OpenAI Monitoring & Cost Tracking
Two lines of Python give every OpenAI call, chat completions, streaming, embeddings, images, audio, a full trace with token counts, USD cost at OpenAI rates, and security scanning. No wrappers, no decorators.
Free tier · No credit card · 2-minute setup
What you get with Fluiq for OpenAI
Full span tree
Every chat completion, embedding, and image call becomes a structured span with input/output tokens, model version, latency, and USD cost at OpenAI's published rates.
Response caching
Fluiq mines your trace history to find repeated prompts and serves them from Redis, cutting both latency and API spend on high-repetition workloads.
Security scanning
Every prompt passes through Fluiq's server-side guard for prompt injection, jailbreak patterns, PII (SSNs, cards, emails), and secret string detection, before the response is returned.
Add Fluiq to your OpenAI app in 2 lines
import fluiq
fluiq.instrument(api_key="fl_...") # patches the openai module at import time
from openai import OpenAI
client = OpenAI()
# All of these are traced automatically, no code changes:
response = client.chat.completions.create(
model="gpt-4o",
messages=[{"role": "user", "content": "Hello"}],
)
embedding = client.embeddings.create(
model="text-embedding-3-small", input="Hello world"
)What Fluiq instruments in OpenAI
Every call to these methods is automatically traced, no decorators, no wrappers, no manual spans.
openai.chat.completions.create()openai.chat.completions.acreate()openai.responses.create()openai.beta.chat.completions.parse()openai.embeddings.create()openai.images.generate()openai.audio.transcriptions.create()openai.audio.speech.create()Start tracing OpenAI in 2 minutes
Free tier. No credit card. Full traces, security scanning, and evals on your first OpenAI call.
50,000 free traces / month · 1,000 evals / month · 14-day retention