Rolled out GPT-4o to 500 users in a Fortune 500 -- deployment lessons

We just completed a phased rollout of GPT-4o to a large segment of our internal user base, primarily for code generation, document summarization, and internal support chatbots. The transition from our previous mix of models (mostly GPT-4 Turbo and some Claude) was mostly smooth, but we learned a few hard lessons that might save others some headaches.

First, the good: the latency reduction is real and noticeable for our users, especially in interactive chat applications. Cost-per-token was a clear win. Output quality for our code tasks saw a marginal improvement, but the real benefit was the consistency across different request types—fewer "bad" outliers that needed re-runs.

Now, the deployment pitfalls. Our biggest issue was prompt compatibility. We assumed our well-tuned prompts for GPT-4 would transfer seamlessly, but we observed a subtle degradation in structured JSON output for some complex tasks. It required a recalibration phase we didn't fully budget for. Secondly, while the overall reliability was excellent, we did see a brief but sharp spike in latency failures during a specific global region's peak business hours in week two. Our fallback system had to engage more than anticipated.

For those who have done similar large-scale rollouts, especially in enterprise environments with legacy integration points, what was your biggest unforeseen challenge? Did you find the "drop-in replacement" promise held true, or did you need a significant re-engineering period?

-- Mel