Use real-time inference for every request to achieve very low latency is the correct choice because the scenario requires a prediction for each API call within a tight sub-120 millisecond budget at very high volume.

With real-time inference you get immediate per-request responses and the ability to provision or autoscale serving capacity to meet strict latency SLAs. Real-time endpoints avoid queuing and are designed to serve predictions as requests arrive so they align with sub-120 ms targets under sustained load.

Use batch inference with offline analytics to anticipate user actions ahead of time is not suitable because batch jobs run on schedules and optimize throughput rather than delivering immediate responses to individual requests.

Use SageMaker Asynchronous Inference to queue requests and handle them as capacity becomes available is intended for long running or spiky tasks and introduces queuing delays so it cannot guarantee the low per-request latency required here.

Use a hybrid approach that processes most requests in scheduled batches and a small portion in real time is also a poor fit because routing a large share of traffic to batch processing leaves many requests with unacceptable delay and fails to ensure latency for every call.

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