Autoscaling endpoints for LLM inference

Together AI · 4 weeks ago · 4 ● 2 sources

Together AI's Dedicated Model Inference platform allows users to autoscale LLM deployments on inference-native metrics like in-flight requests, time-to-first-token, and GPU utilization, with configurable scaling windows. Cold starts take 86 seconds to 4 minutes depending on model size and conditions, making early scaling decisions critical since new replicas cannot respond to traffic spikes faster than they start up. Choosing the right metric—concurrency-driven for robustness, latency-driven for SLO compliance, or efficiency-driven for cost optimization—determines whether deployments balance user-facing latency against infrastructure expenses under variable traffic.