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# Introduction
When you start allowing AI agents to write and run code, the first important question is: where can that code be executed safely?
Running LLM-generated code directly on your application servers is risky. It can leak secrets, consume a lot of resources, or break important systems, either by accident or on purpose. That's why sandboxes for agent code are quickly becoming integral parts of modern AI architectures.
With a sandbox, your agent can build, test, and delete code in an isolated environment. Once everything is working, the agent can generate a pull request for you to review and merge. You get clean, functional code, without worrying about unreliable execution affecting your real infrastructure.
In this post, we'll explore five of the best sandbox platforms for agent AIs:
- Modal
- Blaxel
- Daytona
- E2B
- Together Code Sandbox
# 1. Modal: Serverless AI Compute with Agent-Friendly Sandboxes
Modal is a serverless platform for AI and data teams. You define your workloads as code, and Modal runs them on the CPU or GPU infrastructure, scaling up as needed.
One of its main features for agents is sand boxes: secure, temporary places to run untrusted code. These sandboxes can be launched programmatically, given a live time, and automatically collapsed when idle.
What Modal offers your agents:
- Serverless containers for Python-first AI workloads, from data pipelines to LLM inference
- Sandboxed code execution so agents can compile and run code in isolated containers instead of in your main application infrastructure
- Everything is like a conceptual code which fits well with the agent workflow that dynamically generates infra and pipelines
# 2. Blaxel: Perpetual Sandbox Platform
Blaxel is an infrastructure platform that provides production-grade agents with their own computing environments, including code sandboxes, tool servers, and LLMs.
Blaxel Houses Sand boxes designed specifically for agent workloads: secure micro-VMs spin up quickly, go to zero when idle, and restart in about 25 ms even after weeks.
What Blaxel offers your agents:
- Secure, fast-booting micro-VMs by using AI-generated code with full file system and process access
- Scale to zero with a quick restartso that your agents can live longer and can “sleep” without burning money, yet feel in shape
- SDKs and tools (CLI, GitHub integration, Python SDK) to use agents and connect to Blaxel resources such as tool servers and cluster functions
# 3. Daytona: Run the AI Code
Daytona it started as a cloud-native dev environment, and then evolved into it secure infrastructure for running AI-generated code. It offers robust, scalable sandboxes designed to be used primarily by AIs rather than humans.
Daytona focuses on the rapid creation of sandboxes: sub-90 ms from “code to execution” in their marketing materials, with some sources describing secure, scalable runtimes of around 27 ms.
What Daytona offers your agents:
- Lightning fast, decent sandboxes designed for continuous agent workflow
- Secure, isolated working hoursusing Docker by default with support for robust isolation layers such as Kata and Sysbox containers
- Full program control over file operations, Git, LSP, and code execution with a clean, agent-friendly SDK
# 4. E2B: Sandbox for computer-based agents
E2B describes itself as cloud infrastructure for AI agentsoffering secure, decentralized sandboxes in the cloud that you manage with Python and JavaScript SDKs
Most people know E2B from them Code Interpreter Sandbox: a way to give your application a runtime to execute code similar in spirit to a “Code Interpreter,” but under your control and tuned to the agent's workflow.
What E2B offers your agents:
- Open source, sandboxed cloud environments to AI agents and AI-powered applications.
- Interpreter code runtime for Python and JS/TS, exposed via SDKs and CLI.
- Designed for data analysis, visualization, codegen evals, and full AI-generated applications which require a secure execution layer.
# 5. Together Code Sandbox: MicroVMs for AI Coding Products
Together the AI known for its cloud-native AI: open and specialized models, inference, and GPU clusters. On top of that they present Together Code Sandboxa microVM-based environment for building AI coding tools at scale.
Together Code Sandbox provides fast, secure code sandboxes for creating purpose-built full-scale development environments for AI. It offers groups of configurable microVMs with fast startup times, robust snapshots, and native aging tools. Developers use it to power next-generation AI coding tools and agent workflows over a fast, high-performance infrastructure.
What Coding Sandbox gives your agents:
- Quick VM creation from snapshot in ~500 ms and render new ones from scratch in less than 2.7 seconds (P95)
- Scale from 2 to 64 vCPU and 1 to 128 GB of RAM, with a variable size for heavy computing workloads
- Deep integration with Together's model library and AI-native cloudso your agents can generate and run code in one place
# How to Choose the Right Sandbox Code for Your AI Agents
All five options give agents a secure, isolated place to run the code. Choose based on what you are preparing:
- Model: The first Python platform for pipelines, batch operations, training/description, and sandbox execution in one place.
- Blaxel / Daytona: Traditional sandboxes turn quickly and can continue as a real work environment.
- E2B: Code interpreter styling with robust JS + Python SDK and open source roots.
- Together Code Sandbox: At least the best if you build serious AI code products and are already working on Together's infra.
Abid Ali Awan (@1abidiawan) is a data science expert with a passion for building machine learning models. Currently, he specializes in content creation and technical blogging on machine learning and data science technologies. Abid holds a Master's degree in technology management and a bachelor's degree in telecommunication engineering. His idea is to create an AI product using a graph neural network for students with mental illness.
