7 Best Code Sandboxes for AI Agents in 2026 (E2B Alternatives Compared)

Your AI agent just wrote 40 lines of Python. Someone has to run them.

It shouldn't be your production server.

The moment an agent can write and execute code, you need somewhere to run it that can't touch your secrets, your database or your other customers' data. That place is an AI agent sandbox: an isolated machine you spin up through an API, use for seconds or hours, and throw away.

If you're:

• building a coding agent or a "code interpreter" feature,
• giving an LLM a computer to analyze files and data,
• or looking for E2B alternatives because of pricing or session limits,

this comparison is for you.

Short answer: pick E2B if you want the safest default for code-interpreter agents. Pick Daytona for long-lived, stateful agent workspaces. Pick Blaxel for bursty agents that sit idle most of the time. Already on Vercel or Cloudflare? Use the sandbox your platform already ships. Need it in your own cloud? Northflank, with the caveat explained in its section.

How this comparison was made: this edition is based on each vendor's official docs and pricing pages, checked on November 2, 2026. I didn't run hands-on tests for this edition, so there are no cold-start or latency numbers here, only what each vendor documents publicly. Anything I couldn't confirm in the docs is marked as "not stated" instead of guessed.

What an AI sandbox actually does

An AI sandbox gives your agent a disposable computer. Your code calls an API, gets a fresh Linux environment, and sends it commands: install a package, run a script, read a file back.

Three things separate a good one from a risky one:

• Isolation: a microVM (own kernel) is a much stronger wall than a shared-kernel container.
• Lifecycle: how long a sandbox can live, and whether it can pause and resume without losing state.
• Billing unit: per second of vCPU and RAM, per GB of memory, or per active CPU time. Same workload, very different invoices.

The feature lists look alike.

The billing units and session limits don't.

Sandboxes run code. If your agent also has to drive a real browser, that's a different layer: see my comparison of the best browser automation APIs for AI agents.

AI agent sandboxes compared

⚠️ Northflank has an open security incident as of November 2, 2026. Details in its section.

Pricing checked on 2026-11-02. Rates are the published list rates; regions, plans and discounts change them.

What the same workload costs on each one

Per-second rates are hard to compare in your head, so here's one workload priced on all seven: one sandbox with 2 vCPU and 4 GiB of RAM, busy 1 hour a day for 30 days, CPU counted as fully busy (worst case).

Free credits aren't subtracted, since they're one-off for some vendors and monthly for others. With credits, this workload is free on E2B, Daytona and Blaxel for months.

At this size the price gap is a few dollars.

At 500 agent sessions a day, it's your margin.

The 7 best sandboxes for AI agents

1. E2B

E2B is the sandbox most AI agent tutorials reach for first. Each sandbox is an isolated machine your agent can run code in, browse from, and pause or fork, with SDKs for Python (pip install e2b) and JavaScript (npm install e2b).

Here's what the integration looks like with the Python SDK:

from e2b import Sandbox sbx = Sandbox.create() result = sbx.commands.run("python3 -c 'import platform; print(platform.platform())'") print(result.stdout) sbx.kill() 

Swap the command for whatever your agent generated, and read the files it writes back with sbx.files.read().

Pros

• $100 in credits with no credit card
• Python and JavaScript SDKs, 1–8 vCPU and 1–8 GiB per sandbox
• 24-hour sessions and 100 concurrent sandboxes on Pro

Cons

• Hobby sandboxes stop after 1 hour
• Pro is $150/month before usage, the steepest plan fee on this list

Best for: code-interpreter agents and teams that want the most widely used default.

2. Daytona

Daytona describes its sandboxes as "full composable computers" with a dedicated kernel, filesystem and network stack, and its homepage says they "run indefinitely" for long-running tasks and persistent agents. You choose between container sandboxes and full VM sandboxes with their own kernel.

Its published hourly rates match E2B's exactly ($0.0504/vCPU-h and $0.0162/GiB-h), billed per second. The difference is the starting balance: $200 in free compute, double E2B's.

