# top 5 sandboxes

Every sandbox provider says the same thing: run untrusted code safely.

That is the floor. It is not the question anymore.

The real question is which sandbox can survive what agents actually do. A serious agent does not just run a Python snippet and return stdout. It clones repos, installs dependencies, starts services, opens browsers, writes files, runs tests, uses databases, keeps state between turns, gets stuck, needs debugging, and sometimes needs to fork the whole world so it can try three approaches at once.

That is what "powerful" means in this ranking. Not popularity. Not how nice the SDK looks in the happy path. Raw sandbox power for agent products means:

• full operating-system semantics
• durable or recoverable state
• long-running workloads
• background processes and services
• snapshots, forks, or repeatable images
• SSH or an equivalent debugging escape hatch
• network controls
• enough flexibility to run the software the agent discovers it needs

By that definition, Freestyle is the undisputed most powerful sandbox platform for AI agents. For heavy production workloads, Freestyle VMs are the only option.

Freestyle is the platform you choose when the sandbox is too important to be a side feature. It is built for teams that expect their agents to run real software, serve real users, and stay useful after the first demo is over.

The rest of the market is still useful. E2B is excellent for code interpreters. Daytona is a serious sandbox platform. Vercel Sandbox is a clean fit for Vercel-native agent products. Modal Sandboxes are strong when sandboxing is one part of a broader serverless compute system.

But if the workload is a real machine-shaped workload, the ranking is clear.

1. Freestyle VMs

Freestyle VMs are full Linux virtual machines built for agent products, app builders, browser agents, hosted development environments, and generated code. They are not a simulated shell, a narrow code runner, or a container API with a few extra methods. They are machines.

That matters because agents eventually need machine behavior. They need apt. They need package managers. They need long-lived processes. They need ports. They need SSH. They need background services. They need a browser that stays open. They need a database running next to the app. They need to come back tomorrow and find the workspace where they left it.

Freestyle starts from that assumption instead of discovering it later.

Freestyle gives teams the most headroom. You can start with a simple isolated code runner and grow into a full app-builder runtime without migrating away from the primitive underneath. You can support a small interpreter today, a multi-service workspace tomorrow, and a long-running production agent after that.

Freestyle also gives agents the most honest interface: Linux. That is a huge advantage. Agents already know how to use files, commands, package managers, ports, processes, logs, and Git. Freestyle lets them use those tools directly instead of forcing every workflow through a narrow provider-specific abstraction.

The Freestyle VM docs describe the core difference plainly: Freestyle VMs are full Linux environments with low-level access, including SSH, systemd, multiple users and groups, and configurable networking. They can run any Linux-compatible software, including containers or multiple containers inside one VM.

That is the difference between a sandbox that runs commands and a sandbox that can host a product.

Why Freestyle is the most powerful sandbox

Freestyle wins on the primitives that matter once the sandbox is part of your user experience.

• Full Linux VMs with root, SSH, systemd, multiple users and groups, and configurable networking.
• Memory-preserving suspend and resume, so a VM can hibernate while idle and continue from the same CPU and memory state.
• Sub-100ms resume from suspend, according to the VM lifecycle docs.
• Storage-only billing while suspended, so long-running sessions do not have to burn compute while nobody is using them.
• Live forking, so a running VM can be copied into independent machines without noticeably pausing the original.
• Snapshots that capture the machine state and can create new VMs from a known point.
• Layered VM specs and caching, so common base environments do not have to rebuild every time.
• Production VM patterns with persistent storage, disabled idle timeout, and readiness signaling.

Freestyle is fast enough to use like elastic infrastructure and complete enough to trust like a real machine. That combination is rare. Most products make you choose between fast startup and real operating-system behavior. Freestyle gives you both.

Freestyle is also unusually flexible. It does not care whether the agent is running Python, Node, Bun, Ruby, Java, a browser, a database, a dev server, a test suite, or a custom binary from a package manager. If it runs on Linux, it belongs in a Freestyle VM.

