Best sandbox for Mastra agents in 2027

Q: What matters in a sandbox for Mastra?

Per-user machines that cost nothing idle. A Mastra app usually serves many users. One named sandbox per user pauses when nothing happens. It resumes with its files, memory and processes when that user returns. This keeps each user's work apart and keeps idle hours cheap. Packages without a build step. An image with Python, Node.js and the data libraries in place, where pip install works when the agent needs more. Steps that can take a while. A command runs up to the timeout you give it, so a long data job is one tool call, not a polling loop. One tool set across frameworks. Sandbox tools written as AI SDK tools serve a Mastra agent, an AI SDK route and a workflow step alike.

Q: When might another sandbox fit better?

Mastra's workspace features. If you want Mastra's workspace layer itself, with its filesystem abstraction and mounts, use one of the workspace providers Mastra lists. Runtime connects as tools instead.

Which sandbox is best for Mastra?

It depends on whether you want Mastra's workspace layer. For per-user agents added as plain tools, Runtime has the lowest cost of the sandboxes priced below: $34.17 for twenty thousand data questions, compared with $106.13 on Vercel Sandbox and $165.60 on E2B. It bills measured CPU, and each user's sandbox pauses with its memory kept. Pick a workspace provider Mastra lists, such as Vercel, Daytona or E2B, for Mastra's filesystem abstraction and mounts. Keep LocalSandbox for code you trust.

How does Mastra connect an agent to a sandbox?

Mastra has two routes. Its workspaces attach a sandbox provider to an agent with workspace: new Workspace({ sandbox }). Mastra's sandbox docs list Daytona, E2B, Modal, Vercel and Docker among the providers. Separately, any AI SDK tool can go straight into an agent's tools. It needs no workspace and no provider package, and the same tools work in a plain generateText call.

Route	Where commands run	What you add
`LocalSandbox`, no isolation	The app's own host	Nothing; Mastra calls it not secure
`LocalSandbox` with bwrap or seatbelt	The host, with writes limited	Bubblewrap or macOS Seatbelt
A workspace sandbox provider	That provider's machines	The provider's Mastra package

What matters in a sandbox for Mastra?

Per-user machines that cost nothing idle. A Mastra app usually serves many users. One named sandbox per user pauses when nothing happens. It resumes with its files, memory and processes when that user returns. This keeps each user's work apart and keeps idle hours cheap.
Packages without a build step. An image with Python, Node.js and the data libraries in place, where pip install works when the agent needs more.
Steps that can take a while. A command runs up to the timeout you give it, so a long data job is one tool call, not a polling loop.
One tool set across frameworks. Sandbox tools written as AI SDK tools serve a Mastra agent, an AI SDK route and a workflow step alike.

What does a Mastra data agent cost per month?

Twenty thousand questions, each three minutes on a 2 vCPU, 4 GiB sandbox and busy for 30 CPU-seconds, at each provider's published rates:

Provider	Isolation	CPU billed on	A month of questions
Runtime Cloud	Firecracker microVM	Measured use	$34.17
Vercel Sandbox	Firecracker microVM	Measured use	$106.13
E2B	Firecracker microVM	Every vCPU held	$165.60
Daytona	Containers by default	Every vCPU held	$165.60
Modal	gVisor, a shared kernel	The CPU requested	$237.96

When might another sandbox fit better?

Mastra's workspace features. If you want Mastra's workspace layer itself, with its filesystem abstraction and mounts, use one of the workspace providers Mastra lists. Runtime connects as tools instead.

Runtime Cloud

With Runtime, a Mastra agent gets a full Linux microVM from one line, tools: runtimeTools(sbx) from withruntime. It pays for CPU only while code runs. The four tools run commands and read, write and list files in a Firecracker microVM with its own kernel.

Sandbox.getOrCreate("user-42") gives each user one named sandbox. It pauses itself after 300 seconds with nothing happening.
The default image has Python 3.12, Node.js 24, pandas, NumPy and matplotlib.
The tools are AI SDK tools, so the same sandbox code serves a Mastra agent, an AI SDK route and a workflow step.

The twenty thousand questions above cost $34.17 on Runtime, 68% less than Vercel Sandbox and 86% less than Modal, at rates checked 25 October 2026.

A run on Runtime, recorded 1 October 2026

TypeScriptimport { Agent } from "@mastra/core/agent";import { Sandbox } from "withruntime";import { runtimeTools } from "withruntime/ai";await using sbx = await Sandbox.create();const analyst = new Agent({  id: "analyst",  name: "Analyst",  instructions: "Answer with numbers you computed in the sandbox.",  model: "anthropic/claude-sonnet-4-6",  tools: runtimeTools(sbx),});const answer = await analyst.generate("Total revenue by region in sales.csv?", { maxSteps: 6 });console.log(answer.text);

We ran this agent's generate() loop with @mastra/core 1.71.0 and the AI SDK's mock model in place of Claude. The model's two tool calls were scripted, and Mastra ran them against a live sandbox:

textruntime_write_file sales.csv   Wrote 62 bytes to /workspace/sales.csvruntime_exec python3 -c "...groupby('region').revenue.sum()..."  exitCode 0  region  east     1250  south     950  west     1500

pandas was there with nothing installed first. Per-user sandboxes and the rest of the API are in Mastra in a sandbox.

Sign in with npx withruntime login, and the agent above runs on free usage. Tools that already call the E2B, Daytona or Vercel Sandbox SDK move with the migration guide.

Sources

Checked 25 October 2026.

Mastra sandboxes: workspaces, the provider list and the LocalSandbox isolation levels
Each provider's published rates, checked 25 October 2026, as the pricing guide lists them
The recorded run: Runtime's TypeScript SDK with @mastra/core 1.71.0 and ai 7.0.87's MockLanguageModelV4, against production on 1 October 2026