Top AI Code Sandbox Products in 2025

Large language model (LLM) applications, and the “software agents” they power, are generating more and more new code on‑the‑fly. Cursor is writing almost 1 billion lines of accepted code each day.

Executing that code directly on your application servers is a security and reliability risk: it can expose secrets, overwhelm resources, or even escape the container. AI‑first sandboxes solve three problems at once:

1. Security isolation – containers or micro‑VMs cut the blast radius of malicious or buggy code.
2. Ephemeral scale – thousands of developer‑agent sessions can be spun up and torn down in seconds without leaving idle VMs running.
3. Observability & networking guardrails – a good provider exposes granular process logs & metrics and throttles egress

When evaluating a provider, look for (i) start‑up latency, (ii) language/runtime flexibility, (iii) per‑sandbox networking controls, (iv) price and autoscaling limits, and (v) an SDK that fits your stack.

1. Modal Sandboxes

How it works – Modal lets you define a sandbox with one line of Python and then exec arbitrary commands inside. Sandboxes inherit Modal’s serverless container fabric, so they autoscale from zero to 10,000+ concurrent units and back with sub‑second cold starts.

Strengths

• Scale & reliability: Production users such as Lovable and Quora run millions of untrusted code snippets a day without pre‑provisioning capacity.
• Flexibility: Sandbox images can be dynamically defined at runtime via Modal’s Python SDK.
• Robust networking features: Built‑in tunnelling for direct external connections and granular egress policies to lock down outbound networking.
• Code‑first DX: Python/TS/Go SDKs, no YAML, and snapshot/volume primitives that feel native to developer‑agent workflows.

Weaknesses - on‑prem deployment is not an option today.

2. E2B

An open‑source runtime purpose‑built for AI “developer agents”. A sandbox boots in less than a second and can be orchestrated from Python or JavaScript.

Strengths – OSS licence (plus a hosted SaaS option), bring‑your‑own cloud, fine‑grained filesystem API.

Weaknesses – you manage the cluster; scaling past a few hundred sandboxes means running the E2B control‑plane yourself. No built‑in outbound‑network policies or IP filtering. You have to craft & push a Docker image for every custom environment.

3. Together Code Sandbox

Together AI extends its GPU cloud with sandboxes that start a full VM from snapshot in 500 ms (2.7 s cold) and resume with memory already loaded—great for heavy IDE‑style developer agents.

• Strengths – hot‑swappable VM sizes (2‑64 vCPU), Git‑versioned storage, live preview hosts.
• Weaknesses – Docker‑based Dev‑Container images limit on-the-fly environments. No first‑class tunnels. Pricing is VM‑style (per vCPU & GiB‑RAM per minute); less attractive for bursty, sub‑minute jobs.

4. Fly Machines (DIY)

Fly.io’s Machines API spins up a micro‑VM in less than a second and exposes a REST interface. Developers often script Machines as an ad‑hoc sandbox for user code.

• Strengths – global edge regions, persistent VM option, straightforward CLI.
• Weaknesses – no sandbox‑specific features (tunnels, snapshots, per‑process logs). Networking controls and secrets management have to be layered on.

5. Daytona

Targets AI agents and eval pipelines with 90 ms sandbox creation, built‑in Git and LSP support, and a Python SDK.

• Strengths – low latency, live stream of stdout/stderr, file upload helpers.
• Weaknesses – young ecosystem; feature parity with Modal’s tunnels or Together’s snapshots still evolving.

6. Roll‑your‑own on Kubernetes

You can assemble a sandbox layer with Kubernetes + gVisor, Kata Containers, or Firecracker micro‑VMs.

• Strengths – full control, no vendor lock‑in.
• Weaknesses – steep ops burden: patch vulnerabilities, handle image caching, and wire up per‑sandbox network policies yourself. A misconfigured pod can expose the entire cluster.
• Example: Using Firecracker and Go to run short, untrusted code execution jobs

Comparative snapshot

*Prices normalized to cost per physical CPU core (=2 vCPUs) per second. Note that some providers bundle in memory while others charge for it separately.

Launch a Modal Sandbox in a few lines of code

import modal
app = modal.App.lookup("sandbox-manager", create_if_missing=True)
sb = modal.Sandbox.create(app=app)

p = sb.exec("python", "-c", "print('hello')")
print(p.stdout.read())
sb.terminate()

The Modal Sandbox shuts down automatically when your developer‑agent finishes. No YAML, no VM lifecycle headaches—just clean, scalable isolation.

Using Modal Sandboxes for your software agents

If your roadmap involves software agents that write or modify code, investing in a purpose‑built sandbox saves months of security engineering. Modal offers scaling to handle millions of executions daily, with sub‑second starts that keep your agents responsive. The built‑in networking tunnels and per‑sandbox egress policies mean your agents can safely interact with databases and APIs without exposing your entire infrastructure. Plus, the code‑first SDK integrates seamlessly into existing Python workflows—no Kubernetes manifests or VM provisioning required.

Start with Modal’s free tier and scale to tens of thousands of concurrent sandboxes when your agent platform takes off.