Autoscaling endpoints for LLM inference
Together AI ● Covered by 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.
Why it matters
GPU utilization can read healthy while your queue backs up, and a new replica takes minutes to warm. Here's how to pick autoscaling metrics, tune scale-up/down windows, and budget for cold starts on dedicated inference.