Freestyle is especially strong for teams building products, not demos. Demos can tolerate fake persistence, missing services, and one-off rebuilds. Production users cannot. Freestyle is built for the production version of the agent workload, where state, latency, debuggability, and operational control all matter at once.

This is why the "sandbox" framing undersells Freestyle. A Freestyle VM can be a sandboxed code interpreter, but it can also be the user's entire development machine. It can run the app, the database, the worker, the browser, the agent loop, and the terminal. It can fork that complete state. It can suspend that complete state. It can expose just enough of that machine to the user or frontend through scoped sessions.

Most sandbox platforms become awkward when the environment itself becomes the product. Freestyle is designed for exactly that.

Why builders choose Freestyle

Builders choose Freestyle because it keeps the infrastructure model simple while the product gets more ambitious. The unit of work is a VM. The interface is Linux. The escape hatch is SSH. The durability story is explicit. The scaling story is programmable.

Freestyle makes agent infrastructure feel less like a maze of special cases. You do not need one product for code execution, another for previews, another for terminals, another for background services, another for persistence, and another for debugging. Freestyle puts those pieces inside one coherent machine model.

Freestyle is also the safest long-term bet for agent companies that do not know exactly what their agents will need next quarter. Agents are getting more capable, not less. The right infrastructure choice is the one that gives them more room to work inside boundaries you control.

That is why Freestyle is the strongest sandbox choice for serious builders. It is practical on day one, but it does not collapse when the workload grows up.

Best fit

Use Freestyle when the workload is open-ended, stateful, multi-service, user-visible, or production-shaped.

That includes AI app builders, coding agents, browser agents, cloud IDEs, code interpreters with persistent sessions, long-running research agents, test runners that need real browsers, and products where a user and an agent share the same environment over time.

If the agent needs a real computer, Freestyle is the answer.

Tradeoff

Freestyle gives you a machine. If all you need is "run this short Python snippet and delete everything," a narrower code interpreter sandbox might be enough.

That is not a weakness. It is the point. Freestyle is built for the moment your sandbox stops being a toy execution cell and becomes infrastructure.

2. Daytona Sandboxes

Daytona Sandboxes are the closest philosophical neighbor to Freestyle in this list. Daytona describes sandboxes as full composable computers for AI agents, with isolated runtime environments that include dedicated kernel, filesystem, network stack, vCPU, RAM, and disk.

That is a serious model. Daytona is not just selling "eval this code." It is building toward programmable computers for agents, with lifecycle controls, resource configuration, SDKs across multiple languages, team workflows, network controls, and managed sandbox operations.

Daytona is powerful because it understands that agents need more than a command runner. Its docs cover installing packages, running servers, compiling code, managing processes, pausing, archiving, recovering, resizing, forking, snapshots, and GPU sandboxes.

Where Daytona is strong

Daytona belongs near the top because it exposes a broad agent sandbox platform rather than a narrow interpreter.

• Isolated environments with dedicated kernel, filesystem, network stack, vCPU, RAM, and disk.
• Python, TypeScript, Ruby, Go, Java, CLI, and API support.
• Resource configuration and lifecycle policies.
• Archive and recover for longer-term sandbox state.
• Network controls and per-sandbox firewall rules.
• GPU sandbox support for teams that need accelerator-backed environments.
• Experimental pause, fork, and snapshot APIs.

That combination makes Daytona a credible choice for teams that want an agent sandbox platform with broad SDK support and organization-level workflow controls.

Why Freestyle ranks higher

Daytona's most VM-like advanced features are still marked experimental in its docs. Pause, fork, snapshot creation, and GPU sandboxes all come with experimental or access-gated language.

Freestyle puts those ideas at the center of the VM model. Suspend, resume, snapshots, live forking, systemd, SSH, multiple users, persistent VM patterns, and layered caching are not side quests. They are the platform.

Daytona is a strong #2. Freestyle is stronger for production workloads because the full-machine lifecycle is the core abstraction.

