Autoscaling endpoints for LLM inference

The Together AI platform offers a model inference solution that enables autoscaling of deployments based on metrics the inference engine comprehends, such as in-flight requests, time to first token (TTFT), GPU utilization, and token throughput. Users can configure replica bounds, choose appropriate metrics, and fine-tune scaling windows to optimize for cost and performance under varying traffic conditions. This approach is crucial for effectively handling peaky traffic while minimizing latency impacts. The platform's autoscaling mechanism involves a continuous feedback loop that adjusts replica counts based on observed metrics relative to targets, with timing windows that manage how quickly replicas are added or removed. Different autoscaling policies can be applied depending on specific deployment needs, such as concurrency-driven, SLO-driven, or efficiency-driven metrics, each with distinct implications for system behavior and costs. The document highlights the importance of selecting the right metric to avoid under-provisioning, which can lead to significant latency spikes, and over-provisioning, which incurs unnecessary expenses. It also emphasizes the need for an intuitive understanding of traffic patterns to set optimal scaling windows and provides guidance on refining autoscaling policies through empirical observations and adjustments.

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