AI agents need separate, secure computers to execute untrusted code and handle tasks in parallel. Whether you're developing coding agents, executing generated scripts, or automating desktops, sandbox choice affects development speed and production safety. Secure sandboxes keep each agent's code isolated, and sandbox-as-a-service providers create and manage them, so you don't have to run your own VM fleet.
This guide compares six platforms for agent workloads in 2026, beginning with E2B, which isolates every agent in its own Firecracker microVM and runs in E2B Cloud, your own cloud, or self-hosted.
Key takeaways
- Sandbox isolation: E2B and Vercel Sandbox run each sandbox in its own Firecracker microVM. Modal uses gVisor by default, and Northflank's managed cloud uses Kata for CPU workloads and gVisor for GPU workloads
- Deployment options: E2B runs as managed E2B Cloud, BYOC on AWS, GCP, or Azure, or self-hosted on its open-source Apache-2.0 runtime. It is the only platform in this comparison that offers all three
- Workload fit: E2B adds memory-preserving pause and network and secret controls. Modal, Daytona, and Northflank support GPUs, Daytona adds Windows desktops plus macOS through a separate service, Blaxel stops compute charges during standby, and Vercel Sandbox bills CPU only while it is active
1. E2B
E2B gives every AI agent its own secure Linux computer, where untrusted code runs isolated from the host and other sandboxes. Teams start, pause, and resume sandboxes with memory intact straight from the Python or JavaScript/TypeScript SDK.
Key features
- Sandbox isolation: Each sandbox runs inside a separate Firecracker microVM, giving generated code hardware-level isolation
- Deployment choices: E2B can deploy in managed E2B Cloud, BYOC, or self-hosted E2B Embed. BYOC runs on AWS, GCP, or Azure. Each option uses E2B's open-source Apache-2.0 runtime
- Pause and resume: Memory-preserving pause keeps files and memory intact, so running processes survive interruptions. Paused sandboxes are kept indefinitely, and each resume resets the session time limit
- Network and credentials: Teams can change egress rules for IP addresses, CIDR ranges, and domains without restarting a sandbox. Secret injection keeps credential values outside the sandbox, so code inside it can't read them
- Parallel execution: Snapshot-based forking creates up to 20 copies per call from a prepared state, giving parallel agents the same starting point
Security and data controls
E2B gives teams control over credentials and where data lives. E2B blocks sandbox traffic to private and link-local addresses, including the cloud metadata service, so code in a sandbox can't use it to read credentials. With BYOC, templates, snapshots, and logs stay in the customer's VPC, and only anonymized metrics reach E2B.
E2B is SOC 2 Type II compliant and signs HIPAA BAAs on Enterprise plans.
In production
94 of the Fortune 100 have signed up for E2B, more than 100,000 teams have run a sandbox, and over two billion sandboxes have been started.
Ideal for: Teams running production agents that need each agent in its own isolated computer, especially when workloads must run in their own cloud.
2. Modal
Modal gives ML agents GPU acceleration inside the sandboxes that execute their code. The platform offers serverless functions and GPU workloads alongside sandboxes.
Key features
- GPUs in the sandbox: Sandboxes support GPUs such as the H100, so agents can run GPU work in the same place their code runs
- Isolation options: Sandboxes use gVisor by default, with a VM runtime option available. GPU sandboxes use gVisor only
- Traffic rules: Allowlists control incoming and outgoing network traffic. Domain allowlists are in beta
- Snapshot recovery: Teams restore a saved snapshot into a new sandbox to continue work, rather than resuming the original sandbox in place
Where it fits
Modal suits ML workloads that combine generated code and GPU processing, especially for teams with applications already on the platform. Sessions have a default duration of 5 minutes and a maximum of 24 hours.
Ideal for: Teams developing ML-heavy agents that need sandbox GPUs and prefer managed deployment without BYOC.
3. Daytona
Daytona offers Windows and macOS desktops and large GPU options for long-lived agent computers. The platform includes a Linux VM runtime, while its macOS desktops run on a separate service.
Key features
- Desktop options: Built-in Windows VMs, plus macOS from another provider, give teams desktop environments beyond Linux
- GPU capacity: Each sandbox supports up to 8 GPUs, with hardware ranging from RTX 4090 to B300 and MI355X
- VM controls: Teams can fork VMs, pause and resume them, and save memory snapshots. New VM sandboxes require an existing VM snapshot
- Session duration: Sessions have no time cap, letting agents continue work without a fixed session deadline
- Hosting choices: Managed sandboxes are available, and Enterprise plans add BYOC for teams that need deployment in their own cloud
Where it fits
Daytona fits teams whose agents need desktop access or several GPUs in a single sandbox for extended work. Its production codebase is closed source as of June 2026.
Ideal for: Teams developing long-lived agents that require Windows or macOS desktops, or running workloads needing several GPUs within one sandbox.
4. Northflank
Northflank pairs self-serve BYOC with low list rates for teams running agent sandboxes. The platform bills by elapsed time, including time agents spend waiting, and supports GPU workloads.
