Unlocking the Future: Generative AI for Canadian Organizations

Generative AI has emerged as a transformative force across various sectors, enabling organizations to enhance their operations and improve customer experiences. Canadians are now uniquely positioned to harness the power of advanced foundation models such as Anthropic’s Claude Sonnet 4.5 and Claude Haiku 4.5, accessible through Amazon Bedrock via Cross-Region Inference (CRIS). This article delves into how Canadian organizations can leverage these capabilities to accelerate their AI initiatives, migrate from older models, and manage quotas effectively.

Canadian Cross-Region Inference: Your Gateway to Global AI Innovation

Amazon Bedrock introduces Cross-Region Inference (CRIS), a robust feature facilitating the distribution of inference processing across multiple AWS Regions. This technology enables higher throughput and scalability, ensuring that generative AI applications remain both responsive and reliable, even under heavy loads.

Types of Cross-Region Inference Profiles

There are two distinct types of CRIS profiles available:

1. Geographic CRIS: Automatically selects the optimal commercial Region within a specified geography to process inference requests.

2. Global CRIS: Enhances the capability of routing inference requests to AWS Regions worldwide, optimizing resource use and bolstering model throughput.

What’s crucial to understand is that while inference processing might occur in another Region, all data at rest—logs, knowledge bases, configurations—stays securely within the Canada (Central) Region. This approach ensures compliance with data governance requirements while utilizing global AI capabilities.

Cross-Region Inference Configuration for Canada

CRIS provides Canadian organizations with earlier access to cutting-edge foundation models. During peak business periods, such as tax season and Black Friday, CRIS efficiently handles demand spikes, delivering higher throughput and resilience by distributing requests across a broader pool of resources.

Organizations can choose between two CRIS profiles based on their specific needs:

• US Cross-Region Inference

  • Source Region: ca-central-1
  • Destination Regions: Various supported US Regions
  • Description: Requests routed from Canada can be processed in US Regions where capacity allows.
• Global Inference
  • Source Region: ca-central-1
  • Destination Regions: Accessibility to Global AWS Regions
  • Description: Requests routed globally for comprehensive capacity utilization.

CRIS Profile	Source Region	Destination Regions	Description
US Cross-Region	ca-central-1	Multiple US Regions	Requests from Canada are routed to supported US Regions.
Global Inference	ca-central-1	Global AWS Regions	Requests routed to any supported global region.

Getting Started with CRIS from Canada

To leverage CRIS, organizations must follow specific steps:

Configure AWS Identity and Access Management (IAM) Permissions

Ensure that the IAM role or user has the necessary permissions to invoke Amazon Bedrock models through cross-Region inference profiles. Example policy for US cross-Region inference:

json
{
"Version": "2012-10-17",
"Statement": [
{
"Effect": "Allow",
"Action": [
"bedrock:InvokeModel"
],
"Resource": [
"arn:aws:bedrock:ca-central-1::inference-profile/us.anthropic.claude-sonnet-4-5-20250929-v1:0"
]
},
{
"Effect": "Allow",
"Action": [
"bedrock:InvokeModel"
],
"Resource": [
"arn:aws:bedrock:*::foundation-model/anthropic.claude-sonnet-4-5-20250929-v1:0"
],
"Condition": {
"StringLike": {
"bedrock:InferenceProfileArn": "arn:aws:bedrock:ca-central-1::inference-profile/us.anthropic.claude-sonnet-4-5-20250929-v1:0"
}
}
}
]
}

For specifics about global CRIS, AWS provides dedicated guidance to optimize the approach.

Use Cross-Region Inference Profiles

After configuring IAM permissions, integrate your application with the appropriate inference profile. Profiles utilize prefixes for routing scope, including models like Claude Sonnet 4.5 and Claude Haiku 4.5.

Here’s how to set up the inference profile IDs:

Model	Routing Scope	Inference Profile ID
Claude Sonnet 4.5	US Regions	us.anthropic.claude-sonnet-4-5-20250929-v1:0
Claude Sonnet 4.5	Global	global.anthropic.claude-sonnet-4-5-20250929-v1:0
Claude Haiku 4.5	US Regions	us.anthropic.claude-haiku-4-5-20251001-v1:0
Claude Haiku 4.5	Global	global.anthropic.claude-haiku-4-5-20251001-v1:0

Example Code

Utilizing the Amazon Bedrock Converse API with a US CRIS inference profile from Canada is straightforward. Below is an illustrative Python code snippet:

python
import boto3

Initialize Bedrock Runtime client

bedrock_runtime = boto3.client(
service_name="bedrock-runtime",
region_name="ca-central-1" # Canada (Central) Region
)

Define the inference profile ID

inference_profile_id = "us.anthropic.claude-sonnet-4-5-20250929-v1:0"

Prepare the conversation

response = bedrock_runtime.converse(
modelId=inference_profile_id,
messages=[
{
"role": "user",
"content": [
{
"text": "What are the benefits of using Amazon Bedrock for Canadian organizations?"
}
]
}
],
inferenceConfig={
"maxTokens": 512,
"temperature": 0.7
}
)

Print the response

print(f"Response: {response[‘output’][‘message’][‘content’][0][‘text’]}")

Quota Management for Canadian Workloads

When operating with CRIS from Canada, quota management is essential. Quota increases requested for the Canada (Central) Region apply universally to all inference requests originating from Canada, regardless of where they are processed.

Understanding Quota Calculations

It’s important to calculate your required quota increases thoughtfully, especially when considering the burndown rate. For instance, certain models like Anthropic Claude Opus 4 and Claude Sonnet 4.5 have a burn down rate of 5x for output tokens. This means one output token consumes five tokens from your quotas. In contrast, other models have a 1:1 ratio for input tokens.

Requesting Quota Increases

To request quota increases for CRIS in Canada:

1. Navigate to the AWS Service Quotas console in the Canada (Central) Region.
2. Search for the specific model quota (e.g., “Claude Sonnet 4.5 tokens per minute”).
3. Submit a request based on your projected usage.

Migrating From Older Claude Models to Claude 4.5

Organizations using older Claude models should plan for a transition to Claude 4.5. This will allow them to tap into the latest model capabilities.

key considerations during this migration include:

1. Benchmark Current Performance: Establish baseline metrics.
2. Test With Representative Workloads: Validate performance and optimize prompts.
3. Gradual Rollout: Implement a phased transition.
4. Monitoring and Adjustments: Continuously track performance metrics and quotas.

Choosing Between US and Global Inference Profiles

Canadian organizations have the flexibility to choose between US and Global inference profiles based on specific needs. If your organization has existing US data processing agreements, the US cross-Region inference might be the ideal choice. Alternatively, global profiles offer maximum capacity for organizations prioritizing scalability.

By leveraging CRIS, Canadian organizations can remain competitive while adhering to compliance requirements and tapping into global AI advancements, setting the stage for a new era of innovation and operational excellence.