What are the key considerations for building production-grade OpenAI applications?

Moving from a prototype to a production OpenAI application requires addressing several concerns that do not arise during development: reliability, cost control, observability, and safety.

# Production patterns:

# 1. Never use -latest model aliases in production
# BAD:
client.responses.create(model="gpt-5-latest", ...)  # behaviour changes without notice
# GOOD:
client.responses.create(model="gpt-5.5", ...)  # stable, predictable

# 2. Set both usage limits AND per-request caps:
client.responses.create(
    model="gpt-5.5",
    input=user_message,
    max_output_tokens=2048,   # prevent runaway outputs
    timeout=30.0,             # prevent hanging requests
)

# 3. Log everything for debugging:
import logging
logger = logging.getLogger("openai-app")

response = client.responses.create(model="gpt-5.5", input=prompt)
logger.info({
    "model": "gpt-5.5",
    "input_tokens": response.usage.input_tokens,
    "output_tokens": response.usage.output_tokens,
    "response_id": response.id,
})

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