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Powering Claude: My Deep Dive into the 5GW Anthropic-AWS Trainium Partnership

I deep dive into the expanded Anthropic and AWS partnership, focusing on how 5 gigawatts of AWS Trainium capacity will fuel Claude's advanced training and inference. This article covers Trainium's role, the sheer scale of the deal, and practical examples of consuming Claude via Amazon Bedrock.

18 min read
Powering Claude: My Deep Dive into the 5GW Anthropic-AWS Trainium Partnership
TL;DR

I deep dive into the expanded Anthropic and AWS partnership, focusing on how 5 gigawatts of AWS Trainium capacity will fuel Claude's advanced training and inference. This article covers Trainium's role, the sheer scale of the deal, and practical examples of consuming Claude via Amazon Bedrock.

Prerequisites

As a cloud architect and AI specialist, I've seen firsthand how the explosion of frontier AI models like Anthropic's Claude is redefining compute requirements. It's no longer just about software; it's a physical infrastructure challenge, pushing the boundaries of dedicated silicon. That's why the expanded partnership between Anthropic and AWS, securing up to 5 gigawatts (GW) of compute capacity, has really caught my attention. This isn't just a cloud credit deal; it's a strategic commitment to custom-designed AWS Trainium2 and the forthcoming Trainium3 chips, built to handle the insatiable demands of developing and deploying advanced large language models (LLMs).

When I architect large-scale AI solutions, the sheer compute required is often the most critical bottleneck. Training advanced LLMs like Anthropic's Claude isn't a trivial task; it demands exaflops of processing power, vast amounts of high-bandwidth memory, and an infrastructure capable of sustaining operations for weeks or months. This deepened Anthropic and AWS partnership directly addresses that challenge. It's a strategic move to ensure Claude has the dedicated, custom-designed silicon it needs, not just for today's models, but for the next generation.

In this article, I'll explain the significance of this Anthropic and AWS partnership, detail what AWS Trainium is, and illustrate how Claude can leverage this massive compute capacity for both training and inference. We'll explore the implications of securing up to 5 GW of power, including nearly 1 GW of Trainium2 and Trainium3 coming online by the end of 2026, against the backdrop of exploding demand for Anthropic's models and their forthcoming IPO.

To follow along with the concepts and potential implementations I discuss, you'll need a basic setup:

  • An AWS Account with appropriate permissions for services like Amazon Bedrock, Amazon SageMaker, and EC2.
  • The AWS CLI configured and authenticated (version 2.15.x or newer is recommended). I typically configure it for a European region from the start:
aws configure set default.region eu-west-1
  • Python 3.12+ installed, along with pip for dependency management.
  • Familiarity with infrastructure as code (IaC) principles, ideally with Terraform.

You can find the latest AWS CLI installation instructions on the official AWS documentation website.

Example Repository:

While direct access to Anthropic's internal training infrastructure on Trainium is proprietary, you can explore patterns for high-performance AI inference on AWS through community examples. I often look to the AWS Samples GitHub organization for various reference architectures involving AI/ML.

Architecture & Concepts

At the core of this monumental collaboration is AWS's custom-designed silicon, specifically Trainium. In the world of AI, general-purpose GPUs are powerful, but custom ASICs (Application-Specific Integrated Circuits) like Trainium and Inferentia are engineered from the ground up for the unique demands of deep learning workloads. Trainium chips are optimized for high-performance training of deep learning models, often offering significant cost-performance advantages over comparable GPU instances for specific tasks.

Anthropic's commitment to utilize Trainium for current and future generations of Claude (including Trainium2 and Trainium3, and likely future Trainium4 chips) highlights the strategic advantage of custom silicon. By working closely with AWS Annapurna Labs, Anthropic can provide direct feedback, ensuring that future Trainium designs are tailored to the specific needs of frontier LLMs like Claude. This iterative co-design process is crucial for pushing the boundaries of AI capabilities.

The Co-Design Advantage

This tight feedback loop between a major AI developer like Anthropic and the chip design team at AWS Annapurna Labs is a game-changer. It means future Trainium designs aren't just theoretically optimized; they're battle-tested against the specific, real-world workloads of frontier LLMs. This strategic alignment accelerates innovation in ways that off-the-shelf hardware can't match, directly influencing Claude's capabilities down the line.

You can read more about the partnership on the Anthropic news page.

