Best AI Agent Sandboxes in 2026: E2B, Daytona, Modal and GPU Sandboxes Compared

Search "agent sandbox" right now and nearly every result is the same shape: a conceptual explainer on isolation boundaries, hypervisors versus containers, and why running agent-generated code needs a security boundary at all. None of them put E2B, Daytona, Modal, Vercel, and Cloudflare next to each other, and none of them say what any of those cost today. Here are six named agent sandboxes, five built for short CPU code execution and one built around a persistent GPU VM, lined up on the things a buyer actually checks: start time, lifetime, GPU access, state persistence, SSH, pricing model, and MCP support.

TL;DR: Which Agent Sandbox Should You Actually Use in 2026?

E2B is the fastest and simplest option for short, CPU-only agent code execution; Daytona is the strongest E2B alternative once an agent needs GPU access or a direct SSH session; Spheron's Agent GPU Sandbox is the only one of the six built around a persistent GPU VM instead of a disposable snippet runner.

Spheron's Agent GPU Sandbox connects over one MCP URL and persists files, packages, and models on the VM between jobs, whether that's the H100 row above or another supported tier.

What an AI Agent Sandbox Actually Does

An AI agent sandbox is an isolated runtime an agent can execute code or shell commands in without touching the host machine, the operator's other customers, or anything outside its own boundary. That boundary is the point: an agent that can write and run arbitrary Python is only as safe as whatever it's running inside, and five of the six tools here exist specifically to make that boundary cheap and fast to spin up.

The isolation layer varies by vendor. Some run a full microVM per sandbox (Firecracker, the default for E2B). Others run inside a stricter container runtime: Modal requires its GPU Sandboxes to use runtime="gvisor" specifically, trading some microVM isolation for a lighter-weight model. The security consideration is the same either way: a sandbox has to assume the code inside it is hostile, whether that's a prompt-injected agent reading another tenant's files or a legitimate job that just goes wrong. For the deeper mechanics of how Firecracker and gVisor isolate a GPU, this blog's breakdown of E2B, Daytona, and Firecracker covers passthrough and pooling in more depth than this comparison needs.

None of those five tools give an agent a GPU it can keep for an hour. That's a different job, closer to a workstation than a snippet runner, and it's why a GPU-capable option belongs in this comparison rather than a separate one.

Quick Comparison: Best AI Agent Sandboxes in 2026

Pricing fluctuates based on GPU availability and vendor rate cards. Spheron rates referenced above are live as of 14 Nov 2026; other providers reflect their most recently published rates as of November 2026 and may have changed. Check current GPU pricing for live rates.

E2B: CPU-Only, Fastest to Start, No GPU Path

E2B's own FAQ is unambiguous: "No. E2B sandboxes are CPU-only," with no GPU option anywhere in the SDKs, CLI, API, or template definitions, and that holds even for bring-your-own-cloud deployments. If an agent's job needs a GPU for even a second, E2B's managed tier isn't the tool for it.

Where E2B wins is the CPU-only job it was built for. E2B's pricing page lists Hobby at $0 a month with a one-time $100 usage credit, up to 10 concurrently running sandboxes, and sessions up to 1 hour. Pro is $150 a month plus usage, raises concurrency to 100 (expandable to 600 or 1,100 via add-ons), and extends sessions to 24 hours. E2B's own pricing calculation doc puts compute billing at $0.000014 per vCPU-second and $0.0000045 per GiB-second of RAM, charged only while running; paused or killed sandboxes aren't billed for compute at all. That vCPU-second rate works out to roughly $0.10 an hour for a 2-vCPU sandbox, cheap enough that most teams don't think about the bill until concurrency climbs into the hundreds. It's predictable, metered, CPU-only execution with no infrastructure to manage, and it just doesn't extend to anything that needs a GPU.

Daytona: GPU Sandboxes, Token-Based SSH, Bundled MCP Server

Daytona is built explicitly around the gaps in a CPU-only tool. Its pricing page advertises "sub 90ms sandbox creation from code to execution," bills everything per second across compute, memory, and storage, with the first 5 GiB of storage free, and adds a GPU tier none of the pure CPU sandboxes have.

That same pricing page lists preemptible GPU rates, per hour, running from Nvidia RTX 4090 at $0.57, RTX 5090 at $0.74, L40S at $1.14, and RTX PRO 6000 at $1.74, up through H100 at $2.27, H200 at $2.61, AMD MI355X at $3.44, B200 at $3.59, and B300 at $4.08. Every one of those is preemptible, carrying the same reclaim risk any spot-style GPU tier does, and there's no published on-demand GPU rate on the same page.

For a team evaluating E2B alternatives because an agent needs a GPU or a terminal, Daytona is the closest like-for-like replacement here.

Modal Sandboxes: Preemptible GPUs Inside a gVisor Runtime

Modal's Sandbox product covers both CPU and GPU workloads, but the GPU path comes with a constraint: Modal's own docs state "GPU Sandboxes are only supported with runtime=\"gvisor\"," and even then, "Unlike CPU Sandboxes, GPU Sandboxes are subject to preemption." That's a different isolation posture than Daytona's bare preemptible GPU tier, since Modal's own runtime choice is what gates GPU access in the first place.

Lifetime defaults are short by design: Modal's sandbox guide says "Sandboxes have a default maximum lifetime of 5 minutes." Beyond that, Modal recommends filesystem snapshots to preserve state and resume into a new sandbox, rather than expecting one sandbox to run indefinitely.

