Forem: Haowen Huang

Deploying OpenClaw on AWS EC2 - A Developer's Perspective

Haowen Huang — Thu, 16 Apr 2026 04:10:44 +0000

What is OpenClaw?

OpenClaw is a personal AI assistant that you can deploy on your own infrastructure. It can respond to you through channels you already use — WhatsApp, Telegram, Slack, Discord, Google Chat, Signal, iMessage, WeChat, Lark, and 20+ other platforms. It can have voice conversations on macOS/iOS/Android, and render a real-time Canvas you can control. The Gateway is just the control plane — the product itself is the assistant.

If you want a self-hosted, fast, always-on single-user personal assistant, OpenClaw is it.

Why Run Your Own AI Assistant on AWS?

Most people interact with AI through hosted services. That works — until you care about:

Privacy: Your conversations, code, and data stay in your AWS account
Cost control: Pay only for Bedrock API usage, no per-seat SaaS fees
Customization: Full control over models, plugins, and integrations
Security: No open ports, no public endpoints, SSM-only access

By deploying OpenClaw on AWS with Amazon Bedrock, you can use the latest Claude models (Opus 4.6, Sonnet 4.6) as your personal AI assistant — not just for coding, but for automation, messaging, device control, and more.

I decided to try it out myself. Here's how it went.

Architecture: What You're Deploying

🚀 Quick Start: If you're an experienced developer, you can use the deployment template directly:
curl -O https://raw.githubusercontent.com/hanyun2019/openclaw-on-aws/main/openclaw-deployment.yaml

aws cloudformation create-stack \
  --stack-name openclaw \
  --template-body file://openclaw-deployment.yaml \
  --capabilities CAPABILITY_IAM \
  --region us-east-1
📦 Template URL: github.com/hanyun2019/openclaw-on-aws

⚠️ Non-US Region Users: Additional configuration is required after deployment. See the Non-US Region Deployment Guide below.

Before we dive in, here's what the CloudFormation template sets up:

User Access: Connect via SSM Session Manager to establish a secure tunnel, forwarding local localhost:18789 to the EC2 instance — no inbound ports required.

Key design decisions:

EC2 in the public subnet — for initial setup (package downloads via Internet Gateway)
VPC Endpoints in the private subnet — ensures Bedrock API calls never leave the AWS network
No inbound ports open — all access through SSM port forwarding
Graviton (ARM) instances — better price-performance ratio

Prerequisites

Before you start, make sure you have:

An AWS account with billing enabled
IAM permissions: Ability to create CloudFormation, EC2, IAM, and VPC resources
Local tools installed:
- AWS CLI v2
- SSM Session Manager Plugin

US Region Deployment Guide

If you're deploying in us-east-1 or us-west-2, it's genuinely a one-click experience.

Step 1: Download the Template

curl -O https://raw.githubusercontent.com/hanyun2019/openclaw-on-aws/main/openclaw-deployment.yaml

Step 2: Deploy the Stack

aws cloudformation create-stack \
  --stack-name openclaw \
  --template-body file://openclaw-deployment.yaml \
  --capabilities CAPABILITY_IAM \
  --region us-east-1

Optional parameters:

Parameter	Default	Description
`InstanceType`	`t4g.large`	Graviton instance type (2 vCPU, 8GB RAM)
`KeyPairName`	(none)	EC2 key pair for SSH access (optional)
`CreateVPCEndpoints`	`true`	Create VPC endpoints for private Bedrock access
`AllowedSSHCIDR`	(empty)	CIDR block for SSH access (optional)

For example, to use a larger instance:

aws cloudformation create-stack \
  --stack-name openclaw \
  --template-body file://openclaw-deployment.yaml \
  --capabilities CAPABILITY_IAM \
  --region us-east-1 \
  --parameters ParameterKey=InstanceType,ParameterValue=t4g.xlarge

Step 3: Wait (~10-15 minutes)

aws cloudformation wait stack-create-complete \
  --stack-name openclaw --region us-east-1

Go grab a coffee. ☕

Step 4: Get Your Access Info

aws cloudformation describe-stacks \
  --stack-name openclaw \
  --query 'Stacks[0].Outputs' \
  --region us-east-1

This gives you the instance ID and access URL.