The scale of this agreement—securing up to 5 GW of capacity, with nearly 1 GW of Trainium2 and Trainium3 by late 2026—is staggering. To put that in perspective, a typical modern nuclear power plant generates around 1 GW. This level of dedicated compute ensures Anthropic can continue to innovate rapidly, train more complex models, and expand Claude's capabilities without being constrained by hardware availability, a common concern in the booming AI industry. This massive compute investment is also a strong indicator for investors, especially with Anthropic's forthcoming IPO, signaling its commitment to scalable infrastructure. For a deeper financial perspective, I often look at how these demands shape commodity stocks; my team's analysis on Clear Signals (markets.thecloudarchitect.io/en/analysis/) tracks these implications on the energy sector. For a direct head-to-head of the three hyperscalers through an investor lens — cloud revenue growth, AI capex, operating margins, and valuation — see my comparative analysis Hyperscaler Showdown: Microsoft Azure vs Alphabet Google Cloud vs Amazon AWS.

From an architectural standpoint, Claude's deployment on AWS Trainium involves two primary use cases:

  1. Training: This involves large-scale, distributed training runs for foundational models. It typically uses massive clusters of Trainium instances working in parallel, utilizing high-speed interconnects (like AWS Elastic Fabric Adapter - EFA) and petabytes of high-performance storage. AWS SageMaker provides the orchestration for these training jobs, managing distributed data parallelism and model parallelism across many instances.
  2. Inference: This is about deploying trained Claude models for real-time or batch inference. While Inferentia is AWS's dedicated inference chip, Trainium can also perform inference, especially for larger, more complex models or scenarios where latency is less critical than throughput, or when the model requires a specific Trainium-optimized runtime. For general production inference, Anthropic makes Claude available via services like Amazon Bedrock, which abstracts the underlying compute. You can learn more about AWS Trainium capabilities on their product page.

Model Governance and Security: When deploying AI models at this scale, security and governance are paramount. I'd typically utilize AWS services for securing model artifacts (e.g., S3 with encryption and access policies), managing access to training and inference environments (IAM), and monitoring for anomalies (CloudWatch, CloudTrail). Integrating with AWS Key Management Service (KMS) for data encryption at rest and in transit, and leveraging PrivateLink for secure network access, are standard practices for securing sensitive AI workloads.

Code Example: Illustrative Training Cluster Infrastructure with Terraform

While I can't directly configure Anthropic's private Trainium clusters, I can show you how I'd set up foundational infrastructure for a highly-performant, secure compute environment using Terraform in eu-west-1. This might involve a VPC, subnets, security groups, and an EC2 instance profile with permissions for SageMaker to launch Trainium instances.

# main.tf - Illustrative Terraform for high-performance compute environment

# Configure AWS Provider for a European region
provider "aws" {
  region = "eu-west-1"
}

# Create a VPC for isolation
resource "aws_vpc" "ai_vpc" {
  cidr_block = "10.0.0.0/16"
  enable_dns_hostnames = true
  tags = {
    Name = "anthropic-compute-vpc"
  }
}

# Public Subnet (for example, if needed for NAT Gateway or Load Balancer egress)
resource "aws_subnet" "public_subnet" {
  vpc_id            = aws_vpc.ai_vpc.id
  cidr_block        = "10.0.1.0/24"
  availability_zone = "eu-west-1a"
  map_public_ip_on_launch = true
  tags = {
    Name = "anthropic-compute-public-subnet"
  }
}

# Private Subnet (for Trainium instances, ensuring no direct internet access)
resource "aws_subnet" "private_subnet" {
  vpc_id            = aws_vpc.ai_vpc.id
  cidr_block        = "10.0.2.0/24"
  availability_zone = "eu-west-1a"
  tags = {
    Name = "anthropic-compute-private-subnet"
  }
}

# Security Group for Trainium instances - allowing internal EFA traffic, SSH for management
resource "aws_security_group" "trainium_sg" {
  vpc_id = aws_vpc.ai_vpc.id
  name   = "trainium-instance-sg"
  description = "Security group for Trainium instances"

  # Allow all internal traffic for distributed training (EFA)
  ingress {
    from_port   = 0
    to_port     = 0
    protocol    = "-1"
    self        = true
  }