Modal's pricing page bills per second across CPU and GPU: H100 SXM5 sandboxes run $0.001097 per second, roughly $3.95 an hour sustained; A100 80GB runs $0.000694 per second; B200 runs $0.001736 per second. The Starter plan costs $0 plus compute, with $30 a month in free credits and a cap of 10 GPU containers running concurrently. This blog's Modal alternatives roundup covers its serverless pricing and cold-start tradeoffs in more depth than this comparison needs.

Vercel Sandbox: 24-Hour Sessions, No GPU, Built for Coding Agents

Vercel Sandbox is the CPU-only option built for Vercel's own platform rather than as a standalone product. According to Vercel's sandbox pricing docs, "the limit resets every time a sandbox stops and resumes, so the total lifetime of a persistent sandbox is effectively unbounded."

Resource caps scale with plan: 4 vCPUs/8GB on Hobby, 8 vCPUs/16GB on Pro, 32 vCPUs/64GB on Enterprise. Pro billing runs $0.128 per Active-CPU-hour and $0.0212 per GB-hour of provisioned memory in the default iad1 region. No GPU option appears anywhere on Vercel's pricing or quotas pages; every billed dimension is CPU, memory, storage, network, or sandbox creation count, a fit for a coding-agent workflow already living on Vercel's platform and nothing beyond that.

Cloudflare Sandbox SDK: Durable Objects Underneath, No GPU

Cloudflare's Sandbox SDK is the odd one out here because it has no pricing page of its own. Cloudflare's own pricing docs for the Sandbox SDK state "Sandbox SDK pricing is determined by the underlying Containers platform it's built on." There's no separate GPU line item and no sandbox-specific lifetime cap published alongside it; the SDK leans on Cloudflare's existing primitives rather than its own metered dimensions. That's a fit for a team already building on Workers and Durable Objects that wants agent code execution billed the way the rest of that stack is, rather than a dedicated agent-sandbox product with its own rate card.

Spheron Agent GPU Sandbox: A GPU VM, Not a Code Snippet Runner

Spheron's Agent GPU Sandbox solves a different problem than the five tools above it. Claude, Claude Code, Cursor, Codex CLI, or any other remote MCP client connects over one URL, connect.spheron.ai/mcp, and the agent deploys its own GPU VM from there, with GPUs ranging from H100 to B300. Spheron's own docs describe the setup as taking "about 1 to 3 minutes after the instance is running," minutes where the five CPU sandboxes above measure themselves in milliseconds. That's the honest cost of provisioning an actual GPU instead of a container slice.

What the extra wait buys back is persistence the CPU sandboxes don't offer. Files, packages, and models stay on the VM between jobs instead of resetting after every execution, which matters for a fine-tune you want to resume or a model you don't want to re-download. The owner's own SSH key keeps working the whole time, so you can get onto the same box the agent is using rather than trusting its report secondhand. Commands run 10 minutes by default in the foreground, and background jobs run up to 24 hours, long enough for a full engine benchmark sweep or a profiling pass, the kind of sustained GPU work a vLLM-versus-SGLang benchmark run or a CUDA kernel debugging session actually need, not something a 5-minute timeout holds. Billing follows the GPU instance's own hourly rate until you stop or terminate it, exactly as without the sandbox attached, and every deploy, Stop, Start, and terminate shows a price preview first and waits for approval. An H100 bills at $2.98/hr on-demand as of 14 Nov 2026, whether or not an agent is driving it.

The honest limits matter too. This isn't a drop-in replacement for E2B, Daytona, Modal, Vercel Sandbox, or Cloudflare's Sandbox SDK: a 1-to-3-minute boot can't compete with a sub-second cold start for a stateless snippet, and the sandbox is single-tenant by design, built for one owner running one agent's GPU job rather than a platform serving many end users' code the way E2B or Daytona are. There's no managed multi-tenant orchestration layer on top of it either, no templates, no pooling, no per-tenant isolation controls, because that's not the product it's trying to be. For the exact setup walkthrough, including the connect command itself, this blog's guide to giving Claude Code or Cursor a real GPU covers it step by step, and Spheron's own docs carry the current limits and safeguards in full.

Which Sandbox Fits Your Agent

The right pick depends on what the agent is doing once the sandbox opens, not which tool has the best headline cold-start number.

• Multi-tenant product, short CPU code: E2B, the simplest managed option, with a $0 Hobby tier and per-second billing that stays cheap at low concurrency.
• Same job, plus GPU access, SSH, or a bundled MCP server: Daytona, which covers all three without leaving the CPU-sandbox category.
• Already building on Modal and fine with preemption: Modal Sandboxes extend naturally from functions already deployed there.
• Already building on Vercel or Cloudflare: Vercel Sandbox and Cloudflare's Sandbox SDK fold agent code execution into infrastructure you already pay for, at the cost of no GPU path on either.
• One agent that needs its own GPU for a fine-tune, a benchmark sweep, a kernel debug, or a longer eval run: Spheron's Agent GPU Sandbox, a job none of the five CPU sandboxes above it are built to do at all.

If your agent's next task outgrows a five-minute sandbox, a LoRA fine-tune, a vLLM-versus-SGLang benchmark, or a kernel debug that needs a real GPU under it, point it at a sandbox built to stay up for the whole run instead of resetting after each execution.

Connect your agent to the Agent GPU Sandbox →