Choosing the Right Solution for Claude Code with Amazon Bedrock
A guide explaining different approaches to setup Claude Code on AWS tech stack
Co-written by: Gili Nachum, Pr. AI Solutions Architect | Hamza Usmani, Sr. GTM SSA AI/ML GenAI | Jeffrey Zeng, WW Specialist SA – GenAI | Amit Kumar Agrawal, Sr WW Specialist - GenAI
Multiple solutions exist for integrating Anthropic’s Claude Code with Amazon Bedrock, each designed for different organizational needs and scales. This guide will help you select the approach that best fits your requirements.
How to use this guide:
• Ready to choose? Use the comparison table below to select your solution
• Need to understand trade-offs first? Read Claude Code deployment patterns and best practices with Amazon Bedrock for detailed guidance on authentication, infrastructure, and monitoring decisions
• New to Claude Code on Bedrock? Start with Why Run Claude Code, Cursor, and other AI Coding tools on Amazon Bedrock
Quick Reference Comparison table
Solution Name | Best For | Setup Time | Cost Tracking | Key Feature |
|---|---|---|---|---|
Quick Start | Individual developers, small teams, proof-of-concept deployments | Under 30 minutes | Basic (session-level /cost command only; enhanced with Application Inference Profiles) | Minimal setup with AWS CLI |
Multi-Provider & Flexible Architecture | Organizations using multiple AI vendors or requiring quota enforcement | Moderate (requires AWS expertise) | Aggregate tracking with optional per-team/project tagging | LiteLLM integration for multi-vendor API management and quota enforcement |
Enterprise SSO & Secure Distribution | Large enterprises with hundreds to thousands of developers with strict governance | 2-3 hours initial deployment | OpenTelemetry/CloudWatch monitoring with per-user dashboards | Enterprise SSO integration via OIDC federation (Okta, Azure AD, Auth0) |
Application Inference Profiles (Optional Enhancement) | Precise cost tracking and chargeback models in AWS cost explorer | Minimal ( profile creation) | Fine-grained cost tagging in AWS Cost Explorer | Transparent cost allocation in AWS billing (works with any solution above) |
Key Considerations for Choosing a Solution
Before choosing a solution, evaluate your organization across these dimensions:
- Team Size & Scale: Are you supporting a handful of developers for pilots, or hundreds across your enterprise?
- Authentication Requirements: Do you need enterprise SSO integration with your corporate identity provider, or is basic AWS credential management sufficient?
- Cost Management: Do you require granular cost allocation per user, team, or project for chargeback models?
- Quota Management: Do you require the ability to cap usage per developer or team to control spending and prevent quota exhaustion?
- Governance & Compliance: Does your organization have strict security, auditability, and compliance requirements?
- Administrative Capacity: What level of setup complexity and ongoing maintenance can your team handle?
For comprehensive discussion over the above dimensions visit the blog post: Claude Code deployment patterns and best practices with Amazon Bedrock .
Solution Comparison
1) Quick Start: Individual Developers & Pilots
Best for: Individual developers, small teams, proof-of-concept deployments
Characteristics:
- Minimal setup time (under 30 minutes)
- Requires AWS CLI configuration with standard credentials or IAM Identity Center
- Basic cost tracking via session-level /cost command only. Can achieve cost allocation in AWS billing when combined with Application Inference Profiles .
- No built-in per-user cost allocation or rate limiting
- Manual distribution - each developer configures individually
When to choose: You want to quickly test Claude Code with Bedrock, validate the value proposition, or enable a small team without complex IT integration.
2) Multi-Provider & Flexible Architecture
Best for: Organizations using multiple AI vendors or requiring architectural flexibility or already standardized on LiteLLM
Solution: Scale Claude Code to hundreds of developers using Amazon Bedrock with self-managed usage limits
Characteristics:
- Supports integration with LiteLLM for multi-vendor API management
- Self-defined usage policies using CloudWatch alarms and AWS Budgets
- AWS SSO or Bedrock API Keys for centralized access management
- Aggregate cost tracking with optional per-team/project tagging
- Maximum control over quotas without vendor-imposed caps
- Moderate complexity requiring AWS expertise
When to choose: You already use LiteLLM in your organization, need to connect to multiple AI API vendors (not only Bedrock), want control over usage policies, or are scaling beyond initial pilots but aren't ready for full enterprise deployment.
3) Enterprise SSO & Secure Distribution
Best for: Large enterprises with hundreds to thousands of developers requiring strict governance
Characteristics:
- Enterprise SSO integration via OIDC federation (Okta, Azure AD, Auth0)
- Automatic temporary credential management - no static keys
- Platform-specific installers (Windows, macOS, Linux) with one-click setup
- Optional OpenTelemetry/CloudWatch monitoring with per-user dashboards
- Self-service distribution portal with SSO login
- Comprehensive security and auditability
- Higher setup complexity (2-3 hours initial deployment of multiple components within a VPC)
When to choose: You need OIDC-based authentication with your corporate identity provider, have strict security and compliance requirements, want seamless distribution to large developer populations, or require detailed usage monitoring and attribution.
Enhancement option: Granular Cost Allocation in AWS Billing
Best for: Organizations requiring precise cost tracking and charge back models
Characteristics:
- Fine-grained cost tagging per user, team, or project in AWS Cost Explorer
- Works with any authentication method from other solutions
- Simple profile creation via AWS CLI
- Minimal end-user complexity (just environment variable update)
- Enables monitoring and budget alerts per profile
- No direct rate limiting but facilitates cost-based governance
When to choose: You need transparent cost allocation for chargeback models, want to track AI spending by department or project, or need to identify cost outliers quickly. This approach should be combined with one of the other solutions for complete functionality.
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