Lack of Customization and Optimization Capabilities

7/10 High

ChatGPT API does not support optimization for latency/throughput based on traffic patterns, advanced inference techniques (prefill-decode disaggregation, prefix caching, speculative decoding), long contexts, batch-processing, structured decoding, or fine-tuning with proprietary data. This prevents developers from gaining competitive advantages or tailoring the model to their specific workloads.

ChatGPTOpenAI API
Category
ecosystem
Workaround
hack
Stage
build
Freshness
persistent
Scope
single_lib
Upstream
wontfix
Recurring
Yes
Buyer Type
team

Sources

Collection History

Query: “What are the most common pain points with ChatGPT for developers in 2025?”4/8/2026

GPT models are built for general-purpose chat, not for your unique workload or latency requirements. Here's what you can't do with ChatGPT or the OpenAI API: Optimize for latency or throughput based on your real traffic patterns. Implement advanced inference techniques like prefill–decode disaggregation, prefix caching, or speculative decoding.

Created: 4/8/2026Updated: 4/8/2026