The Lakera Alternative: Security Plus the Full Ops Stack
Lakera Guard is focused entirely on security. Fluiq covers the same prompt injection and PII detection, plus adds tracing, caching, and evals, all server-side with no extra round-trip API call per request.
Free tier · No credit card · 2-minute setup
Feature comparison
How Fluiq and Lakera Guard stack up across the features that matter in production.
Fluiq~ = partial support · - = not available
An honest take
We'll be straight. Here's where Lakera Guard genuinely excels, and where Fluiq goes further.
Where Lakera Guard shines
- Purpose-built security with a very high detection rate for prompt injection and jailbreak patterns, one of the most battle-tested models available.
- Dedicated, constantly updated threat model trained specifically on adversarial LLM attack patterns.
- Clear security-first focus makes it easy to reason about the scope of protection in compliance or audit contexts.
- Integrates easily into any HTTP-based LLM stack as a standalone guard step.
Where Fluiq pulls ahead
- No extra API round-trip: Fluiq's security scanning runs server-side as part of the trace processing pipeline, your latency profile doesn't change.
- Full observability included: trace every LLM call, map agent spans, track token counts, and attribute cost, security alone doesn't tell you what's happening inside your pipelines.
- Trace-driven caching: Fluiq learns which prompts repeat and serves them from Redis, Lakera has nothing equivalent.
- LLM-as-judge evals run inline in production, flagging hallucination, toxicity, and coherence issues alongside security events.
- One SDK, one dashboard: security events, traces, evals, and cost data are all in the same place, not spread across separate tools.
Switch from Lakera Guard in minutes
Remove the guard.detect() call. fluiq.secure(mode='block') intercepts requests server-side before they reach the LLM, no client-side API call needed.
Before, Lakera Guard
import lakera_guard
import openai
guard = lakera_guard.Guard(api_key="lak_...")
def run_safely(query: str) -> str:
# Extra API call before every LLM request
result = guard.detect({"messages": [
{"role": "user", "content": query}
]})
if result.flagged:
raise ValueError("Blocked by Lakera Guard")
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_...")
fluiq.secure(mode="block") # server-side guard, no extra round-trip
import openai
def run_safely(query: str) -> str:
# FluiqSecurityError raised automatically if attack detected
response = openai.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