Key features
- BYOC deployment: Teams deploy sandboxes in cloud accounts they own without going through a sales process
- Workload isolation: On Northflank's managed cloud, Kata isolates CPU tasks in lightweight VMs, while GPU tasks use gVisor
- GPU acceleration: Agents can use GPUs inside their sandboxes for compute-heavy machine learning tasks
- Session length: Sessions have no time cap, letting agents handle tasks that run for extended periods
- Saved files: Volumes preserve the filesystem across pauses. Running processes do not survive, so persistence covers files rather than ongoing work
Where it fits
Northflank suits teams that prioritize deployment control and low sandbox rates. Self-serve BYOC keeps workloads in customer cloud accounts, with GPU access available through the same platform.
Ideal for: Teams seeking self-serve BYOC, GPU access, and uncapped agent sessions whose persistence needs are met by saved files alone.
5. Blaxel
Blaxel's "perpetual standby" model preserves microVM sandbox state with no compute charges while idle. The platform bills by memory, and standby may require higher quota tiers.
Key features
- Standby resume: Sandboxes in standby resume in under 25 ms, though that speed applies only to waking an existing sandbox rather than creating a new one
- Idle billing: Compute charges stop during standby, though teams still pay for storage
- Memory retention: Pausing preserves both memory and the filesystem, keeping the sandbox's working state available for the next task
- MCP tools: Each sandbox includes a Model Context Protocol server, letting agents use a standard protocol to call its tools
Where it fits
Blaxel suits agents that spend long periods waiting between brief tasks. Its standby model keeps state available through those gaps, so teams can preserve continuity while avoiding idle compute charges.
Ideal for: Teams whose agents work in short bursts and need memory to survive idle periods, with quick resume as a priority.
6. Vercel Sandbox
Vercel Sandbox's active-CPU billing suits agents that spend much of their time waiting for LLM responses. The platform runs untrusted agent code in Firecracker microVMs and integrates with Vercel's deployment platform.
Key features
- CPU and memory charges: CPU charges stop during I/O and LLM waits. Provisioned memory remains billable, with a 1-minute minimum
- Parallel capacity: The Pro plan supports as many as 10,000 sandboxes running at once, letting teams run agents in parallel
- Saved files: The filesystem saves automatically when a sandbox stops. Snapshots expire after 30 days without use, and pause preserves files only
- Session duration: Hobby sessions last up to 45 minutes; Pro sessions allow 24 hours. Resuming resets the session limit
Where it fits
Vercel's deployment integration makes it a practical fit for teams already using its platform. Its managed service suits CPU workloads where teams need saved files between sessions and do not require memory-preserving pause.
Ideal for: Teams using Vercel or Next.js for LLM-bound agents that use little CPU and need no GPUs.
What sets E2B apart for AI agents
Verifiable security controls
- Isolation: Every E2B sandbox runs in a Firecracker microVM with its own kernel, while Modal defaults to gVisor and Northflank's managed cloud runs GPU tasks in gVisor
- Open-source runtime: E2B's runtime is open source under Apache-2.0, so teams can audit the code that runs their sandboxes. Daytona's production codebase is closed source as of June 2026
Proven capacity for agent fleets
- Enterprise concurrency: Enterprise supports tens of thousands of sandboxes running at once
- Customer proof: Arena runs as many as 600,000 E2B sandboxes a day
Bring-your-own-cloud deployment
- Customer VPC: Enterprise BYOC runs on AWS, GCP, or Azure, keeping templates, runtime logs, and snapshots in the customer's VPC
- Operated by E2B: E2B provisions, monitors, and operates the sandbox cluster through the customer's cloud account
Frequently asked questions
Why does an AI agent need a separate execution environment?
A sandbox gives an agent an isolated computer for executing code away from production systems. Agents use that environment to run shell commands, install packages, and execute generated code, while a managed provider handles sandbox creation and lifecycle through an API. E2B places each sandbox inside a separate Firecracker microVM, giving the agent a shell, filesystem, and network access within that environment.
Which controls protect credentials and data when agents run code?
Sandbox security combines isolation, restricted network access, and credentials kept outside the execution environment. E2B uses Firecracker microVMs and network rules that allow or block destinations by IP, CIDR, and domain, and it blocks cloud metadata access by default. Its secret injection adds credentials at the network proxy, keeping their actual values outside the VM and unavailable for code inside it to read.
How does sandbox execution connect to an existing agent framework?
E2B connects sandbox execution to any model or agent framework through SDKs for Python and JavaScript/TypeScript. Teams can use its Code Interpreter SDK for code execution or its OpenAI Agents SDK integration for native sandbox execution, with E2B serving as a hosted provider.
When does an open-source sandbox make sense for a team?
An open-source sandbox fits teams that want to operate the software on infrastructure they control. E2B Embed supports self-hosting through E2B's Apache-2.0 runtime, which also powers its Cloud and BYOC deployments, giving teams a choice of where to run agents. Because self-hosting puts updates and security configuration entirely in your team's hands, source access alone doesn't guarantee security. Defaults and patch practices still matter.
Which deployment options keep sandbox data inside a customer VPC?
E2B is a strong choice for teams that need sandbox data kept inside their own VPC. Its Enterprise BYOC deployment supports AWS, GCP, and Azure, retaining templates, snapshots, and logs in the customer VPC while sending E2B only anonymized metrics. Northflank's BYOC option is self-serve, and Daytona's requires Enterprise; E2B also offers Cloud hosting and self-hosted Embed for teams with different deployment needs.
Read the sandbox Quickstart