Nodus vs E2B: which sandbox should AI agents use?

E2B is the more complete sandbox product, with a Firecracker microVM for every sandbox and pauses that keep running processes alive. Nodus Sandboxes fit better when the agent that runs code also trains or evaluates models, because sandboxes and GPU jobs share one account and one budget. At listed rates, a 2 vCPU, 4 GiB sandbox on Nodus also costs about a fifth of what it does on E2B.

Nodus is our product, so read this as a vendor's comparison and check the links: each E2B claim points to E2B's own pricing page or docs, and the arithmetic is shown. There is also a section on where E2B is the better pick.

Sources: E2B pricing, billing and limits, security, sandbox lifetime, persistence, internet access and public URLs; the Nodus pricing page, Sandboxes guide and Sandbox isolation. Checked October 14, 2026.

E2B draws the boundary at the hypervisor. Each sandbox is a Firecracker microVM with its own kernel, and sandboxes never share a kernel, filesystem or memory with another customer's.

Nodus stacks several layers instead. System calls go through a user-space kernel that passes only a small, filtered set to the host. Each sandbox gets its own network namespace with a default-deny firewall that always blocks private ranges and cloud metadata addresses.

A cgroup holds CPU and memory to what you asked for and caps processes at 256. Commands run as a non-root user with no Linux capabilities.

The biggest difference is tenancy. A Nodus machine serves one organization at a time, and an empty machine is destroyed after 10 minutes rather than handed to someone else. Code that escaped a Nodus sandbox would land on a machine holding only your own work.

On paper a microVM is the stronger single boundary. If your security review asks for hardware virtualization by name, that settles it in E2B's favor.

This is where the two products behave most differently, and it is the failure you will actually hit. Agents start sandboxes and then crash, or sit waiting on a human.

E2B's timeout is a clock that starts at creation, defaults to 5 minutes, and restarts when you call set_timeout(). When it runs out, the default is to kill the sandbox, and a killed sandbox cannot be resumed. An agent that spends six minutes cloning a repo and installing dependencies with default settings loses the sandbox at minute five.

Nodus counts idle time instead. A running command, even a silent one, keeps the sandbox busy. After 5 minutes with no command, file request or terminal input, it stops, saves /workspace and stops charging for compute. The next command starts it again with the files back.

Here is what each product's documented defaults do in three common cases:

Our rule for both: set the end-of-life behavior explicitly on day one. On E2B, create sandboxes with lifecycle={"on_timeout": "pause"} and a timeout longer than your slowest step, since a paused sandbox is kept until you kill it. On Nodus, set maxLifetime to the longest a session should last, plus a maxCostUSD cap, so a forgotten sandbox ends on a date and a dollar amount you chose.

Nodus is cheaper at listed rates, by roughly 4.6 times for E2B's default size of 2 vCPU and 4 GiB. E2B charges $0.000014 per vCPU-second and $0.0000045 per GiB-second. For 2 vCPU and 4 GiB that is 2 x $0.0504 + 4 x $0.0162 = $0.1656 per hour. The Nodus pricing page lists the same size at $0.0357 per hour.

Here is what a month of busy sandbox time costs at each rate. The Pro column adds E2B's $150 plan fee, which you need for more than 20 sandboxes at once or for runs over an hour without pausing.

Prices checked October 14, 2026, on the E2B pricing page and the Nodus pricing page.

Two things the table leaves out. Both free credits are one-time, so we ignored them. Stopped Nodus sandboxes keep their files as storage, while E2B includes sandbox storage in every plan at no extra cost.

On either product, the cheapest sandbox-hour is the one you do not run. Both stop charging for compute when a sandbox is paused or stopped, so stop sandboxes that sit waiting on a model or a person.

On E2B, no. Its pricing page says sandboxes are CPU-only and there are no GPU sandboxes, so GPU work needs a second platform with its own keys and its own bill.

Nodus has no GPU sandboxes yet either, but the same account runs GPU Jobs and, in Beta, training. An agent can test generated code in a Sandbox, then price a capped GPU run before starting it:

nodus run --dry-run --gpu L4 --max-cost 2 --timeout 30m -- python train.py Enter fullscreen mode Exit fullscreen mode Without --dry-run, the same command starts the Job.

Reinforcement learning is where this pays off most. A Nodus TrainingJob (Beta) runs program graders in grading Sandboxes that Nodus creates for it, with no network access, and their cost counts toward the TrainingJob's own maxCostUSD. Generated code, grading and GPU training then sit under one budget instead of two. We cover how to set those limits in how to stop an AI agent from overspending on GPUs.

Yes, from different directions. E2B is built to host the agent, with guides for running Claude Code, Codex and Amp inside a sandbox. Its MCP gateway gives code in the sandbox access to tools from the Docker MCP Catalog.

Nodus starts from the agent you already run on your laptop. nodus mcp install claude (or cursor, or codex ) adds the Nodus MCP server to its config. The server's exec and files tools run commands in a Sandbox, up to 300 seconds per call, and read and write its files. The setup is in how to give Claude Code, Codex or Cursor GPUs with MCP.

E2B is the better choice in these cases:

If your agents only run code, never touch a GPU and the sandbox bill is small, E2B is the safe default. Nodus earns its place when the bill grows or GPU work enters the picture.

The calls map almost one to one. Here is a typical E2B snippet that writes a file, runs it and cleans up:

from e2b import Sandbox sbx = Sandbox. create ( timeout = 600 ) sbx. files. write ( " /home/user/solution.py ", code ) result = sbx. commands. run ( " python /home/user/solution.py " ) print ( result. stdout ) sbx. kill () Enter fullscreen mode Exit fullscreen mode The Nodus version, after pip install nodus-compute and nodus login:

import nodus sb = nodus. Sandbox. create ( cpu = 2, memory = " 4Gi ", idle_timeout = " 5m ", max_cost = 1 ) try: with sb. open ( " /workspace/solution.py ", " w " ) as f: f. write ( code ) p = sb. exec ( " python ", " /workspace/solution.py " ) print ( p. stdout. read ()) p. wait () finally: sb. terminate () Enter fullscreen mode Exit fullscreen mode Two changes catch people out. The working directory is /workspace, not /home/user. Outbound network is off, so code that runs pip install needs network=nodus.Egress.open() in create.

On Nodus, create a Sandbox with the same name and the same spec, and you get the existing one back, started again if it was stopped. On E2B, keep the sandbox ID and call Sandbox.connect(id), which also resumes a paused sandbox.

On Nodus, send the SHA-256 of the version you read as expectedSHA256 with the file write. If someone changed the file in the meantime, nothing is written and you get 412 SHA256Mismatch, so you can re-read and merge.

It snapshots its filesystem and stops with the reason InsufficientCredits. Once you add credits, the next command or file request starts it again with /workspace intact.

A few seconds. The first request answers 503 SandboxStarting with a 2-second Retry-After, and the CLI and SDK wait and retry for you.

More guides like this on the Nodus Compute blog.