We hit exactly the tight-coupling problem you describe after switching from GPT-4 to Claude mid-project — the routing layer ended up being the single best investment in the stack. Curious how you handle latency differences between providers when load balancing across them.
That’s a great real-world example. Switching providers mid-project is exactly where the tight coupling starts to hurt.
For latency, the cleanest approach is usually latency-aware routing at the gateway layer. The gateway can track response times per provider/model and route traffic to the fastest healthy option, with fallback rules if performance degrades. Some teams also keep different routing profiles per workload (e.g., low-latency vs high-reasoning tasks).
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We hit exactly the tight-coupling problem you describe after switching from GPT-4 to Claude mid-project — the routing layer ended up being the single best investment in the stack. Curious how you handle latency differences between providers when load balancing across them.
That’s a great real-world example. Switching providers mid-project is exactly where the tight coupling starts to hurt.
For latency, the cleanest approach is usually latency-aware routing at the gateway layer. The gateway can track response times per provider/model and route traffic to the fastest healthy option, with fallback rules if performance degrades. Some teams also keep different routing profiles per workload (e.g., low-latency vs high-reasoning tasks).