  # Egress to anywhere (e.g. S3, external APIs)
  egress {
    from_port   = 0
    to_port     = 0
    protocol    = "-1"
    cidr_blocks = ["0.0.0.0/0"]
  }

  tags = {
    Name = "trainium-sg"
  }
}

# IAM Role for SageMaker/Trainium instances
resource "aws_iam_role" "sagemaker_trainium_role" {
  name               = "sagemaker-trainium-role"
  assume_role_policy = jsonencode({
    Version   = "2012-10-17"
    Statement = [
      {
        Action    = "sts:AssumeRole"
        Effect    = "Allow"
        Principal = {
          Service = "sagemaker.amazonaws.com"
        }
      }
    ]
  })
}

# IAM Policy for S3 access (training data, model artifacts)
resource "aws_iam_policy" "sagemaker_s3_policy" {
  name        = "sagemaker-s3-access-policy"
  description = "Allows SageMaker to access S3 buckets for AI training and model storage"
  policy      = jsonencode({
    Version   = "2012-10-17"
    Statement = [
      {
        Action   = [
          "s3:GetObject",
          "s3:PutObject",
          "s3:ListBucket"
        ],
        Effect   = "Allow",
        Resource = [
          "arn:aws:s3:::*sagemaker*", # For SageMaker-managed resources
          "arn:aws:s3:::*ai-model-training-data*", # For your own training data/model buckets
          "arn:aws:s3:::*" # Broad access for example, narrow in production
        ]
      }
    ]
  })
}

resource "aws_iam_role_policy_attachment" "sagemaker_s3_attach" {
  role       = aws_iam_role.sagemaker_trainium_role.name
  policy_arn = aws_iam_policy.sagemaker_s3_policy.arn
}

output "vpc_id" {
  value = aws_vpc.ai_vpc.id
}
output "private_subnet_id" {
  value = aws_subnet.private_subnet.id
}
output "trainium_security_group_id" {
  value = aws_security_group.trainium_sg.id
}
output "sagemaker_trainium_role_arn" {
  value = aws_iam_role.sagemaker_trainium_role.arn
}

Reference Implementation: This Terraform example establishes the network and IAM foundations. For actual SageMaker training job definitions utilizing Trainium instances (e.g., ml.trn1.32xlarge or ml.trn1n.32xlarge), you would integrate this with SageMaker's API or SDK. I find the AWS Machine Learning Blog often features deep dives into such implementations.

Implementation Guide

As a practitioner, while I won't be directly provisioning Trainium clusters for Anthropic, my interest lies in leveraging the end product: powerful LLMs like Claude. The expanded Trainium capacity means Anthropic can train more capable models faster, which ultimately translates to better, more accessible models for developers like us via services like Amazon Bedrock.

Here, I'll walk through how you can interact with Claude via Amazon Bedrock, which is the primary consumption mechanism for Anthropic's models on AWS. This assumes Anthropic has deployed a Claude model to Bedrock, leveraging their immense Trainium-backed capacity. The model ID will refer to the current Claude Sonnet 4.6 model.

1. Set up Your AWS Environment and Bedrock Access

First, ensure your AWS CLI is configured for a European region like eu-west-1. Then, enable access to Anthropic's Claude models within Amazon Bedrock. This is a one-time setup in the Bedrock console.

# Configure AWS CLI to a European region
aws configure set default.region eu-west-1

# (Optional) Verify current region
aws configure get default.region

# Expected Output:
# eu-west-1

# To enable model access for Claude in Bedrock (usually done via console or SDK)
# Example CLI command to check model availability (requires prior console activation)
aws bedrock list-foundation-models --query "modelSummaries[?providerName=='Anthropic'].modelId" --output json

Expected Output (example):

[
    "anthropic.claude-sonnet-4-6",
    "anthropic.claude-opus-4-7"
]

This confirms that Claude Sonnet 4.6 and Opus 4.7 are available in your specified region after enabling them in the Bedrock console (under Model access).

2. Invoke Claude Sonnet 4.6 via Amazon Bedrock (Python)

Now, let's use Python to make an inference call to a Claude Sonnet 4.6 model. I typically use the boto3 SDK for this.

