AWS Bedrock Prompt Caching: Enterprise Efficiency

For enterprise developers, AWS Bedrock provides a managed environment for models from Anthropic, Meta, and others. While the underlying models (like Claude) support caching, Bedrock adds its own layer of infrastructure for managing "Stable" prefixes at scale.

In this lesson, we explore how to configure caching in AWS Bedrock, understanding the "Provisioned Throughput" relationship, and how to use Python's boto3 to trigger cache hits.

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1. Native Model Caching vs. Bedrock Infrastructure

When you use Claude 3.5 on Bedrock, you are essentially using the Anthropic API inside an Amazon wrapper. However, AWS is moving toward a more unified Context Caching system that works across multiple model types.

The Bedrock specific pricing

On Bedrock, pricing for cached tokens usually follows the same "Write vs. Read" philosophy we saw in Lesson 5.2.

• Write tokens: Standard rate.
• Read tokens: ~90% lower rate.

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2. Configuring Caching in boto3

Unlike the direct Anthropic SDK, Bedrock requires you to pass the caching instructions as part of the messages array in the invoke_model call.

Python Code: Bedrock Caching with Claude

import boto3
import json

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

def invoke_with_caching(prompt_context, user_query):
    body = {
        "anthropic_version": "bedrock-2023-05-31",
        "max_tokens": 1024,
        "system": [
            {
                "type": "text",
                "text": prompt_context,
                "cache_control": {"type": "ephemeral"} # The Cache Point
            }
        ],
        "messages": [
            {"role": "user", "content": user_query}
        ]
    }
    
    response = bedrock_runtime.invoke_model(
        modelId="anthropic.claude-3-5-sonnet-20240620-v1:0",
        body=json.dumps(body)
    )
    
    # Usage metrics in Bedrock are returned in the response headers
    # often named 'x-amzn-bedrock-input-token-count' and similar
    return json.loads(response.get('body').read())

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3. The Role of Provisioned Throughput

In the AWS ecosystem, if you use Provisioned Throughput (PT) (renting a model for $X/hour), prompt caching benefits you differently.

• On On-Demand pricing: Caching saves you Money.
• On Provisioned Throughput: Caching saves you Capacity.

By caching a prompt, the "Model Units" do less work per request. This means your single rented instance can handle more concurrent users before hitting its limit. Caching effectively increases your ROI on your fixed-cost infrastructure.

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4. Cache Expiration in Bedrock

Bedrock caches are Ephemeral. This means they stay in memory for a short window (typically 5 to 30 minutes) after the last hit.

Architectural Tip: If your application has sparse traffic (one user every 2 hours), prompt caching won't help you because the cache will expire between visits. To maximize hits, "Bundle" your tasks into high-frequency batches.

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5. Security and Token Hygiene (IAM)

Because cached prompts stay in the provider's GPU memory, some organizations worry about Data Isolation. On AWS Bedrock:

• Caching is managed within your AWS Account Boundary.
• A cached prompt from User A cannot be accessed by User B if they are in different accounts or if the prompt prefix differs.

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6. Summary and Key Takeaways

1. Invoke Wrapper: Use the standard cache_control headers inside the Bedrock message body.
2. TTFT Priority: Caching on Bedrock is primarily used to solve high-latency issues in complex enterprise search.
3. PT Awareness: Caching increases the "Tokens-per-Minute" capacity of your rented model instances.
4. TTL (Time to Live): Be aware that caches are temporary; they are "Warm Memory," not "Permanent Storage."

In the next lesson, Managing Cache Lifecycles, we look at چگونه to programmatically ensure your cache stays "Hot" during busy hours.

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Exercise: The Bedrock Tracker

1. If you are using boto3, find where the input_token_count is located in the response object.
2. Run a loop of 10 identical requests (with a 5,000 token system prompt).
3. Record the Latency (ms) for each request.

• Does Request #1 take significantly longer than Request #2?
• If so, by what percentage? (This is your "Cache Speed ROI").

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Congratulations on completing Module 5 Lesson 3! You are now an AWS AI optimization expert.