing is free and unlimited, and the judge tokens are included unless you bring your own key. If you mostly want cheap high-volume tracing, Langfuse is hard to beat. If you want evaluation and security you do not have to assemble, that is the trade.
Free
$0
Unlimited traces, 100 evals and 1,000 security scans a month. Bring your own provider keys.
Starter
$29/mo
2,000 evals, 50k security scans, unlimited retention, multi-model judge jury.
Team
$149/mo
10,000 evals, 500k security scans, response caching, SSO.
Growth
$499/mo
50,000 evals, 2M security scans, priority support.
Hobby
$0
50,000 units a month, 30-day access, 2 users.
Core
$29/mo
100,000 units, then $8 per 100k. 90-day access.
Pro
$199/mo
Same 100,000 units included, 3-year access, unlimited users.
Enterprise
$2,499/mo
Same unit allowance, plus enterprise controls.
Langfuse pricing from their public pricing page, checked July 2026. Plans change; check theirs before deciding.
Ready to switch?
Free tier. No credit card. Full observability, security, and evals on your first LLM call.
Unlimited free traces · 100 evals & 1,000 security scans / month · 14-day retention