# bedrock_claude_inference.py
import boto3
import json

def invoke_claude_sonnet(prompt_text: str, region_name: str = "eu-west-1") -> str:
    """
    Invokes the Claude Sonnet 4.6 model on Amazon Bedrock for inference.
    """
    client = boto3.client(service_name="bedrock-runtime", region_name=region_name)

    # The model ID for Claude Sonnet 4.6. Verify current stable versions in Bedrock documentation.
    # Using 'anthropic.claude-sonnet-4-6' as the current stable ID.
    # Always verify latest API identifiers on https://docs.aws.amazon.com/bedrock/latest/userguide/model-ids.html
    model_id = "anthropic.claude-sonnet-4-6"

    # The format of the request body varies by model. For Claude, it often uses 'anthropic_version' and 'messages'.
    # The prompt should be formatted for Claude's conversation turn structure.
    body = json.dumps({
        "anthropic_version": "bedrock-2023-05-31",
        "max_tokens": 1024,
        "messages": [
            {
                "role": "user",
                "content": prompt_text
            }
        ],
        "temperature": 0.7,
        "top_p": 0.9
    })

    response = client.invoke_model(
        body=body,
        modelId=model_id,
        accept="application/json",
        contentType="application/json"
    )

    response_body = json.loads(response.get("body").read())

    # Extracting the content from the response
    for output_content in response_body.get("content", []):
        if output_content.get("type") == "text":
            return output_content.get("text")
    return "No text content found in response."

if __name__ == "__main__":
    my_prompt = "Explain the significance of the Anthropic and AWS partnership in one paragraph."
    print(f"\
User: {my_prompt}")
    claude_response = invoke_claude_sonnet(my_prompt)
    print(f"\
Claude: {claude_response}")

    financial_prompt = "What are the potential financial market implications for compute providers given the Anthropic-AWS 5GW deal?"
    print(f"\
User: {financial_prompt}")
    financial_response = invoke_claude_sonnet(financial_prompt)
    print(f"\
Claude: {financial_response}")

Run the script:

python3.12 bedrock_claude_inference.py

Expected Output (example):

User: Explain the significance of the Anthropic and AWS partnership in one paragraph.

Claude: The Anthropic and AWS partnership is highly significant because it secures a massive, long-term compute capacity — up to 5 gigawatts, primarily on custom Trainium chips — for Anthropic to train and deploy its Claude models. This dedicated infrastructure alleviates a major bottleneck in AI development, enabling Anthropic to accelerate research, develop more advanced models, and scale inference efficiently to meet surging demand. For AWS, it solidifies its position as a leading provider of specialized AI infrastructure and strengthens its ecosystem around services like Bedrock, demonstrating the efficacy of its custom silicon strategy.

User: What are the potential financial market implications for compute providers given the Anthropic-AWS 5GW deal?

Claude: The Anthropic-AWS 5GW deal signals a massive, sustained demand for AI-specific compute, which will likely drive significant revenue growth for cloud providers like AWS that invest in custom AI silicon. This could intensify the competitive landscape among infrastructure providers and potentially impact stock valuations of companies specializing in AI hardware. It also highlights the growing importance of securing long-term compute commitments, potentially leading to similar large-scale deals and further integrating AI startups into major cloud ecosystems, influencing their IPO prospects and market trajectories.

For more details on invoking models with Bedrock, I refer to the Amazon Bedrock User Guide.

3. Provisioning Inference Endpoints with Terraform (Conceptual)

While Bedrock handles the underlying inference infrastructure, for more customized or fine-tuned models, I might use AWS SageMaker. Here’s a conceptual Terraform configuration for a SageMaker endpoint that could host a model for inference, illustrating how infrastructure for high-scale inference is managed.

# sagemaker_inference.tf - Conceptual SageMaker Inference Endpoint

# Reuse the IAM Role created earlier
data "aws_iam_role" "sagemaker_role" {
  name = aws_iam_role.sagemaker_trainium_role.name # From main.tf
}

# Placeholder for a Sagemaker Model (assuming a model artifact exists in S3)
resource "aws_sagemaker_model" "claude_fine_tuned_model" {
  name               = "my-fine-tuned-claude"
  execution_role_arn = data.aws_iam_role.sagemaker_role.arn
  primary_container {
    image = "763104351884.dkr.ecr.eu-west-1.amazonaws.com/huggingface-pytorch-inference:2.0.1-transformers4.28.1-gpu-py310-cu118-ubuntu20.04"
    model_data_url = "s3://<your-model-bucket-name>/model.tar.gz" # Replace with the actual S3 path to your model artifact
  }