Pros

• $200 in free compute, no credit card, and no documented session cap
• SDKs for Python, TypeScript, Ruby, Go and Java, the widest list here
• Per-second billing with no monthly plan fee, plus startup credits up to $50k

Cons

• The default sandbox class is a container; you have to pick the VM class for a kernel-level boundary
• No published self-serve plan tiers, just pay-as-you-go or Enterprise

Best for: agents that need a persistent workspace across many steps, and the biggest free runway.

3. Blaxel

Blaxel is built around one idea: most agent sandboxes do nothing most of the time. Its sandboxes suspend after about 5 seconds of inactivity and resume in about 25 ms, and suspended time isn't billed as compute.

Each agent runs in its own hardware-isolated microVM, and the published rate is per GB of RAM per second, with no separate vCPU charge.

Pros

• Suspend and resume in ~25 ms, so idle agents cost almost nothing
• microVM isolation, documented on the pricing page
• Up to $200 in free credits

Cons

• Billing per GB of RAM makes direct comparisons harder
• Support plans start at $800/month + 3% of usage
• Free tier caps you at 10 concurrent sandboxes

Best for: chat-style agents that run a command, wait for the user, then run another.

4. Modal

Modal is a serverless platform for Python and GPU workloads, and Sandboxes are one of its primitives. If your team already runs inference or batch jobs on Modal, the sandbox lives next to them.

Pros

• $30/month in free credits that renew every month (Starter)
• Same platform for sandboxes, GPU jobs and web endpoints
• gVisor or VM runtime, with SDKs for Python, JavaScript and Go

Cons

• Sandboxes are billed at 3x Modal's list price, the highest published vCPU rate here
• Default lifetime is 5 minutes; you raise it per sandbox, up to 24 hours
• Team plan is $250/month + compute

Best for: Python and ML teams who already use Modal and want one bill.

5. Vercel Sandbox

Vercel Sandbox gives Vercel projects an isolated environment to run untrusted or AI-generated code without leaving the platform. Usage on Pro draws from the plan's monthly $20 credit.

Pros

• Native to Vercel projects: no new vendor, no new bill
• Hobby includes 5 hours of active CPU and 5,000 sandbox creations a month
• Hobby never overcharges: sandbox creation pauses when you hit the quota

Cons

• Hobby sessions stop at 45 minutes, and the default timeout is 5 minutes
• $0.128/h of active CPU makes it the most expensive option for sustained workloads
• Hobby is capped at 10 concurrent sandboxes and 4 vCPUs

Best for: Next.js teams adding an AI feature to an app that already ships on Vercel.

6. Cloudflare Sandboxes

Cloudflare Sandboxes run on Cloudflare Containers. Each instance is a microVM with its own kernel and network, and Cloudflare bills it every 10 ms while it's actively running. For lighter jobs, Dynamic Workers run untrusted JavaScript, Python or WebAssembly without a full container.

Pros

• microVM with its own kernel, documented on the overview page
• CPU billed on active usage only, every 10 ms
• Workers Paid includes 375 vCPU-minutes and 25 GiB-hours of memory a month

Cons

• No free tier: requires Workers Paid ($5/month), plus Workers and Durable Objects usage
• Fixed instance types, so you can't size memory exactly
• The older Sandbox SDK 0.x is legacy: new projects should start on Sandboxes

Best for: agents that already run on Workers and need code execution next to them.

7. Northflank

Northflank is a general deployment platform (services, jobs, databases) with sandboxing built in. It isolates workloads with microVMs and gVisor on every plan, and it can run in your own cloud account (BYOC). Its published compute rate is the lowest on this list.

⚠️ Security incident (open as of November 2, 2026). On October 30, 2026, Northflank reported a security incident on its status page. The company says evidence points to secrets held in its control plane, and some customers reported unauthorized use of AI API keys stored on the platform. Northflank says running workloads were unaffected and that customers on dedicated control planes weren't affected. It asks all customers to rotate their secrets, and a root-cause analysis hasn't been published yet. Check the status page before you store credentials there.

Pros

• $0.01667/vCPU-h, the cheapest published compute here
• microVM and gVisor isolation on every plan, including free
• Bring your own cloud, plus a free Sandbox plan with no card

Cons

• The open security incident above
• A full platform rather than a sandbox SDK, so there's more to set up for a simple agent

Best for: teams that need sandboxes inside their own cloud for compliance, and lowest cost at scale.

