AWS's Native LLM Strategy

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LLM benchmarks, prompt techniques, and token-cost breakdowns — not another AI news roundup.

Amazon Nova is Amazon's in-house family of foundation models, available exclusively through Amazon Bedrock. For teams already running workloads on AWS, Nova offers tight integration with IAM, VPC, CloudWatch, and S3 - without routing traffic through a third-party API.

The three-tier lineup covers different cost/capability tradeoffs:

Model	Context	Best For	Price (Input)
Nova Micro	128k	Fast text tasks	$0.035/1M
Nova Lite	300k	Multimodal, balanced	$0.060/1M
Nova Pro	300k	Complex reasoning + multimodal	$0.800/1M

Nova Micro is one of the cheapest commercially available LLMs at $0.035/1M input tokens.

Multimodal Input on Nova Lite and Pro

Nova Lite and Pro accept images, video clips, and documents alongside text:

python

import boto3
import json
import base64

bedrock = boto3.client("bedrock-runtime", region_name="us-east-1")

with open("diagram.png", "rb") as f:
    image_data = base64.b64encode(f.read()).decode()

response = bedrock.invoke_model(
    modelId="amazon.nova-pro-v1:0",
    body=json.dumps({
        "messages": [
            {
                "role": "user",
                "content": [
                    {
                        "image": {
                            "format": "png",
                            "source": {"bytes": image_data}
                        }
                    },
                    {"text": "Describe the architecture shown in this diagram."}
                ]
            }
        ],
        "inferenceConfig": {"max_new_tokens": 1024}
    })
)

result = json.loads(response["body"].read())
print(result["output"]["message"]["content"][0]["text"])

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Bedrock Converse API

The Converse API provides a unified interface across all Bedrock models - Nova, Claude, Llama, Mistral, and others - with the same request/response format:

python

response = bedrock.converse(
    modelId="amazon.nova-pro-v1:0",
    messages=[
        {"role": "user", "content": [{"text": "Summarize the key risks in this contract."}]}
    ],
    inferenceConfig={"maxTokens": 2048, "temperature": 0.3}
)

print(response["output"]["message"]["content"][0]["text"])

Switching from Nova Pro to Claude Sonnet is a one-line change - modelId only.

RAG With Bedrock Knowledge Bases

For enterprise document Q&A, Bedrock Knowledge Bases manages the full RAG pipeline: S3 ingestion, chunking, embedding (via Titan Embeddings), OpenSearch Serverless vector store, and retrieval:

python

bedrock_agent = boto3.client("bedrock-agent-runtime")

response = bedrock_agent.retrieve_and_generate(
    input={"text": "What are the termination clauses in our supplier agreements?"},
    retrieveAndGenerateConfiguration={
        "type": "KNOWLEDGE_BASE",
        "knowledgeBaseConfiguration": {
            "knowledgeBaseId": "your-kb-id",
            "modelArn": "arn:aws:bedrock:us-east-1::foundation-model/amazon.nova-pro-v1:0"
        }
    }
)
print(response["output"]["text"])

Cost vs OpenAI on AWS

Running GPT-4o via Azure OpenAI on AWS adds egress costs and cross-cloud latency. Nova Pro at $0.80/1M input is cheaper than GPT-4o at $2.50/1M, with all traffic staying within the AWS network.

Summary

Amazon Nova is the pragmatic choice for AWS-native teams: competitive pricing, deep ecosystem integration, and a unified API across all Bedrock models. Explore the model lineup at aws.amazon.com/bedrock/nova and the full Bedrock documentation at docs.aws.amazon.com/bedrock.

#amazon-nova#aws-bedrock#enterprise#multimodal#300k-context

Mahmudul Haque Qudrati

CEO & ML Engineer

Visionary technologist, software engineer, and machine learning specialist. Founder and CEO of Pristren, directing engineering teams that ship production-grade AI/ML pipelines, mission-critical full-stack applications, and developer tooling. Creator of Zlyqor, the unified team workspace platform. Author of 540+ technical guides and benchmark research reports on large language models, agentic workflows, Model Context Protocol (MCP), and modern web stacks.