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

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

~agent evals without multi-model jury or DAG-aware layers

Multi-model judge jury with audit trail

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

~datasets are IO-centric; no pinned tool/MCP trajectories

Eval warn / block modes (inline)

Run-vs-run regression comparison on datasets

End-user feedback & team annotations

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

~code scorers are inspectable; LLM-judge scores don't carry the rendered prompt

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.
Transparent judging: every score records the exact judge prompt and version behind it, and each org can edit those prompts — then diff any two dataset runs to see what regressed, and gate CI with python -m fluiq.ci.
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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Unlimited free traces · 1,000 evals / month · 14-day retention