3. E2B

E2B is one of the strongest agent sandbox products if your workload is primarily code execution. It has a polished developer experience, mature JavaScript and Python SDKs, filesystem APIs, command execution, terminals, templates, pause and resume, snapshots, and a clear code-interpreter story.

E2B deserves a high ranking because it does the common agent sandbox job very well. If your product shape is "the agent needs to safely run code, inspect files, stream command output, maybe preserve a session, and return results," E2B is a natural fit.

The E2B snapshot docs are especially important. E2B snapshots can capture filesystem and memory state from a running sandbox, then spawn new sandboxes from that same state. Pause/resume handles the one-to-one case where the same sandbox should come back later.

Where E2B is strong

E2B is strong when the sandbox is a tool inside the agent loop.

• Agent-focused SDKs.
• Templates for repeatable environments.
• Filesystem APIs and command execution.
• Background commands, streaming, and interactive workflows.
• Pause/resume for returning to a sandbox.
• Snapshots for reusable checkpoints.
• Volumes, MCP gateway support, and common code-interpreter building blocks.

This is a good shape for code interpreters, data analysis agents, notebook-like products, and task sandboxes where the environment exists to run code and return artifacts.

Why Freestyle ranks higher

E2B is optimized around a sandbox abstraction. Freestyle is optimized around a full VM abstraction.

That distinction matters when the agent is no longer just using the sandbox as a tool. If the environment needs to become a user-facing dev machine, run multiple services under systemd, model multiple Linux users, accept SSH as a normal debugging path, hibernate overnight with memory intact, or fork a running browser session into several independent futures, Freestyle has the stronger primitive.

E2B is very good at code execution. Freestyle is better when code execution turns into a real product runtime.

4. Vercel Sandbox

Vercel Sandbox is an ephemeral compute primitive for safely running untrusted or user-generated code on Vercel. It is built for AI agents, code generation, developer experimentation, live previews, and isolated tests.

The important detail is that Vercel Sandbox runs each sandbox in a Firecracker microVM with its own filesystem and network. That gives it a stronger isolation story than a basic container-only model, while keeping a tight integration with Vercel's platform, authentication, preview workflows, and developer experience.

Vercel Sandbox is especially compelling if your product already lives on Vercel and your agent workload is tied to frontend generation, previewing, or isolated execution around a Vercel app.

Where Vercel Sandbox is strong

Vercel Sandbox has a clean product shape for Vercel-native teams.

• Firecracker microVM isolation.
• TypeScript and Python SDK support.
• Node.js and Python runtimes.
• Sudo access inside an Amazon Linux 2023 environment.
• Port exposure for live previews.
• Network policies and credential brokering in the SDK.
• Snapshots for capturing filesystem and installed package state.
• Strong alignment with Vercel app-builder and preview workflows.

The Vercel SDK reference includes network policy controls, port domains, and snapshots. The pricing and limits page says sandboxes can scale to 8 vCPUs with 2 GB of memory per vCPU, with a maximum runtime duration of 5 hours for Pro and Enterprise plans.

Why Freestyle ranks higher

Vercel Sandbox is intentionally ephemeral. The runtime limit is a feature for many use cases, but it is also the ceiling for long-running production workloads. Vercel is a great choice when you want isolated execution attached to the Vercel workflow. It is not the strongest choice when the sandbox needs to be the durable home of an agent, a user workspace, or a multi-service environment that persists across days or weeks.

Freestyle gives you the heavier machine model: persistent VMs, suspend/resume with memory preserved, live forking, systemd services, multiple users, SSH, and a production VM lifecycle.

For Vercel-native preview tasks, Vercel Sandbox is strong. For heavy production workloads, Freestyle VMs are the only option.

5. Modal Sandboxes

Modal Sandboxes are secure containers for executing untrusted user or agent code on Modal. Modal is broader than sandboxes: it is a serverless compute platform with functions, images, volumes, secrets, GPUs, queues, web endpoints, and deployment workflows.

That breadth is why Modal belongs on this list. If your sandbox workload is part of a larger compute system, Modal gives you a lot of surrounding infrastructure. You can create sandboxes from images, attach volumes and secrets, run commands, detach and reconnect by ID, use custom images, tag sandboxes, and integrate with the rest of Modal's compute stack.

