In the news
Autoscaling endpoints for LLM inference
Together AI · Published · 3 min read
In 30 seconds
- What happened
- 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.
- Why it matters
- Engineers deploying language models need this when traffic is unpredictable and balancing between over-provisioning costs and under-provisioning latency degradation.
- Watch out
- 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.
- llm
- inference
The patterns behind this
Each one covers how the technique works, when it earns its cost, and where it breaks.
The Agent Architect
One pattern, one tradeoff, one production failure story. A short weekly briefing for people building agentic systems.
Weekly email, one-click unsubscribe. We only use your address to send the briefing.