Step 5: Connect via SSM Port Forwarding

# Get Instance ID
INSTANCE_ID=$(aws cloudformation describe-stacks \
  --stack-name openclaw \
  --query 'Stacks[0].Outputs[?OutputKey==`InstanceId`].OutputValue' \
  --output text --region us-east-1)

# Start the tunnel
aws ssm start-session \
  --target $INSTANCE_ID \
  --region us-east-1 \
  --document-name AWS-StartPortForwardingSession \
  --parameters '{"portNumber":["18789"],"localPortNumber":["18789"]}'

Step 6: Open OpenClaw

Navigate to the Step3AccessURL from the stack outputs:

http://localhost:18789/?token=<your-token>

That's it. You're in. 🎉

Non-US Region Deployment Guide

If you're deploying outside the US (e.g., Sydney ap-southeast-2), you need to modify the model IDs.

Note: If you're deploying in us-east-1 or us-west-2, you can skip this section. The template's default us. prefixed model IDs work out of the box in US regions.

The Issue

The template defaults to us.anthropic.claude-opus-4-6-v1 — this is the US region Inference Profile. In other regions, you need to use the corresponding regional prefix.

Solution Steps

1. Connect to the Instance via SSM

aws ssm start-session --target <instance-id> --region ap-southeast-2

2. Query Available Inference Profiles (Optional)

aws bedrock list-inference-profiles --region ap-southeast-2 \
  --query 'inferenceProfileSummaries[*].inferenceProfileId' \
  --output table

3. Update OpenClaw Configuration

nano /home/ubuntu/.openclaw/openclaw.json

Replace the us. prefix in model IDs with your region's prefix:

{
  "models": {
    "providers": {
      "amazon-bedrock": {
        "models": [
          {
            "id": "au.anthropic.claude-opus-4-6-v1",
            "name": "Claude Opus 4.6"
          },
          {
            "id": "au.anthropic.claude-sonnet-4-6",
            "name": "Claude Sonnet 4.6"
          }
        ]
      }
    }
  },
  "agents": {
    "defaults": {
      "model": {
        "primary": "amazon-bedrock/au.anthropic.claude-opus-4-6-v1"
      }
    }
  }
}

nano save: Ctrl+O → Enter to confirm → Ctrl+X to exit

4. Restart the Gateway

systemctl --user restart openclaw-gateway.service

Done! 🎉

Understanding Inference Profile Prefixes

Here's a quick reference:

Prefix	Scope	Example Regions
`us.`	US cross-region	us-east-1, us-east-2, us-west-2, ca-central-1
`eu.`	Europe cross-region	eu-central-1, eu-west-1, eu-west-3, eu-north-1
`apac.`	Asia-Pacific cross-region	ap-northeast-1, ap-southeast-1, ap-southeast-2, ap-south-1
`au.`	Australia	ap-southeast-2, ap-southeast-4
`jp.`	Japan	ap-northeast-1, ap-northeast-3
`global.`	Global cross-region (routes to optimal commercial region)	Can be called from all commercial regions

Rule of thumb: Use the most specific prefix for your deployment region.

Deploying in us-east-1 or us-west-2? The default us.anthropic.claude-opus-4-6-v1 just works. No changes needed.
Deploying in ap-southeast-2 (Sydney)? Use au.anthropic.claude-opus-4-6-v1.
Deploying in ap-northeast-1 (Tokyo)? Use jp.anthropic.claude-sonnet-4-6 or apac. prefix.
Deploying in eu-west-1 (Ireland)? Use eu.anthropic.claude-opus-4-6-v1.
Not sure? Query aws bedrock list-inference-profiles in your region to see what's available.

Note: Model availability varies by region. For example, Claude Opus 4.6 in Japan may only be available with the global. prefix, while Sonnet 4.6 has a jp. prefix. Use aws bedrock list-inference-profiles to check actual available profiles in your region.

Cost Estimate

Resource	Spec	Est. Monthly Cost	Calculation
EC2 (`t4g.large`)	2 vCPU, 8GB RAM	~$49	$0.0672/hr × 730 hrs
EBS (`gp3`)	50GB	~$4	$0.08/GB-mo × 50GB
VPC Endpoints (×4)	Interface type	~$29	$0.01/hr × 730 hrs × 4 endpoints
Bedrock API	Usage-based	Variable	—
Total (excl. API)	—	~$82	—

Pricing References (us-east-1 region, 2024):

EC2 pricing: AWS EC2 T4g Instances

EBS pricing: AWS EBS Pricing

VPC Endpoint pricing: AWS PrivateLink Pricing

Note: Actual prices may vary by region and time. Please refer to official AWS pricing pages for current rates.