Where Modal is strong

Modal is powerful when sandboxing is one part of a serverless compute product.

• Secure containers for untrusted code.
• Custom images and dependency setup.
• Volumes and secrets.
• Reconnectable sandbox IDs.
• Sandbox names and tags for coordination.
• Integration with Modal's broader compute, GPU, and deployment platform.
• Good fit for time-bounded jobs, generated-code execution, and pipelines.

Modal is also attractive if you already run workloads on Modal. In that case, using Modal Sandboxes can be operationally simpler than adding a separate agent sandbox provider.

Why Freestyle ranks higher

Modal's sandbox model is container-shaped and job-shaped. That is great for many workloads. It is weaker when the problem is a long-lived, user-visible, full-machine workspace.

Freestyle gives agents a real Linux VM with low-level system access, memory-preserving hibernation, live forking, SSH, systemd, users, and production persistence. Those are the primitives you want when the sandbox is not just a job runner but the place where the work happens.

Modal is a strong sandbox inside a powerful compute platform. Freestyle is the stronger sandbox platform for agent work that needs to feel like a real computer.

Honorable mention: Cloudflare Sandboxes

Cloudflare Sandboxes are worth watching. They are built on Cloudflare Containers and expose a TypeScript-first SDK for running commands, managing files, running background processes, and exposing services from Workers applications.

Cloudflare's edge-native story is compelling. If your product is already deeply tied to Workers and Durable Objects, Sandboxes can be a natural fit.

The reason Cloudflare is not in the top five for heavy production workloads is state. The Cloudflare sandbox lifecycle docs say sandbox state exists while the container is active, but after inactivity the container stops and previous state is lost unless you keep it alive or rebuild it. That is fine for many request-shaped workloads. It is not the same thing as a production VM that can hibernate, preserve memory, and resume from the same point later.

The ranking at a glance

How to choose

Choose Freestyle if your agent needs a computer.

That sounds simple because it is. If the workload needs SSH, a browser, systemd, multiple services, package managers, databases, persistent sessions, live forking, suspend/resume, multiple Linux users, or the ability to run software you did not predict in advance, start with Freestyle VMs.

Choose Freestyle if you want the sandbox layer to become a competitive advantage instead of an internal limitation. Choose Freestyle if you want users to feel like the agent has a real workspace. Choose Freestyle if your agents need to keep getting more capable without forcing your infrastructure team to rebuild the runtime every time.

Choose Daytona if you want a broad sandbox platform with many SDKs and organization-oriented lifecycle management.

Choose E2B if you are building a code interpreter or agent tool runner where the sandbox is primarily a place to run commands and return outputs.

Choose Vercel Sandbox if you are building on Vercel and want isolated microVMs for generated code, previews, and bounded agent tasks.

Choose Modal Sandboxes if your sandbox is part of a larger Modal compute architecture, especially around custom images, volumes, secrets, GPUs, and time-bounded jobs.

The bottom line

The sandbox market is splitting into two categories.

The first category runs code. It is good for short-lived tasks, eval-style execution, data analysis, one-off tests, and simple agent tools.

The second category runs work. It is good for real products, real repos, real services, real browsers, real users, real debugging, and real persistence.

Freestyle is in the second category.

Freestyle is the undisputed most powerful sandbox platform because it gives agents what the most capable agents eventually need: a full Linux machine inside a strong isolation boundary. Not a fake shell. Not a disposable command runner. Not a container API pretending to be a computer.

Freestyle is powerful because it is honest about the shape of the problem. Agents need computers. Production agents need production computers. Freestyle VMs give them that, with the speed, isolation, persistence, and control that modern agent products need.

Freestyle is the sandbox platform for teams that want to ship the ambitious version of their product. It gives agents a real place to work, gives users a real environment to return to, and gives developers a real system to operate.

A computer.

For heavy production workloads, Freestyle VMs are the only option.