Batch Prompts in LangChain with ChatBedrockConverse to Save on LLM CostsLangChain with ChatBedrockConverseBatch Prompts in LangChain with ChatBedrockConverse to Save on LLM Costs

Are you building an AI-powered app using LangChain and Amazon Bedrock? If you’re struggling with high LLM costs and slow response times, there’s a smart solution: use the ChatBedrockConverse class with the abatch function in LangChain.

In this guide, you’ll learn how to implement ChatBedrockConverse with abatch, reduce costs, speed up large language model (LLM) responses, and optimize your AI workflows for scale and performance.

Table of Contents

What Is `ChatBedrockConverse` in LangChain?

ChatBedrockConverse is a LangChain integration that allows developers to use conversational AI models available on Amazon Bedrock (like Claude, Titan, and Llama 3) through a simplified interface.

It abstracts the complexity of interacting with Bedrock’s Converse API, making it easier to build chat-based applications, LLM pipelines, and automated workflows using foundation models.

What Does the `abatch` Function Do?

The abatch method lets you send multiple prompts in parallel—batching them into a single API request. This leads to:

Reduced LLM cost per API call
Faster processing of multiple prompts
Optimized token usage
Improved performance in high-volume AI pipelines

You can view the full pricing information for normal calling and batch processing here

How to Use ChatBedrockConverse with `abatch`

Step 1: Install LangChain AWS Package

pip install -U langchain-aws

Step 2: Configure Your AWS Credentials

Make sure your AWS credentials are properly set up using either environment variables or an AWS credentials file.

Step 3: Initialize `ChatBedrockConverse`

from langchain_aws import ChatBedrockConverse

llm = ChatBedrockConverse(
    model="us.anthropic.claude-3-5-haiku-20241022-v1:0",
    region_name="us-east-1"
)

Here I have used Anthropic’s Claude haiku 3.5 model but you can change the model name to match any other Amazon Bedrock-supported LLMs, such as Titan or Llama.

Step 4: Create a List of Prompts

prompts = [
    "What is the future of AI in healthcare?",
    "Explain large language models in simple terms.",
    "How does climate change affect global food supply?"
]

Step 5: Use the `abatch` Function

import asyncio

async def process_batch():
    results = await llm.abatch(prompts)
    for result in results:
        print(result.content)

asyncio.run(process_batch())

And it will print the output of all the batches at the same time!

Best Practices to Optimize LLM Cost and Performance

1. Choose the Right Batch Size

A batch size of 4–8 usually offers the best balance between performance and model output quality.

2. Keep Prompts Uniform

Send similar types of questions together in a batch to avoid context drift.

3. Implement Error Handling

Use try/except logic to handle timeout or API failures without disrupting the entire batch process.

Also Read: AWS Bedrock Agents: Deep Dive into Action Groups and Their Role in AI Workflows

What Is ChatBedrockConverse in LangChain?

What Does the abatch Function Do?

How to Use ChatBedrockConverse with abatch