> ## Documentation Index
> Fetch the complete documentation index at: https://docs.jhansi.io/llms.txt
> Use this file to discover all available pages before exploring further.

# jhansi.io vs Daytona

> Both address isolated execution for AI-generated code, but at different levels of maturity and breadth.

This is a positioning guide, not a security benchmark. Capabilities change — check both products against current documentation before making a decision.

## Comparison

| Dimension         | jhansi.io                                                      | Daytona                                                                                |
| ----------------- | -------------------------------------------------------------- | -------------------------------------------------------------------------------------- |
| Product scope     | A focused execution runtime with SDK and MCP access.           | A broader sandbox infrastructure platform with multiple SDKs and operational features. |
| Compute model     | Designed to be deployed and operated by the user.              | Supports managed services and customer-managed runner infrastructure.                  |
| Environment model | Minimal sandbox lifecycle centred on running a requested task. | Full programmable sandbox environments with broader tooling.                           |
| Maturity          | Early stage, actively developed.                               | More established with a wider feature surface.                                         |

## When jhansi.io is the better fit

* You want a small, inspectable execution layer you can understand and operate yourself.
* You need SDK and MCP access without adopting a broader platform.
* Simplicity and a narrow architectural principle matter more than feature breadth.

## When Daytona may be the better fit

* You need a more complete sandbox environment today.
* Your use case requires features beyond basic execution lifecycle.
* You want a platform with broader ecosystem support.