  # Network configuration using the VPC/Subnets from main.tf
  vpc_config {
    security_group_ids = [aws_security_group.trainium_sg.id]
    subnets            = [aws_subnet.private_subnet.id]
  }

  tags = {
    Name = "claude-fine-tuned-model"
  }
}

# SageMaker Endpoint Configuration
resource "aws_sagemaker_endpoint_configuration" "claude_endpoint_config" {
  name = "claude-endpoint-config"
  production_variant {
    variant_name           = "default"
    model_name             = aws_sagemaker_model.claude_fine_tuned_model.name
    initial_instance_count = 1
    instance_type          = "ml.g5.2xlarge" # Example GPU instance for inference, or ml.inf1/inf2 for Inferentia
    initial_variant_weight = 1
  }

  tags = {
    Name = "claude-inference-endpoint-config"
  }
}

# SageMaker Endpoint
resource "aws_sagemaker_endpoint" "claude_inference_endpoint" {
  name                    = "claude-inference-endpoint"
  endpoint_config_name    = aws_sagemaker_endpoint_configuration.claude_endpoint_config.name

  tags = {
    Name = "claude-inference-endpoint"
  }
}

output "sagemaker_endpoint_name" {
  value = aws_sagemaker_endpoint.claude_inference_endpoint.name
}

This Terraform configuration gives you control over the instance types, scaling policies, and networking for your inference endpoints. While ml.g5 instances are GPUs, for Anthropic's scale, they might use custom Inferentia-based endpoints that offer extreme cost efficiency for specific types of inference. The model_data_url would point to your pre-trained model artifact, likely stored in an S3 bucket in eu-west-1 or eu-central-1.

You can find complete examples of SageMaker endpoint deployment on the Terraform AWS Provider documentation.

Troubleshooting & Verification

Verifying your AI infrastructure and model invocations is critical. Given the distributed nature of these systems, understanding common pitfalls saves a lot of time. When I'm working with these deployments, I always start with these checks.

Verification Commands:

To verify Bedrock access and Claude model invocation:

# Check Bedrock runtime status (general service health)
aws bedrock-runtime get-model-invocation-logging-configuration

# If using the Python script, verify the output directly.
# A successful response from Claude indicates the setup is correct.

# To check the status of a deployed SageMaker endpoint (if using SageMaker)
aws sagemaker describe-endpoint --endpoint-name claude-inference-endpoint

# Expected output for SageMaker endpoint:
# {
#     "EndpointName": "claude-inference-endpoint",
#     "EndpointArn": "arn:aws:sagemaker:eu-west-1:123456789012:endpoint/claude-inference-endpoint",
#     "EndpointConfigName": "claude-endpoint-config",
#     "ProductionVariants": [
#         {
#             "VariantName": "default",
#             "DeployedImages": [
#                 {
#                     "SpecifiedImage": "...",
#                     "ResolvedImage": "...",
#                     "ResolutionTime": 1.23
#                 }
#             ],
#             "CurrentInstanceCount": 1,
#             "DesiredInstanceCount": 1,
#             "VariantStatus": [
#                 {
#                     "Status": "InService",
#                     "StartTime": 1.23,
#                     "Message": ""
#                 }
#             ],
#             "CurrentWeight": 1.0,
#             "DesiredWeight": 1.0
#         }
#     ],
#     "EndpointStatus": "InService",
#     "CreationTime": 1.23,
#     "LastModifiedTime": 1.23
# }

Common Errors & Solutions:

  1. Error: AccessDeniedException when invoking Bedrock or SageMaker
An error occurred (AccessDeniedException) when calling the InvokeModel operation: User: arn:aws:iam::123456789012:user/developer is not authorized to perform: bedrock:InvokeModel on resource: arn:aws:bedrock:eu-west-1::foundation-model/anthropic.claude-sonnet-4-6

**Solution:** This typically means your IAM user or role lacks the necessary permissions. Ensure the principal calling the API has `bedrock:InvokeModel` permission for the specific model ID or `*` for all models. For SageMaker, check the `execution_role_arn` on your model and endpoint configuration resources. You might need to attach the `AmazonBedrockFullAccess` or `AmazonSageMakerFullAccess` managed policies for testing, then narrow down to least privilege for production.
  1. Error: ModelNotFoundException or ValidationException: Model ID anthropic.claude-sonnet-4-6 not found
An error occurred (ValidationException) when calling the InvokeModel operation: Model ID 'anthropic.claude-sonnet-4-6' not found.

