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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 safely execute?
Running LLM-generated code directly on your application server is risky. It can leak secrets, consume a lot of resources, or even break critical systems, whether by accident or intent. This is why agent-native code sandboxes have increasingly become an essential part of modern AI architectures.
With Sandbox, your agent can create, test, and debug code in a completely isolated environment. Once everything works, the agent can generate a pull request for you to review and merge. You get clean, functional code, without having to worry about unreliable execution touching your real infrastructure.
In this post, we will explore five major code sandbox platforms designed specifically for AI agents:
- model
- blexel
- daytona
- e2b
- code together sandbox
# 1. Model: Serverless AI Compute with Agent-Friendly Sandbox
model is a serverless platform for AI and data teams. You define your workloads as code, and the model runs them on CPU or GPU infrastructure, scaling up and down as needed.
One of its key features for agents is sandbox: Secure, ephemeral environment for running untrusted code. These sandboxes can be launched programmatically, given a time to go live, and automatically torn down when inactive.
What the model gives 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 separate containers instead of your main app infrastructure
- Everything-as-code mentality which fits well with agent workflows that dynamically generate infras and pipelines
# 2. Blaxel: The Perpetual Sandbox Platform
blexel is an infrastructure platform that gives production-grade agents their own compute environment, including code sandboxes, tool servers, and LLMs.
blexel’s sandbox Designed specifically for agentic workloads: secure micro-VMs that spin up fast, scale to zero when idle, and restart within about 25 ms, even after weeks.
What Blaxel gives your agents:
- Secure, quick-launch micro-VMs To run AI-generated code with full file system and process access
- Scale-to-zero with a fast resumeSo that your long-lived agents can “sleep” without wasting money, still feel stateful
- SDK and tools (CLI, GitHub integration, Python SDK) to deploy agents and connect to Blaxel resources like tool servers and batch jobs
# 3. Daytona: Run AI Code
daytona Started as a cloud-native dev environment, then grew into Secure infrastructure to run AI-generated code. It provides a stateful, elastic sandbox that is designed to be used primarily by AI agents rather than humans.
Daytona focuses on fast construction of the sandbox: their marketing materials describe secure, elastic runtimes of sub-90 ms “from code to execution”, with some sources reporting up to around 27 ms.
What Daytona gives your agents:
- Lightning-fast, stateful sandbox Designed for continuous agent workflow
- Secure, isolated runtimeUsing Docker by default with support for strong isolation layers like Kata containers and sysbox
- full programmatic control File manipulation, Git, LSP and code execution through a clean, agent-friendly SDK
# 4. E2B: Sandbox for Computer Use Agents
e2b describes himself as Cloud infrastructure for AI agentsOffers secure isolated sandboxes in the cloud that you control via Python and JavaScript SDKs
Many people know E2B by their name code interpreter sandbox: A way to give your app a code-running runtime similar to a “code interpreter”, but under your control and tuned to the agent workflow.
What E2B gives your agents:
- Open-source, sandboxed cloud environment For AI agents and AI-powered apps.
- code interpreter-style runtime For Python and JS/TS, exposed via SDK and CLI.
- designed for Data analysis, visualization, codegen evaluation, and full AI-generated apps Which requires a secure execution layer.
# 5. Code Together Sandbox: MicroVM for AI Coding Products
Together A.I. Known for its AI-native cloud: open and specialized models, inference, and GPU clusters. On top of that they launched code together sandboxA MicroVM-based environment for building large-scale AI coding tools.
The accompanying Code Sandbox provides a fast, secure code sandbox for building purpose-built full-scale development environments for AI. It provides teams with configurable microVMs with fast startup times, robust snapshotting, and mature dev-environment tooling. Developers use it to power next-generation AI coding tools and agentic workflows on top of scalable, high-performance infrastructure.
What the Together Code Sandbox gives your agents:
- quick vm creation Provision new from a snapshot in ~500 ms and from scratch in less than 2.7 seconds (P95)
- Scale from 2 to 64 vCPUs and 1 to 128 GB RAM, with hot-swappable sizes for compute-intensive workloads
- Deep integration with Together Model Library and AI-Native CloudSo your agents can generate and execute code on a single platform
# How to Choose the Right Code Sandbox for Your AI Agents
All five options give agents a secure, isolated location to run code. Choose depending on what you’re optimizing for:
- Modal: Python-first platform for pipelines, batch jobs, training/inference, and sandbox execution in one place.
- Blaxell/Daytona: Agent-native sandboxes that scale quickly and can persist like real workspaces.
- E2B: Code-interpreter style execution with a robust JS + Python SDK and open-source roots.
- Code sandbox together: It is best suited if you are building serious AI coding products and are already running on Together’s infra.
abid ali awan (@1Abidaliyawan) is a certified data scientist professional who loves building machine learning models. Currently, he is focusing on content creation and writing technical blogs on machine learning and data science technologies. Abid holds a master’s degree in technology management and a bachelor’s degree in telecommunication engineering. Their vision is to create AI products using graph neural networks for students struggling with mental illness.
