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Kiro

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Kiro by AWS is a developer platform that makes it easy to build, test, and scale AI agents and applications with modular tools, flexible APIs, and cloud-native deployment.

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August 22nd, 2025

About Kiro

Kiro is an AI-native development platform from AWS (Amazon Web Services) designed for building, testing, and running AI agents in production. It gives developers the infrastructure and modular components needed to create scalable, secure, and efficient AI applications without starting from scratch.

Through its intuitive developer console and APIs, Kiro unifies model access, agent orchestration, tool integration, and deployment pipelines. Developers can mix cloud-hosted models with their own, add plugins for retrieval and automation, and deploy agents directly on AWS infrastructure.

Kiro is built for production-grade performance, offering enterprise-level security, session memory, async inference, and monitoring out of the box. It also integrates with AWS services for storage, compute, and scalability, making it a natural choice for companies already in the AWS ecosystem.

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๐Ÿ’ฐ Pricing

KIRO Free โ€“ $0/mo ยท 50 vibe requests

KIRO Pro โ€“ $20/mo ยท 225 vibe requests, 125 spec requests

KIRO Pro+ โ€“ $40/mo ยท 450 vibe requests, 250 spec requests

KIRO Power โ€“ $200/mo ยท 2,250 vibe requests, 1,250 spec requests

๐ŸŒ Why Choose Kiro?

โœ… Backed by AWS reliability and cloud infrastructure
โœ… Unified environment for building, testing, and scaling agents
โœ… Works with both hosted and custom models
โœ… Secure, production-ready architecture with AWS compliance
โœ… Flexible for individuals, startups, and enterprises

๐ŸŒ Discover Kiro and thousands of other AI tools on Beyond The AI - your trusted directory for AI solutions.

Key Features

7 features
  • Unified platform for AI agent development and deployment
  • API access to multiple models and orchestration flows
  • Plugin ecosystem for search, RAG, and automation
  • Session management with both stateless and stateful agents
  • Secure architecture leveraging AWS compliance and standards
  • Cloud-native scalability for enterprise and startup use cases
  • Developer-friendly console with monitoring and debugging tools

Use Cases

5 use cases
  • Building AI agents for SaaS products and internal tools
  • Deploying customer support or workflow automation agents
  • Running RAG-powered applications with external knowledge bases
  • Creating multi-tenant AI applications with modular logic
  • Prototyping and scaling AI workflows directly on AWS infrastructure
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