# AWS Demonstrates On-demand and Batch Document Pipelines > Source: > Published: 2026-06-11 20:56:05.063894+00:00 # AWS Demonstrates On-demand and Batch Document Pipelines According to an AWS blog post, Amazon demonstrates an intelligent document processing pipeline that combines both on-demand and batch inference options on **Amazon Bedrock**. The post describes an on-demand path using a FIFO queue and an AWS Lambda function to process single documents with low latency, and a batch path that groups requests into a single Amazon Bedrock job for asynchronous, cost-optimized processing. The blog also shows how prompts and prompt versions are managed via Amazon Bedrock Prompt Management so callers can specify prompt ID and version per document, enabling a single pipeline to handle multiple document formats including scanned PDFs and text files. The walkthrough is a practical how-to aimed at engineers building document extraction workflows on Bedrock. ### What happened According to an AWS blog post, Amazon published a how-to that demonstrates an intelligent document processing solution combining on-demand and batch inference on **Amazon Bedrock**. Per the post, the design supports dynamically specifying LLM model ID, prompt ID/version, and system prompt ID/version at the document level, with prompt text retrieved from **Amazon Bedrock Prompt Management**. The post describes an on-demand pipeline that uses a FIFO queue and an AWS Lambda function to process single documents with low latency. It also describes a batch inference pipeline that submits multiple document requests in a single **Amazon Bedrock** job, where model invocations are processed asynchronously for cost optimization. ### Editorial analysis - technical context Combining on-demand FIFO-driven processing with batch jobs is a common pattern for intelligent document processing because it separates latency-sensitive work from high-throughput, cost-sensitive workloads. Prompt management per-document reduces the need for separate pipelines per document type, but it increases requirements for consistent prompt versioning, schema extraction, and post-processing normalization. For production systems, integrations with OCR and robust error handling are typically necessary to manage scanned-PDF variability. ### Industry context For practitioners, this AWS example is a practical LLMOps pattern: dynamic model and prompt selection, a queue-driven low-latency path, and an asynchronous bulk path. Organizations standardizing on cloud-hosted model platforms will frequently face the same tradeoffs between per-request latency and per-item cost when processing large backlogs of heterogeneous documents. ### What to watch Observe how teams integrate OCR and data normalization before Bedrock invocations, how they implement prompt version governance, and metrics used to decide when to route a document to on-demand versus batch processing. AWS has not published customer performance numbers or cost benchmarks in the post. ## Scoring Rationale This is a practical AWS how-to that codifies a useful LLMOps pattern for document extraction on Bedrock. It is directly useful to engineers but does not introduce new model capabilities or benchmarks. Practice with real Retail & eCommerce data 90 SQL & Python problems · 15 industry datasets 250 free problems · No credit card [See all Retail & eCommerce problems](/problems/datasets/retail)