Cost-saving tip: Set CreateVPCEndpoints=false to save ~$29/month. In this case, Bedrock API calls will route through the Internet Gateway to Bedrock's public endpoints. Traffic is still encrypted via TLS, but traverses the public internet rather than staying within the AWS backbone. For scenarios with strict security or compliance requirements, keep VPC endpoints enabled to ensure traffic never leaves the AWS network.

FAQ

How do I access EC2 via SSM and use OpenClaw TUI?

Option 1: Via AWS Console

Open the EC2 Console
Select your OpenClaw instance
Click Connect → Select Session Manager → Click Connect

Option 2: Via AWS CLI

aws ssm start-session --target <instance-id> --region <your-region>

After connecting, switch to the ubuntu user and launch TUI:

sudo su - ubuntu
openclaw tui

How do I configure OpenClaw and switch Bedrock models?

Use the openclaw config command for configuration management, including switching models on Amazon Bedrock:

openclaw config

Stack deployment timed out (WaitCondition failure)?

Connect to the instance via SSM and check the setup log:

cat /var/log/openclaw-setup.log

Common causes:

Network issues during package downloads
Bedrock model access not yet approved (it can take a few minutes after requesting)

How do I retrieve my Gateway token?

# Replace <stack-name> with your stack name
aws ssm get-parameter \
  --name "/openclaw/<stack-name>/gateway-token" \
  --with-decryption \
  --query 'Parameter.Value' \
  --output text

How do I switch models?

Edit agents.defaults.model.primary in /home/ubuntu/.openclaw/openclaw.json, then restart:

systemctl --user restart openclaw-gateway.service

How do I update OpenClaw?

SSH or SSM into the instance and run:

npm update -g openclaw
systemctl --user restart openclaw-gateway.service

How do I tear it all down?

aws cloudformation delete-stack --stack-name openclaw --region us-east-1

This removes everything — EC2 instance, VPC, IAM roles, all of it. Clean.

Wrapping Up

Deploying OpenClaw on AWS is straightforward — with one note about region selection:

US regions (us-east-1, us-west-2): Truly one-click. The template works out of the box.
Other regions: You need to replace the model IDs with the corresponding regional inference profiles.

Either way, the CloudFormation template handles 90% of the work. What you get at the end is a private, secure AI assistant running on your own infrastructure — no data leaving your AWS account, no third-party services in the loop, and the full power of Claude at your fingertips.

Resources:

📦 Deployment Template (GitHub)

Series Preview: Four Ways to Deploy OpenClaw on AWS

This post is the first in the OpenClaw on AWS series. There are four ways to deploy OpenClaw on AWS, each suited for different scenarios:

Deployment Method	Use Case	Status
EC2 Deployment	Full control, custom configuration, persistent runtime	✅ This post
Lightsail Deployment	Simple and fast, fixed monthly cost, beginner-friendly	📝 Coming soon
AgentCore Deployment	Managed service, no infrastructure management	📝 Coming soon
EKS Deployment	Containerized, scalable, enterprise-grade	📝 Coming soon

Stay tuned for upcoming posts!

Industry Trends: The Rise of AI Agents

Beyond OpenClaw, the AI Agent space is evolving rapidly. More developers are exploring how to build autonomous, controllable AI agent systems.

One project worth watching is Hermes Agent — an open-source AI Agent framework developed by Nous Research that has been gaining increasing attention in the developer community. It offers a different approach to building and deploying AI agents.

I'll share hands-on experience with Hermes Agent in upcoming blog posts — stay tuned.

Happy Deploying! 🦞

References

Running Claude Agent SDK with Skills on Amazon Bedrock

Haowen Huang — Wed, 11 Mar 2026 08:56:47 +0000

by Haowen Huang (LinkedIn)

The Claude Agent SDK enables building multi-agent systems with Skills, MCP servers, and subagents. By default, it requires an Anthropic API key. This post shows how to run it entirely on Amazon Bedrock instead—no Anthropic API key needed.

