Best sandbox for the Vercel AI SDK in 2027

Q: What matters in a sandbox for the AI SDK?

Any model. Tools that are ordinary AI SDK tools let Claude, GPT, Gemini and open models through the AI Gateway all use the same sandbox. A real machine. A full Linux system where pip install and npm install work when the model needs more. Chat sessions that cost nothing while idle. Each conversation can keep one sandbox that pauses between turns and wakes with its files, memory and processes. Streaming routes. streamText keeps the same tools; stop the sandbox in onFinish, and a time limit stops it if the client disconnects. Harness agents too. The SDK's HarnessAgent takes a sandbox adapter, with ports for bridge harnesses such as Claude Code and Codex.

Q: When might another sandbox fit better?

No sandbox at all. If the model only needs shell-like text tools over files your app already holds, just-bash runs in your process with nothing to create.

Which sandbox is best for the Vercel AI SDK?

It depends on whether the model needs a real machine. For chat turns that mostly wait on the model, Runtime costs least: $53.47 a month for the turns priced below, against $159.11 on Vercel Sandbox and $276.00 on E2B, with any model. Pick just-bash if text tools over your app's own files are enough, a hosted code tool if you stay with OpenAI or Anthropic, or Vercel Sandbox for the SDK's built-in tools.

How does the AI SDK connect an agent to a sandbox?

The SDK runs tools in your own process unless a tool sends the work elsewhere. Its tools registry and harness docs list these ways to run code:

Route	Where code runs	Models
`@ai-sdk/sandbox-just-bash`	A simulated bash in your process	Any
`openai.tools.codeInterpreter()`	OpenAI's hosted container	OpenAI
`anthropic.tools.codeExecution_20260120`	Anthropic's hosted container	Claude
`code-execution`, `bash-tool`, Vercel's own	Vercel Sandbox	Any
`bedrock-agentcore`	AWS Bedrock's code interpreter	Any

just-bash describes itself as "a simulated bash environment with virtual filesystem", so it cannot install a package or run a real binary. A provider's own code tool ties the agent to that provider's models.

What matters in a sandbox for the AI SDK?

Any model. Tools that are ordinary AI SDK tools let Claude, GPT, Gemini and open models through the AI Gateway all use the same sandbox.
A real machine. A full Linux system where pip install and npm install work when the model needs more.
Chat sessions that cost nothing while idle. Each conversation can keep one sandbox that pauses between turns and wakes with its files, memory and processes.
Streaming routes. streamText keeps the same tools; stop the sandbox in onFinish, and a time limit stops it if the client disconnects.
Harness agents too. The SDK's HarnessAgent takes a sandbox adapter, with ports for bridge harnesses such as Claude Code and Codex.

What does an AI SDK chat app cost per month?

Fifty thousand chat turns, each two minutes on a 2 vCPU, 4 GiB sandbox and busy for 10 CPU-seconds, at each provider's published rates:

Provider	Isolation	CPU billed on	A month of turns
Runtime Cloud	Firecracker microVM	Measured use	$53.47
Vercel Sandbox	Firecracker microVM	Measured use	$159.11
E2B	Firecracker microVM	Every vCPU held	$276.00
Daytona	Containers by default	Every vCPU held	$276.00
Modal	gVisor, a shared kernel	The CPU requested	$396.60

When might another sandbox fit better?

No sandbox at all. If the model only needs shell-like text tools over files your app already holds, just-bash runs in your process with nothing to create.

Runtime Cloud

With Runtime an AI SDK agent gets a full Linux microVM from one line, tools: runtimeTools(sbx), with any model the SDK can call. The four tools run commands and read, write and list files in a Firecracker microVM with its own kernel. They are plain AI SDK tools, so changing models is a one-word change. The same machine is also a sandbox for the SDK's HarnessAgent: createRuntimeSandbox() from withruntime/ai-harness, with ports for bridge harnesses such as Claude Code and Codex (guide).

The machine. Ubuntu 24.04 with sudo, Python, Node.js, Bun and gcc, where pandas, NumPy and matplotlib are already installed.
Chat sessions. Sandbox.getOrCreate("chat-42") gives each conversation one named sandbox that pauses itself after 300 seconds with nothing happening and wakes on the next turn.
Cost. CPU is billed on measured use. The fifty thousand turns above cost $53.47 on Runtime, 66% less than Vercel Sandbox and 81% less than E2B, at rates checked 25 October 2026.

A run on Runtime, recorded 2 October 2026

TypeScriptimport { generateText, stepCountIs } from "ai";import { Sandbox } from "withruntime";import { runtimeTools } from "withruntime/ai";await using sbx = await Sandbox.create();const { text } = await generateText({  model: "anthropic/claude-sonnet-4.6",  tools: runtimeTools(sbx),  stopWhen: stepCountIs(10),  prompt: "Print the first 15 Fibonacci numbers with Node.",});console.log(text);

We ran this generateText loop with ai 7.0.87 and the SDK's mock model in place of Claude, so the model's two tool calls were scripted and the AI SDK ran them against a live sandbox:

textruntime_write_file fib.mjs   Wrote 132 bytes to /workspace/fib.mjsruntime_exec node fib.mjs && node --version  exitCode 0  0 1 1 2 3 5 8 13 21 34 55 89 144 233 377  v24.21.0

Node.js 24 was there with nothing installed first. Streaming routes, chat sessions and spending limits are in code execution for the AI SDK.

Try it free after npx withruntime login. An app that already calls another sandbox moves over with the migration guide.

Sources

Checked 25 October 2026.

AI SDK tools registry: the code execution and sandbox tools and where each runs
AI SDK HarnessAgent: the sandbox adapters and what bridge harnesses need
just-bash: its own description
Each provider's published rates, checked 25 October 2026, as the pricing guide lists them
The recorded run: Runtime's TypeScript SDK with ai 7.0.87's MockLanguageModelV4, against production on 2 October 2026