Teams often start custom-model deployments with large architectures, full-precision weights, or older model versions carried over from training environments. When these models transition to Bedrock’s managed inference environment, the compute footprint (especially GPU class) becomes a major cost driver. Common inefficiencies include: * Deploying outdated custom models despite newer, more efficient variants being available, * Running full-size models for tasks that could be served by distilled or quantized versions, * Using accelerators overpowered for the workload’s latency requirements, or * Relying on default model artifacts instead of optimizing for inference. Because Bedrock Custom Models bill continuously for the backing compute, even small inefficiencies in model design or versioning translate into substantial ongoing cost.
Bedrock Custom Models incur hourly charges for the underlying dedicated compute (e.g., GPU accelerator instance types). Model size, architecture, and precision level directly influence resource requirements. Using an unnecessarily large or outdated model increases hourly cost without improving output.