The Problem

The DeepLearning.AI course "Agent Skills with Anthropic" assumes you have an ANTHROPIC_API_KEY. But what if you're already using AWS and want to leverage your existing Amazon Bedrock access?

The Solution: Two Lines of Code

The Claude Agent SDK uses Claude Code under the hood, which supports Amazon Bedrock via environment variables. The key configuration is surprisingly simple:

import os

# Configure Claude Code to use Amazon Bedrock
os.environ["CLAUDE_CODE_USE_BEDROCK"] = "1"
os.environ["AWS_REGION"] = "us-west-2"  # Your preferred region

That's it. Set these before creating ClaudeSDKClient, and the SDK will use your AWS credentials instead of an Anthropic API key.

Prerequisites

Before running, verify your AWS setup:

# Check AWS CLI
aws --version

# Verify credentials
aws configure get aws_access_key_id

# Test Bedrock access
aws bedrock list-foundation-models --query "modelSummaries[?contains(modelId, 'claude')]" --output table

You need:

AWS credentials configured (~/.aws/credentials or environment variables)
Amazon Bedrock model access enabled in your AWS console
IAM permissions for bedrock:InvokeModel

Complete Example

Here's the modified agent.py for Amazon Bedrock:

import asyncio
import os
from dotenv import load_dotenv
from claude_agent_sdk import (
    AgentDefinition, ClaudeSDKClient, ClaudeAgentOptions, AssistantMessage,
)

load_dotenv()
AWS_REGION = os.environ.get("AWS_REGION", "us-west-2")

# Key configuration for Bedrock
os.environ["CLAUDE_CODE_USE_BEDROCK"] = "1"
os.environ["AWS_REGION"] = AWS_REGION

async def main():
    agents = {
        "docs_researcher": AgentDefinition(
            description="Finds information from official documentation.",
            prompt="You research official docs.",
            tools=["WebSearch", "WebFetch"],
            model="haiku"
        ),
    }

    options = ClaudeAgentOptions(
        system_prompt="You are a helpful assistant.",
        allowed_tools=["Skill", "Task", "Write", "Bash", "WebSearch", "WebFetch"],
        model="sonnet",
        agents=agents
    )

    async with ClaudeSDKClient(options=options) as client:
        await client.query("Hello!")
        async for message in client.receive_response():
            if isinstance(message, AssistantMessage):
                print(message.content)

asyncio.run(main())

Adding MCP Servers (Optional)

MCP servers like Notion work the same way. Just ensure the token exists before adding the config:

NOTION_TOKEN = os.environ.get("NOTION_TOKEN")

mcp_servers = {}
if NOTION_TOKEN:
    mcp_servers["notion"] = {
        "command": "npx",
        "args": ["-y", "@notionhq/notion-mcp-server"],
        "env": {"NOTION_TOKEN": NOTION_TOKEN},
    }

options = ClaudeAgentOptions(
    mcp_servers=mcp_servers,
    # ... other options
)

What Works on Amazon Bedrock

Everything from the original SDK works:

Skills (e.g., learning-a-tool)
Subagents with parallel execution
MCP servers (Notion, etc.)
All built-in tools (WebSearch, Bash, Write, etc.)

A Note on the Debugging Process

This entire migration—from identifying the Bedrock configuration, fixing MCP server issues, to successfully running Skills with subagents—was completed through conversation with Kiro, an AI-powered IDE. No external documentation was consulted. Kiro diagnosed issues in real-time, suggested fixes, and validated the setup by running test scripts directly.

The workflow:

Asked Kiro how to use Bedrock instead of Anthropic API
Kiro checked the environment, identified missing dependencies
Kiro found the correct configuration (environment variables, not client parameters)
Fixed runtime errors (MCP config validation when token was missing)
Verified everything worked end-to-end

This demonstrates how AI-assisted development can accelerate debugging unfamiliar SDKs without documentation diving.

Conclusion

Running Claude Agent SDK on Amazon Bedrock requires just two environment variables. Your existing AWS credentials handle authentication, and all SDK features—Skills, subagents, MCP servers—work unchanged.

If you're already in the AWS ecosystem, this is the simplest path to using Claude Agent SDK without managing a separate Anthropic API key.