Also worth a look

• Freestyle: full Linux VMs for agents that run for hours, days or weeks, with pause/resume and live cloning.
• Runloop: micro-VM sandboxes and devboxes built for coding agents.
• Sandbox0: agent sandboxes with a persistent root filesystem that pause between tasks.
• Beam: compute for agents on bare metal, with sandboxes that start in about half a second.

E2B vs Daytona vs Modal: which one should you pick?

These three come up in almost every "E2B alternatives" thread. The choice usually comes down to how long your agent lives:

• Short, one-shot code execution (an LLM runs a script and returns a chart): E2B.
• Workspaces that keep state for hours or days: Daytona, or Blaxel if the agent is idle most of that time.
• GPU work, or you already deploy on Modal: Modal.
• Your app already lives on Vercel or Cloudflare: the sandbox your platform ships.
• Compliance, or it has to run in your own cloud: Northflank (after checking the incident status).

E2B and Daytona charge the same hourly rates. The real E2B vs Daytona decision is about session limits, free credits ($100 vs $200) and the workspace model, not price.

How to choose an AI sandbox

Before you pick one, answer these five questions:

1. What's the isolation boundary? For untrusted, AI-generated code, prefer a microVM or gVisor over a plain container.
2. How long does a session need to live? 45 minutes, 1 hour and 24 hours are all real limits on this list.
3. Does your agent sit idle? If yes, suspend-and-resume billing (Blaxel) or active-CPU billing (Vercel, Cloudflare) changes the math.
4. Which language is your agent written in? Daytona has the widest SDK list (Python, TypeScript, Ruby, Go, Java); Vercel and Cloudflare are TypeScript-first.
5. What does 30 days cost at your real volume? Run your own numbers through the cost table above, then multiply by your sessions per day.

FAQ

❓ What is an AI sandbox?
✅ An AI sandbox is an isolated computing environment, usually a microVM or container created through an API, where an AI agent can run code it generated without access to your servers, secrets or other users' data.

❓ Do AI agents need a sandbox to run code?
✅ Any agent that executes code it wrote itself should run it in a sandbox. LLM-generated code can be wrong, malicious through prompt injection, or simply resource-hungry, and a sandbox contains all three.

❓ What is the best E2B alternative?
✅ It depends on why you're leaving. Daytona has the same hourly rates and double the free credits. Blaxel is cheaper for agents that sit idle. Northflank has the lowest published compute rate and runs in your own cloud.

❓ E2B vs Daytona: what's the difference?
✅ Both publish $0.0504 per vCPU-hour and $0.0162 per GiB-hour. E2B gives $100 in credits and documents Firecracker microVM isolation with 1-hour (Hobby) or 24-hour (Pro) sessions. Daytona gives $200 in credits, documents no session cap, and offers container or full-VM sandboxes for persistent, stateful agents.

❓ Is there a free sandbox for AI agents?
✅ Yes. E2B ($100), Daytona ($200) and Blaxel (up to $200) give free credits, Modal gives $30 a month, Vercel Hobby includes 5 hours of active CPU a month, and Northflank has a free Sandbox plan. Cloudflare requires the $5 Workers Paid plan.

❓ Firecracker microVM vs container: which is safer for AI-generated code?
✅ A microVM. Each one runs its own kernel, so code that escapes the process still hits a hardware virtualization boundary. Containers share the host kernel, which is a thinner wall. gVisor sits in between by intercepting system calls in user space.

Final thoughts

Every sandbox on this list can run your agent's code.

What separates them is what happens when it runs for 3 hours, sits idle for 20 minutes, or runs 10,000 times a day. Pick the billing model that matches how your agent actually behaves, not the one with the nicest landing page.

Are you a vendor in this space? This list is updated every quarter with new pricing and new platforms. If your sandbox product should be here, or something above is out of date, get in touch.

Looking for technical content for your company? I can help — LinkedIn · kevinmenesesgonzalez@gmail.com