Ce qui s'est passé: Autoscaling endpoints for LLM inference

Together AI released autoscaling for LLM inference endpoints using inference-native metrics like in-flight requests, TTFT, and GPU utilization instead of CPU-style signals.

Pourquoi ça compte: Autoscaling endpoints for LLM inference

Engineers deploying language models need this when traffic is unpredictable and balancing between over-provisioning costs and under-provisioning latency degradation.

Vigilance: Autoscaling endpoints for LLM inference

Cold starts take minutes, so autoscaling cannot react to sudden spikes. Scale-to-zero requires explicit restart. Metric choice critically affects behavior under peaky traffic.

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Autoscaling endpoints for LLM inference

Together AI · Publié le 31 juillet 2026 · 3 min de lecture

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Ce qui s'est passé: Together AI released autoscaling for LLM inference endpoints using inference-native metrics like in-flight requests, TTFT, and GPU utilization instead of CPU-style signals.
Pourquoi ça compte: Engineers deploying language models need this when traffic is unpredictable and balancing between over-provisioning costs and under-provisioning latency degradation.
Vigilance: Cold starts take minutes, so autoscaling cannot react to sudden spikes. Scale-to-zero requires explicit restart. Metric choice critically affects behavior under peaky traffic.

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