The fragmented stack
A typical inference environment combines Kubernetes, model-serving engines, GPU schedulers, autoscalers, routing logic, metrics, logs, and cost monitoring. Teams must keep those systems aligned while application traffic changes.
The GPU constraint
GPU model, memory, model weights, KV cache, batching, precision, and concurrency all influence workload placement. General-purpose orchestration alone does not express the full operational requirements of inference.
A coherent control plane
InferGrid is designed to unify deployment, GPU-aware scheduling, autoscaling, routing, observability, and cost visibility. The focus is the lifecycle after a model is ready to serve real application traffic.
Based on InferGrid’s company and product context.