FluiqvsBraintrust

The Braintrust Alternative: Evals Plus the Full Ops Stack

Braintrust is a strong evaluation platform. Fluiq adds production tracing, security scanning, and response caching alongside evals, all from two lines of Python, with no manual scoring scaffolding.

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Free tier · No credit card · 2-minute setup

Inlineevals on every production LLM call

Autotracing, no @traced decorators needed

Built-insecurity on top of evals

Feature comparison

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

Feature

Fluiq

Braintrust

Production LLM tracing (auto)

eval-focused, limited tracing

2-line setup, no decorators

requires @traced or manual logging

13+ framework integrations

Full agent span trees

Per-node token & cost tracking

Prompt injection & jailbreak blocking

PII detection & redaction

Trace-driven response caching

LLM-as-judge evals

Eval warn / block modes (inline)

CI/CD eval gates

Dataset management

Prompt management

~ = partial support · - = not available

An honest take

We'll be straight. Here's where Braintrust genuinely excels, and where Fluiq goes further.

Where Braintrust shines

Best-in-class evaluation UX, Braintrust's playground and scoring interface are genuinely excellent for prompt iteration.
Strong human-in-the-loop annotation workflows with side-by-side diff views.
Dataset versioning and regression testing suite are mature and well thought-out.
Good CI integration with the ability to gate on eval thresholds.

Where Fluiq pulls ahead

Production-first: Fluiq auto-instruments every LLM call at the SDK level, you get full trace trees, latency histograms, and cost attribution without writing a single logging call.
Inline eval modes: fluiq.eval(mode='warn') flags low-scoring responses on the trace; mode='block' intercepts them before they reach users.
Security included: prompt injection blocking, PII redaction, jailbreak scoring, and secret leak prevention run on every production call, not just in eval scripts.
Response caching: trace-driven Redis caching serves repeated prompts instantly, cutting LLM spend without any code changes.
Two lines replace an entire boilerplate setup, no manual span context, no custom scorers to wire up.

Migration guide

Switch from Braintrust in minutes

Remove init_logger, @traced, and manual score calls. Fluiq runs LLM-as-judge automatically on every traced response.

Before, Braintrust

import braintrust
from braintrust import traced, init_logger

logger = init_logger(project="my-project", api_key="bt_...")

@traced
def run_pipeline(query: str) -> str:
    response = openai_client.chat.completions.create(
        model="gpt-4o",
        messages=[{"role": "user", "content": query}],
    )
    result = response.choices[0].message.content
    # manual scoring
    logger.log(scores={"quality": score_quality(result)})
    return result

After, Fluiq

import fluiq
fluiq.instrument(api_key="fl_...")
fluiq.eval(mode="warn")   # automatic LLM-as-judge on every call

# @trace for named agent spans (optional)
from fluiq import trace

@trace
def run_pipeline(query: str) -> str:
    response = openai_client.chat.completions.create(
        model="gpt-4o",
        messages=[{"role": "user", "content": query}],
    )
    return response.choices[0].message.content

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