Batch Inference

Batch Inference processes large groups of inputs together rather than one at a time — exchanging latency for throughput and efficiency. Common batch workloads include nightly scoring of every customer for churn risk, classifying millions of new documents into taxonomies, generating embeddings for an entire content library, and translating large archives. Batch inference can run on cheaper hardware (since latency doesn't matter), use larger batch sizes for higher GPU utilization, and take advantage of off-peak compute pricing from cloud providers. OpenAI's Batch API offers 50% discount for 24-hour turnaround, and Anthropic's Message Batches API offers similar pricing. Self-hosted batch inference uses tools like vLLM's offline mode, ONNX Runtime, and Apache Spark integrated with ML frameworks. AI governance, AI compliance, and AI risk management programs apply the same controls to batch as to real-time inference — supporting responsible AI through consistent oversight regardless of execution mode in every enterprise AI deployment context.

Centralpoint Governs Batch and Real-Time Equally: Whether processing one chat or one million records, Centralpoint by Oxcyon governs each call. The model-agnostic platform supports OpenAI, Gemini, Llama, and embedded models, meters every token, keeps prompts and skills on-prem, and embeds chatbots into your portals via a single JavaScript line.


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