Getting Started with Kiro: AWS's New AI Coding Tool

AWS has quietly launched Kiro IDE, an AI coding tool that takes a fundamentally different approach from GitHub Copilot and Cursor. Instead of endless prompting until something works, Kiro introduces spec-driven development—a structured workflow that transforms your ideas into clear requirements, system designs, and implementation tasks before writing any code.

What Makes Kiro Different: Spec vs. Vibe Coding

Most AI IDEs rely on what Kiro calls “vibe coding”—you prompt, the AI generates code, you refine, repeat. This works for simple tasks but falls apart on complex projects where context gets lost and decisions aren’t documented.

Kiro’s spec-driven development changes this dynamic. When you start a spec session, the AI creates three documents:

• requirements.md: User stories with acceptance criteria in EARS notation
• design.md: Technical architecture and component relationships
• tasks.md: Discrete, trackable implementation steps

This approach shines for complex projects. Building a fractal visualization app with mathematical algorithms? The spec process organizes that complexity into manageable pieces. Creating a simple CRUD interface? You might prefer vibe mode for speed.

Core Features That Set Kiro Apart

Agent Hooks for Background Automation

Agent hooks are Kiro’s answer to repetitive development tasks. These intelligent triggers run automatically based on IDE events—saving files, creating functions, or modifying APIs.

// Example: Auto-generate unit tests on file save
// Configuration in .kiro/hooks/test-generation.yaml
{
  "trigger": "onFileSave",
  "filePattern": "*.js",
  "action": "generateTests",
  "output": "tests/${filename}.test.js"
}

Unlike manual workflows in Cursor or Windsurf, hooks operate continuously in the background. Set up a documentation hook once, and every API change gets documented automatically.

Steering Files for Persistent Context

While other AI coding tools forget your project conventions between sessions, Kiro’s steering files maintain persistent knowledge about your codebase. Store your coding standards, preferred libraries, and architectural decisions in .kiro/steering/ files that load conditionally based on file patterns.

---
inclusion: fileMatch
fileMatchPattern: "src/**/*.tsx"
---
# React component standards
- Use functional components with TypeScript
- Implement error boundaries for data fetching
- Follow atomic design principles

MCP Integration for External Tools

Model Context Protocol (MCP) integration connects Kiro to databases, APIs, and documentation systems. While still basic compared to specialized tools like Cline or Trae AI, this native support means you can query your PostgreSQL database or fetch API schemas directly within the IDE.

Current Reality: Models, Pricing, and Limitations

Model Availability

Kiro runs on Claude Sonnet 4.0 and 4.5 models. Auto mode intelligently balances tasks between them for better performance. No GPT-4 or Gemini support means less flexibility than Cursor, but it is optimized for Claude’s capabilities.

Pricing Structure

Kiro moved out of free preview on October 1, 2025, introducing credit-based plans:

• Pro: $20/month with standard limits
• Pro+: $40/month with higher limits and priority access
• Power: $200/month for enterprise-scale usage

Overages are billed at $0.04 per credit. Pricing aligns with Cursor’s tiers but emphasizes structured development over multi-model flexibility.

Known Limitations

Early adopters report several friction points:

• Terminal integration issues: Commands execute but don’t always register completion
• Limited extension support: Open VSX compatibility but no Cursor or Windsurf imports
• Access limitations: Waitlist codes required and heavy throttling during peak usage

Practical Workflow: From Prototype to Production

Here’s how a typical Kiro workflow progresses:

1. Start with vibe mode for rapid prototyping—build a working proof of concept
2. Switch to spec mode when complexity grows—let Kiro generate requirements and design
3. Configure steering files for your team’s conventions and standards
4. Set up agent hooks for testing, documentation, and code quality checks
5. Use MCP to connect production databases and APIs as you scale

This structured approach particularly benefits teams transitioning from prototypes to production systems, where documentation and consistency matter as much as working code.

When to Choose Kiro Over Alternatives

Choose Kiro for:

• Complex projects requiring clear documentation
• Teams valuing structured workflows over speed
• Codebases where context persistence matters
• Projects heavily using Claude models

Stick with Cursor or Windsurf for:

• Quick iterations and experiments
• Multi-model flexibility needs
• Established workflows with existing tools
• Projects requiring mature community support

Conclusion

AWS Kiro represents a bet that developers want more structure in their AI-assisted development. The spec-driven development approach, combined with agent hooks and steering files, creates a compelling workflow for teams building production systems.

While terminal issues and limited model support need addressing, Kiro’s foundation—turning chaotic AI coding into documented, repeatable processes—solves real problems that other AI coding tools haven’t tackled. For teams willing to adapt their workflow, Kiro offers a path from prototype to production that maintains both velocity and quality.

The preview period provides an opportunity to evaluate whether structured AI development fits your team’s needs. With AWS’s backing and direct Anthropic partnership, Kiro’s trajectory suggests continuous improvement ahead.

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