9 Best AI Agent Sandboxes of 2026

Daytona is a close second and the most flexible sandbox here on operating systems. Its docs describe sandboxes as isolated runtime environments with a dedicated kernel, filesystem and network, offered as fast Linux containers, Linux VMs with fork and memory snapshots, Windows, macOS on Apple silicon and GPU machines. The SDKs cover TypeScript, Python, Ruby, Go and Java, and the APIs handle files, Git, language servers and process execution with streamed output. A computer use API adds mouse, keyboard, accessibility tree and screenshot control, which suits agents that browse or work in desktop apps. Pricing has no plan fee: vCPU costs $0.0504 per hour, memory $0.0162 per GiB hour, and the first 5 GiB of storage is free, all billed per second. New accounts get $200 in compute. The default per-sandbox ceiling of 4 vCPUs and 8 GB RAM is low for heavy builds, and SSO and audit logs are Enterprise features.

E2B ranks first because it is built around the exact job on this list: one isolated machine per agent session. Its site describes a Firecracker microVM per session with no shared kernel, an Apache-2.0 runtime and SOC 2 Type II, and lists HIPAA on the pricing page. Agents can run code, use a CLI and browser, or drive a full Linux desktop through E2B Desktop, which exposes screenshots, clicks and keyboard input for computer-use models. Templates let you bake tools such as Claude Code or Docker into a starting image. The Hobby tier is free with a one-time $100 credit, 20 concurrent sandboxes and 1-hour sessions. Pro costs $150 a month plus usage and raises sessions to 24 hours and concurrency to 100, expandable to 1,100 with add-ons. A default 2 vCPU sandbox bills $0.000028 per second for CPU. The fixed Pro fee is the main drawback for small teams, and BYOC sits on the Enterprise tier.

Vercel Sandbox ranks third for a clean design and fair billing. The docs state that each sandbox runs in a Firecracker microVM with its own filesystem and network, and that it is meant for agent output, user uploads and third-party scripts. Features include snapshots, persistent sandboxes, custom OCI images, a firewall, regional placement and a multi-agent mode that gives each agent its own Linux user and home directory. It can also run privileged processes such as Docker or FUSE drivers, which many sandboxes block. Billing counts active CPU at $0.128 per hour and provisioned memory at $0.0212 per GB hour, so idle waiting on a model costs little CPU. Hobby includes 5 active CPU hours, 5,000 creations and 45-minute sessions free each month; Pro lifts sessions to 24 hours and 10,000 concurrent sandboxes. There is no desktop or computer use product, and Hobby stops creating sandboxes once its allotment is used.

Modal Sandboxes rank fourth and suit teams that want sandboxes on the same platform as their models. The docs describe secure containers for LLM-generated or user-generated code, created from Python, JavaScript or Go, with exec calls that stream output, timeouts, volumes and bucket mounts. Two runtimes are offered: gVisor, which Modal calls suitable for most workloads, and a VM runtime with its own Linux kernel for jobs that need more. GPUs, memory snapshots and autoscaling come from the wider Modal platform, which also covers inference and training. The Starter plan costs $0 plus compute with $30 of monthly credits, up to 3 seats and 100 containers; Team is $250 a month with $100 credits and 5,000 containers. Sandbox CPU bills $0.00003942 per core per second because sandboxes are priced at non-preemptible rates, which Modal states is 3x list price. That makes it pricier per hour than Daytona or Northflank.

Northflank ranks fifth and has the lowest published compute rate here. Its sandbox page describes microVMs on Cloud Hypervisor with a dedicated guest kernel per sandbox, and gVisor for GPU workloads. Sandboxes can be ephemeral, created per request and deleted on return, or long-running with state on volumes and S3. Use cases on the page include coding agents, code interpreters and browser agents that each get headless Chrome over a private network, with egress allowed by domain and secrets injected at the egress layer. Because Northflank is a full deployment platform, sandboxes sit beside services, jobs and databases, and the same setup can run in your own AWS, Google Cloud or Azure account. Pay as you go starts at $0.01667 per vCPU hour and $0.00833 per GB hour, billed by the second with no seat fees, and the free Sandbox tier covers up to $20 of resources a month. The trade-off is a steeper learning curve than sandbox-only tools.

Blaxel ranks sixth for a pricing model that fits how agents behave. Its site describes one microVM per agent, app or job that boots in milliseconds, auto-suspends when idle and resumes in about 25ms with memory state intact, so a sandbox can wait on a user or a model for hours without paying for CPU. Active compute bills $0.0000115 per GB of RAM per second, with snapshots at $0.20 per GB month. Beyond sandboxes it offers batch jobs across thousands of isolated machines, MCP server hosting on the same private network, an Agent Drive shared filesystem, static outbound IPs and per-workload outbound firewalling. Each sandbox keeps its root filesystem in memory and wipes it on deletion, and Europe and US regions are available. New accounts get up to $200 in credits. The site also announces that Blaxel has joined Baseten, so buyers should watch for plan changes, and the Agent Runtime product is not yet live.

Cloudflare Sandboxes rank eighth. The docs describe two environments: Linux sandboxes where each instance is a microVM with its own kernel and network, and Dynamic Workers for JavaScript that only needs the methods your Worker provides. In both cases your Worker starts the sandbox, sends it work and decides which APIs, credentials and services it can reach, and internet traffic reaches a Linux instance only through that Worker. That model gives tight control over what an agent can touch. Guides cover running Claude Code, Codex, OpenCode and Cursor in a sandbox, cloning private repositories, preview URLs and mounting R2 buckets. Pricing follows Containers: the $5 a month Workers Paid plan includes 375 vCPU minutes, 25 GiB hours of memory and 200 GB hours of disk, then usage is billed for every 10ms a container runs. The catch is that you build around Workers and Durable Objects, which suits Cloudflare shops more than teams wanting a standalone API.

Runloop ranks seventh and is the most specialized product here. It sells devboxes for AI coding agents that run in micro-VMs with a container layer inside, which the vendor describes as two layers of security, plus arm64 and x86 support and command execution in about 100ms. Blueprints and snapshots make environments repeatable, repo connections pull in code, agents can control browsers and computers, and MCP servers and SSH tunnels connect outside tools. Its evaluation and benchmark tools are the main reason to choose it over a general sandbox. The Basic plan has no subscription fee and bills devbox CPU at $0.108 per CPU hour and memory at $0.0252 per GB hour; Pro adds suspend and resume, 1 TB of free storage and Slack support for $250 a month. New accounts get $50 of credit with Pro features. The vendor's own example puts a 2 CPU, 4 GB devbox at about $60 of compute a month at 8 hours a day.

Morph Cloud ranks ninth. It sells MorphVM instances through an API with snapshot, branch, pause, resume, reboot, TTL and wake-on-request calls, so an agent workspace can be prepared once and forked into many copies. The docs include guides for auto-scaling agent workspaces, running Claude Code, browser-based Linux remote desktops, Docker BuildKit and GitHub Actions runners, with SSH and HTTP service exposure for each instance. Billing uses Morph Compute Units: one MCU covers 1 vCPU hour plus 4 GB of RAM hours and 16 GB of disk hours, at a standard rate of $0.05. The Free tier costs nothing but includes no credit, Developer is $40 a month for 1,000 MCUs worth $50, and Enterprise Scale is $250 for 7,500 MCUs. Instance ceilings are generous, up to 64 vCPU on Free. The MCU model is harder to estimate than per-hour pricing, and agent framework integrations are less documented than at E2B or Daytona.