> ## Documentation Index
> Fetch the complete documentation index at: https://docs.portkey.ai/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Prompt Caching on Bedrock

Prompt caching on Amazon Bedrock lets you cache specific portions of your requests for repeated use. This feature significantly reduces inference response latency and input token costs by allowing the model to skip recomputation of previously processed content.

With Portkey, you can easily implement Amazon Bedrock's prompt caching through our OpenAI-compliant unified API and prompt templates.

## Model Support

Amazon Bedrock prompt caching is generally available with the following models:

<Info>
  **Currently Supported Models:**

  * Claude 3.7 Sonnet
  * Claude 3.5 Haiku
  * Amazon Nova Micro
  * Amazon Nova Lite
  * Amazon Nova Pro

  Customers who were given access to Claude 3.5 Sonnet v2 during the prompt caching preview will retain their access, but no additional customers will be granted access to prompt caching on the Claude 3.5 Sonnet v2 model.
</Info>

## How Bedrock Prompt Caching Works

When using prompt caching, you define **cache checkpoints** - markers that indicate parts of your prompt to cache. These cached sections must be static between requests; any alterations will result in a cache miss.

<Note>
  You can also use Bedrock Prompt Caching Feature with Portkey's Prompt Templates.
</Note>

## Implementation Examples

Here's how to implement prompt caching with Portkey:

<CodeGroup>
  ```javascript NodeJS theme={"system"}
  import Portkey from 'portkey-ai'

  const portkey = new Portkey({
      apiKey: "PORTKEY_API_KEY", // defaults to process.env["PORTKEY_API_KEY"]
      virtualKey: "VIRTUAL_KEY" // Your Bedrock Virtual Key
  })

  const chatCompletion = await portkey.chat.completions.create({
      messages: [
          { "role": 'system', "content": [
              {
                  "type":"text","text":"You are a helpful assistant"
              },
              {
                  "type":"text","text":"This is a large document I want to cache...",
                  "cache_control": {"type": "ephemeral"}
              }
          ]},
          { "role": 'user', "content": 'Summarize the above document for me in 20 words' }
      ],
      model: 'anthropic.claude-3-7-sonnet-20250219-v1:0'
  });

  console.log(chatCompletion.choices[0].message.content);
  ```

  ```python Python theme={"system"}
  from portkey_ai import Portkey

  portkey = Portkey(
      api_key="PORTKEY_API_KEY",
      virtual_key="BEDROCK_VIRTUAL_KEY",
  )

  chat_completion = portkey.chat.completions.create(
      messages= [
          { "role": 'system', "content": [
              {
                  "type":"text","text":"You are a helpful assistant"
              },
              {
                  "type":"text","text":"This is a large document I want to cache...",
                  "cache_control": {"type": "ephemeral"}
              }
          ]},
          { "role": 'user', "content": 'Summarize the above document in 20 words' }
      ],
      model= 'anthropic.claude-3-7-sonnet-20250219-v1:0',
  )

  print(chat_completion.choices[0].message.content)
  ```

  ```javascript OpenAI NodeJS theme={"system"}
  import OpenAI from "openai";
  import { PORTKEY_GATEWAY_URL, createHeaders } from "portkey-ai";

  const openai = new OpenAI({
      apiKey: "BEDROCK_API_KEY",
      baseURL: PORTKEY_GATEWAY_URL,
      defaultHeaders: createHeaders({
          provider: "bedrock",
          apiKey: "PORTKEY_API_KEY",
    }),
  });

  const chatCompletion = await openai.chat.completions.create({
      messages: [
          { "role": 'system', "content": [
              {
                  "type":"text","text":"You are a helpful assistant"
              },
              {
                  "type":"text","text":"This is a large document I want to cache...",
                  "cache_control": {"type": "ephemeral"}
              }
          ]},
          { "role": 'user', "content": 'Summarize the above document for me in 20 words' }
      ],
      model: 'anthropic.claude-3-7-sonnet-20250219-v1:0',
  });

  console.log(chatCompletion.choices[0].message.content);
  ```

  ```python OpenAI Python theme={"system"}
  from openai import OpenAI
  from portkey_ai import PORTKEY_GATEWAY_URL, createHeaders

  client = OpenAI(
      api_key="BEDROCK_API_KEY",
      base_url=PORTKEY_GATEWAY_URL,
      default_headers=createHeaders(
          api_key="PORTKEY_API_KEY",
          provider="bedrock",
      )
  )

  chat_completion = client.chat.completions.create(
      messages= [
          { "role": 'system', "content": [
              {
                  "type":"text","text":"You are a helpful assistant"
              },
              {
                  "type":"text","text":"This is a large document I want to cache...",
                  "cache_control": {"type": "ephemeral"}
              }
          ]},
          { "role": 'user', "content": 'Summarize the above document in 20 words' }
      ],
      model= 'anthropic.claude-3-7-sonnet-20250219-v1:0',
  )

  print(chat_completion.choices[0].message.content)
  ```

  ```sh REST API theme={"system"}
  curl https://api.portkey.ai/v1/chat/completions \
    -H "Content-Type: application/json" \
    -H "x-portkey-virtual-key: $BEDROCK_VIRTUAL_KEY" \
    -H "x-portkey-api-key: $PORTKEY_API_KEY" \
    -d '{
      "model": "anthropic.claude-3-7-sonnet-20250219-v1:0",
      "messages": [
          { "role": "system", "content": [
              {
                  "type":"text","text":"You are a helpful assistant"
              },
              {
                  "type":"text","text":"This is a large document I want to cache...",
                  "cache_control": {"type": "ephemeral"}
              }
          ]},
          { "role": "user", "content": "Summarize the above document for me in 20 words" }
      ]
    }'
  ```
</CodeGroup>

## Supported Features and Limitations

<Info>
  **Supported Features**

  * Text prompts and images embedded within text prompts
  * Multiple cache checkpoints per request
  * Caching in system prompts, messages, and tools fields (model-dependent)
</Info>

### Supported Models and Limits

Below is a detailed table of supported models, their minimum token requirements, maximum cache checkpoints, and fields that support caching:

| Model             | Model ID                                  | Min tokens per checkpoint | Max checkpoints per request | Cacheable fields        |
| ----------------- | ----------------------------------------- | ------------------------- | --------------------------- | ----------------------- |
| Claude 3.7 Sonnet | anthropic.claude-3-7-sonnet-20250219-v1:0 | 1,024                     | 4                           | system, messages, tools |
| Claude 3.5 Haiku  | anthropic.claude-3-5-haiku-20241022-v1:0  | 2,048                     | 4                           | system, messages, tools |
| Amazon Nova Micro | amazon.nova-micro-v1:0                    | 1,000                     | 4                           | system, messages        |
| Amazon Nova Lite  | amazon.nova-lite-v1:0                     | 1,000                     | 4                           | system, messages        |
| Amazon Nova Pro   | amazon.nova-pro-v1:0                      | 1,000                     | 4                           | system, messages        |

<Note>
  * The Amazon Nova models support a maximum of 32k tokens for prompt caching.
  * For Claude models, tools caching is fully supported.
  * Tools caching is not supported for Amazon Nova models.
</Note>

## Related Resources

<Card title="AWS Bedrock Prompt Caching Docs" href="https://docs.aws.amazon.com/bedrock/latest/userguide/prompt-caching.html">
  For more detailed information on Bedrock prompt caching, refer to:
</Card>