# How are you handling OpenAI SDK rate limits and unpredictable token costs in production?

Using the OpenAI SDK, integration can feel fast and clean thanks to intuitive APIs, strong docs, and reliable structured outputs, function calling, and streaming. At the same time, pay-per-token pricing can scale unpredictably for high-volume tasks, and TPM/RPM throttling often means building custom retry logic for 429 errors. Latency and data privacy constraints can also come up when sending context to external cloud servers. What patterns have worked best for you to manage rate limits, control costs, and reduce latency while keeping development speed high?

##### Post Metadata
- Posted at: 2 months ago
- Author title: CompTIA Security+ | Azure Security Engineer (AZ-500) | 4+ Yrs Exp in 24/7 SOC & IAM | Expert in LogRhythm, CrowdStrike, Qualys & Imperva WAF | SSL Lifecycle Management (Sectigo & Entrust) | ServiceNow SOAR
- Net upvotes: 2


## Comments
### Comment 1

For rate limits, we stopped relying solely on application code. When 429 hits, having 50 parallel FastAPI workers sleep and retry at the same time just re-saturates the limit immediately. Instead, we use the Azure service bus to throttle the actual throughput.

For monitoring, we built a custom LangChain callback handler.

##### Comment Metadata
- Posted at: about 2 months ago
- Author title: Software Engineer




## Related Product
[OpenAI SDK](https://www.g2.com/products/openai-sdk/reviews)

## Related Category
[AI SDK](https://www.g2.com/categories/ai-sdk)

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