AI/Batch Inference/Concept/AWS vs OpenAI vs LangChain

• AWS Bedrock vs OpenAI vs LangChain - Batch Inference Conceptual Overview

  • Overview

    • Batch inference submits many prompts or embedding requests as one asynchronous job, trading higher latency for lower cost and greater throughput.

  • Context

    • Large-scale workloads quickly hit per-minute rate limits and incur repeated network overhead when sent synchronously. Server-side batch jobs aggregate work, ease coordination, and unlock discounted pricing tiers.

  • Key Principles

    • Control-plane job APIs separate submission from execution.
    • External object storage stages inputs and outputs.
    • Discounted pricing incentivises deferred processing.

  • Mechanism

    • Bedrock

      • CreateModelInvocationJob posts a JSON body that points to S3 URIs for input and output. Progress is polled with GetModelInvocationJob; listings and cancellation use ListModelInvocationJobs and StopModelInvocationJob. See the Bedrock batch job guide.
      • Not available for provisioned-throughput endpoints; supports models such as Claude 3, Llama 3, Titan, and Nova.

    • OpenAI

      • Upload data with POST /v1/files, then launch POST /v1/batches and inspect with GET /v1/batches/{id}. Full reference in the OpenAI Batch API docs.
      • Fixed 24-hour completion_window; supports chat, completion, embedding, and vision endpoints.

    • LangChain

      • Runnable.batch() / .abatch() simply fans out individual provider calls concurrently; no provider batch job is invoked. See the LangChain batching section.

    • Comparative Flow

      • mermaid

        graph TD
          A[Client] -->|Upload JSONL| B(S3 / OpenAI Files)
          A -->|Create Job| C(Bedrock Job API)
          A -->|Create Job| D(OpenAI /v1/batches)
          C -->|Write output| B
          D -->|Write output| B

  • Examples

    • Bedrock CLI

      • bash

        aws bedrock create-model-invocation-job \
          --model-id anthropic.claude-3-sonnet-20240229-v1:0 \
          --input-data-config file://input.jsonl \
          --output-data-config s3://my-bucket/results/ \
          --role-arn arn:aws:iam::123456789012:role/BedrockBatchRole

    • OpenAI Python

      • python

        from openai import OpenAI
        client = OpenAI()
        
        file = client.files.create(
            file=open("input.jsonl", "rb"),
            purpose="batch"
        )
        
        batch = client.batches.create(
            input_file_id=file.id,
            endpoint="/v1/chat/completions",
            completion_window="24h"
        )
        print(batch.status)  # "queued"

  • Misconceptions

    • LangChain .batch() triggers provider batch jobs → False. It is client-side concurrency only.
    • Bedrock batch supports provisioned-throughput endpoints → False. Those endpoints cannot be batched.
    • Batch mode streams tokens → False. Bedrock and OpenAI both return file-based results; no token streaming within batch jobs.

  • Related

    • Bedrock InvokeModel · OpenAI Files API