AI Code Interpreter Sandbox Solution Selection Decision Assistant

You are an expert in secure code execution environments for AI agents. I need you to help me evaluate and compare sandboxing solutions for running LLM-generated code. ## Context I am building: [describe your AI agent/application] My requirements: - Language support needed: [Python/JS/multi-language] - Latency tolerance: [microseconds/milliseconds/seconds] - Security level: [hobby project/production/enterprise] - Deployment: [local/cloud/edge] ## Analysis Framework For each solution category (container-based, microVM, embedded interpreter, WASM), evaluate: 1. **Startup Latency**: Cold start time, warm start time 2. **Security Boundary**: What can escape? What is the blast radius? 3. **Resource Overhead**: Memory, CPU, disk footprint 4. **Language Support**: What can run inside? 5. **State Management**: Can you snapshot/restore execution state? 6. **Integration Complexity**: Lines of code to integrate, dependencies 7. **Production Readiness**: Maturity, community, maintenance ## Output Format Provide: 1. A comparison matrix table 2. Recommended solution with reasoning 3. Architecture diagram (as ASCII or Mermaid) 4. Sample integration code for the recommended solution 5. Security checklist before going to production Be specific with numbers (latency in ms, memory in MB). Reference real tools (E2B, Microsandbox, Pydantic Monty, Firecracker, gVisor, WASI) where applicable.