Model Distillation on Bedrock: Shrink AI Costs by 500 Percent

Model Distillation on Bedrock: Shrink AI Costs by 500%

Large language models are expensive to run. Model distillation—training smaller models to mimic larger ones—can slash inference costs while maintaining 90%+ of the quality. AWS Bedrock now offers automated distillation as a managed service.

What is Model Distillation?

Bedrock Distillation Workflow

# Step 1: Generate training data with teacher model
import boto3
import json

bedrock = boto3.client('bedrock-runtime')

prompts = load_your_production_prompts()  # Your actual use cases
training_data = []

for prompt in prompts:
    # Get teacher model responses
    response = bedrock.converse(
        modelId='anthropic.claude-3-5-sonnet-v2',
        messages=[{'role': 'user', 'content': [{'text': prompt}]}]
    )
    training_data.append({
        'prompt': prompt,
        'completion': response['output']['message']['content'][0]['text']
    })

# Step 2: Upload to S3
s3 = boto3.client('s3')
s3.put_object(
    Bucket='my-distillation-bucket',
    Key='training-data.jsonl',
    Body='\n'.join(json.dumps(d) for d in training_data)
)

Launch Distillation Job

# Step 3: Create distillation job
bedrock_admin = boto3.client('bedrock')

job = bedrock_admin.create_model_customization_job(
    jobName='customer-support-distillation',
    customModelName='customer-support-micro',
    roleArn='arn:aws:iam::123456789012:role/BedrockCustomization',
    baseModelIdentifier='amazon.nova-micro-v1',  # Student model
    customizationType='DISTILLATION',
    trainingDataConfig={
        's3Uri': 's3://my-distillation-bucket/training-data.jsonl'
    },
    outputDataConfig={
        's3Uri': 's3://my-distillation-bucket/output/'
    },
    hyperParameters={
        'epochCount': '3',
        'batchSize': '8',
        'learningRate': '0.00001'
    }
)

Cost Savings Example

When to Use Distillation

• High volume: 100K+ requests/month makes distillation ROI positive
• Narrow domain: Specialized tasks distill better than general ones
• Latency requirements: Smaller models respond faster
• Cost pressure: When AI costs threaten project viability

Model distillation is becoming essential for production AI. The quality gap between distilled small models and full-size models is shrinking fast.