Kiro vs Cursor (2026): Which AI IDE to Choose

How Kiro and Cursor differ

Kiro and Cursor are both AI coding tools built on the VS Code codebase, and both run agents that can make changes across your project. Where they differ is how much planning happens before the agent starts writing code.

Kiro is AWS's agentic coding tool, available as an IDE, a CLI, a web app, and a mobile app. Its spec-driven development turns a prompt into requirements, an architectural design, and a sequenced task list before implementing them, while hooks run tasks like unit tests or doc updates in the background. You can choose models from Anthropic, OpenAI, and open-weight providers like DeepSeek and Qwen, or let Auto mode pick one based on the task.

Cursor is an AI-native code editor built around working with the agent directly in your editor, with cloud agents, MCP support, skills, and hooks on paid plans. It offers its own Composer models alongside models from Anthropic, OpenAI, and Google, and its Teams plan adds Bugbot for agentic code reviews.

Kiro vs Cursor pricing

Both tools have a free tier and a $20 individual plan, but they meter usage differently.

Kiro's pricing runs on monthly credits. The free plan includes 50, Pro at $20 a month includes 1,000, and higher tiers go up to 10,000 credits for $200, with add-on credits at $0.04 each. Each model carries its own credit multiplier, and unused credits don't roll over to the next month.

Cursor's pricing starts with a free Hobby plan with limited agent requests, then $20 a month for an individual plan with extended agent limits and access to frontier models. Usage past the included amount continues as on-demand usage billed in arrears. Teams costs $40 per user per month and adds centralized billing, SSO, and usage analytics, while Enterprise is custom-priced with pooled usage and audit logs.

The practical difference is when you find out what a heavy month costs, since Kiro draws down a credit balance you can top up, while Cursor keeps running and bills the extra usage afterward.

Observability for the LLM apps you build with them

Kiro and Cursor help you write the code, but once your app makes its own LLM calls, those calls need to be routed and monitored in production. Respan is the AI router with built-in observability and automated evals, giving your app one OpenAI-compatible endpoint for 1,000+ models.

Each request your app makes is traced with its cost, latency, and token usage, and that production traffic feeds the evals that catch quality regressions before users do. Fallback chains and spend controls sit next to those traces, so when a provider fails or costs jump, you can see which requests were behind it.

If you're building an LLM app with either tool, Respan lets you route, observe, and evaluate every LLM call it makes, starting on the free tier. For the coding itself, Kiro fits developers who want features planned as specs with checks running in the background, while Cursor fits developers who want to iterate with the agent directly in their editor.