**Solution:** Even with correct IAM permissions, you need to explicitly enable access to specific third-party models in the Amazon Bedrock console. Navigate to **Model access** under the **Bedrock** service in your chosen European region and ensure the desired Claude models are enabled. Also, double-check the `model_id` string for any typos or outdated versions. Always verify against the [latest Bedrock model IDs](https://docs.aws.amazon.com/bedrock/latest/userguide/model-ids.html) in your region.
  1. Error: Terraform InvalidSubnetID.NotFound or InvalidSecurityGroupID.NotFound
Error: InvalidSubnetID.NotFound: The subnet ID 'subnet-0abcdef1234567890' does not exist.

**Solution:** This means the subnet or security group ID referenced in your Terraform configuration (e.g., in `aws_sagemaker_model`) doesn't exist or is in a different region/VPC. Verify the IDs by checking the Terraform outputs (e.g., `terraform output vpc_id`) or by manually inspecting your AWS console. Ensure all resources are created in the same target region (`eu-west-1` or `eu-central-1`) and within the correct VPC.

Testing Script (for Bedrock Python invocation):

The bedrock_claude_inference.py script provided earlier serves as a basic testing script. I often extend it to include more complex prompts, handle streaming responses, or integrate it into a CI/CD pipeline for automated testing of my model access.

Conclusion & Key Takeaways

The deepened partnership between Anthropic and AWS, particularly the strategic investment in up to 5 GW of Trainium capacity, is a defining moment in the competitive AI landscape. From my perspective, this isn't merely a business deal; it's a testament to the fact that cutting-edge AI innovation is now inextricably linked to dedicated, high-performance silicon and robust cloud infrastructure. For Anthropic, it guarantees the compute runway necessary to push Claude to new frontiers. For AWS, it validates their custom silicon strategy and solidifies their position as a critical enabler for the most demanding AI workloads.

FinOps: The Hidden Cost of AI Scale

While the focus is often on performance, managing the sheer scale of compute like 5 GW brings significant financial implications. For me, this reinforces the need for robust FinOps practices. When working with large-scale GPU or custom ASIC clusters, I always emphasize proactive monitoring and automated shutdown policies for idle resources. Unused capacity, even for a short duration, can quickly drain budgets. This isn't just about technical efficiency; it's about making AI sustainable from a business perspective, whether I'm building for myself or advising a team.

Key Takeaways:

  • Custom Silicon is King: AWS Trainium chips are purpose-built for AI training, offering significant performance and cost advantages essential for foundational model development.
  • Scale is Unprecedented: Securing 5 GW of capacity, including substantial Trainium2/3, highlights the massive compute requirements of frontier LLMs and the capital intensity of the AI race.
  • Bedrock is the Gateway: As practitioners, we primarily consume Anthropic's advanced models via Amazon Bedrock, which abstracts the underlying Trainium-powered infrastructure, making Claude accessible.
  • Infrastructure as Code is Essential: Even when consuming managed services, using Terraform for foundational networking, IAM, and potentially SageMaker endpoint provisioning ensures scalability, security, and reproducibility.
  • FinOps is Crucial: Proactive cost management, particularly for large-scale, dedicated compute, is essential to ensure sustainable AI development and deployment.

My next steps often involve exploring how these increasingly capable models, powered by such infrastructure, can be integrated into real-world applications with robust MLOps practices. This includes optimizing inference pipelines, implementing cost-effective fine-tuning strategies, and ensuring responsible AI deployment. The financial implications for the entire supply chain, from energy to chip manufacturing, are also profound, shaping investment decisions and market dynamics. This strategic partnership between Anthropic and AWS will undoubtedly accelerate the pace of innovation for years to come, offering builders like me even more powerful tools.

Repository Resources:

  • Complete Example (Foundational Infra): You can find a more extensive foundational infrastructure example for AI workloads in this repository.
  • Official AWS Bedrock Examples: Explore practical Python notebooks and examples for Amazon Bedrock on the AWS Samples Bedrock repository.
  • Terraform AWS Provider: Dive deeper into AWS resource definitions on the Terraform Registry.

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