The depth you expect. Ease and power you didn’t.

One environment, organized around the application

AWS gives you extraordinarily powerful services. The console organizes them by service. Lambda Deploy organizes them by application.

Building an app the usual way means assembling it across separate places — the frontend here, API Gateway there, functions in a third screen (or local), the database table in a fourth, each with its own configuration to master. That knowledge is real and worth having. But your app is the thing you came here to build.

Here, the three pieces live together:

• The frontend is edited, previewed live, and published.
• The API functions behind it are coded, deployed, and tested — with API Gateway endpoints generated from the editor, not hand-built from routes, integrations, and mapping templates.
• The data those functions operate on is browsed, edited, and queried.
• The wiring between them is built/managed for you — Lambda Deploy understands your frontend, backend, and the API calls that maps them

Because the pieces are in one place, the AI can operate on all of them at once. That is the reason work moves faster here.

The toolchain, at full depth

• A professional code editor in the browser — file tree with create/rename/delete/upload, multiple tabs, project-wide search, structure view, command palette, persistent drafts.
• Real Git, per function — branch, diff, commit, history, pull, merge, discard, detach, against your own GitHub or Bitbucket repos.
• Event source triggers as first-class changes — API Gateway, schedules, SQS, DynamoDB, Kinesis, S3, SNS, configured from the editor.
• Layers — versioned shared dependencies, including one-click publish-as-layer straight from a function’s content.
• pip and npm in the browser — Python and JavaScript dependencies built directly online, with no local Docker or build environment to maintain.
• Multi-account, multi-region — switch context deliberately from any screen.
• Full function lifecycle — create, clone, copy, configure, schedule, delete.

An AI that proposes, and a workspace that refuses to lose your work

Every AI change arrives as a reviewable diff. Generated code is a proposal, applied when you approve it. Backend resources the AI wants to create are surfaced for approval before they exist.

• Diff before apply on code.
• Confirm before write on data — reads run automatically, writes and deletes always stop for an explicit gate.
• Incoming-sync detection — the workspace notices when a function or branch changed remotely while you were editing and reconciles three states: your workspace, the deployed Lambda, and the Git branch.
• Data-loss guards and change summaries before configuration saves.

Yours from the first minute

Native code on native AWS — Lambda, API Gateway, DynamoDB, RDS MySQL and Postgres, Aurora, SQS, S3, SNS, and more — in your own AWS account, against your own repos. There’s no proprietary runtime to outgrow and no migration ahead of you. Whatever a founder or a product manager started in here, you inherit as code, in a repository, on the infrastructure it already runs on.