Modal vs Vercel Sandbox: comparing AI sandbox environments in 2026

If you are evaluating Modal and Vercel Sandbox for AI agent workloads, the differences in isolation model, GPU availability, session limits, and region coverage are worth working through before committing to either platform.

This article breaks down both platforms on the dimensions that tend to drive infrastructure decisions at scale.

Modal is a serverless compute platform that includes sandboxes as a first-class product. Modal Sandboxes are dynamically defined containers for executing untrusted or agent-generated code, created and managed programmatically via the Modal SDK. Each sandbox runs inside gVisor, a container runtime developed by Google that intercepts system calls to provide strong isolation without requiring a full virtual machine.

Modal Sandboxes are Python-first, though JavaScript and Go SDKs are also available. Sandboxes support custom container images defined at runtime, GPU workloads, filesystem snapshots for state persistence, tunnels for direct connectivity, and fine-grained networking controls. The platform targets AI agent workflows, reinforcement learning environments, code interpreters, and any workload that requires running code you did not write.

Vercel Sandbox is a compute primitive designed to run untrusted or user-generated code in isolated, ephemeral Linux VMs. It uses Firecracker microVMs for isolation and runs on Amazon Linux 2023 with Node.js (node24, node22) and Python (python3.13) runtimes available by default.

Vercel Sandbox is built to sit inside the Vercel ecosystem. Authentication uses Vercel OIDC tokens by default, which are generated automatically for Vercel-hosted projects. The SDK supports TypeScript and Python. Persistent sandboxes are available as a beta feature. The platform is currently limited to the iad1 (US East) region.

The table below compares Modal Sandboxes and Vercel Sandbox across isolation, session limits, BYOC, GPU support, and pricing, with Northflank included as an option for teams whose requirements extend beyond what either platform covers.

Modal and Northflank both bill per second of a running sandbox. Vercel Sandbox bills on active CPU time only (time spent waiting on I/O such as network requests, database calls, and model API responses does not count toward CPU billing), though memory is billed as provisioned regardless.

To make the per-unit pricing difference concrete, here is what 200 sandboxes costs across providers under the same conditions.

Based on 200 sandboxes, plan: nf-compute-100-4, infra node: m7i.2xlarge. Pricing as of April 2026.

*Through Northflank's BYOC plans, there is a default overcommit (request modifier) that allows you to run more sandboxes on the same hardware. A request modifier of 0.2 means each sandbox requests 20% of its plan's resources as a guaranteed minimum but can burst to the full plan limit if capacity is available. Instead of fitting 8 sandboxes per node, you could fit 40, reducing both infrastructure cost and the Northflank management fee.

Verify current rates on each platform's pricing page before making cost decisions.

Both platforms provide isolated environments for running untrusted code, but they differ in isolation approach, runtime flexibility, session management, and what sits around the sandbox itself.

Modal Sandboxes and Vercel Sandbox take different approaches to isolation. Modal uses gVisor, a container runtime by Google that intercepts Linux system calls in user space rather than passing them directly to the host kernel. This provides strong isolation without requiring a full VM. Vercel Sandbox uses Firecracker microVMs, which give each sandbox its own kernel, limiting the impact of container escape vulnerabilities to that individual workload rather than the host or neighbouring tenants.

Northflank supports Firecracker alongside Kata Containers and gVisor, applied per workload depending on isolation requirements. See these guides on Kata Containers vs Firecracker vs gVisor and Firecracker vs gVisor for a technical breakdown of the trade-offs between these approaches.

Modal Sandboxes have a default timeout of 5 minutes, configurable up to 24 hours via the timeout parameter. For workloads that require state beyond 24 hours, Modal's filesystem snapshots can be used to preserve state and restore it in a subsequent sandbox. Idle timeouts are also supported (a sandbox can be configured to terminate automatically after a period of inactivity).

Vercel Sandbox caps sessions at 45 minutes on Hobby and 5 hours on Pro and Enterprise plans. Concurrency is 10 on Hobby and up to 2,000 on Pro and Enterprise. Persistent sandboxes (which auto-save state on stop and resume where they left off) are available in beta.

Session length is worth factoring in early if your workload involves long-running agents, multi-step pipelines, or background tasks. Northflank sandboxes have no platform-imposed session time limit. For more on how session lifecycle affects agent architecture, see ephemeral execution environments for AI agents.

Modal Sandboxes support custom container images defined at runtime, which means any language or runtime that runs in a container is supported. Images can be built dynamically from code, making Modal flexible for Python-heavy workflows, Node.js, and less common stacks. The primary SDK is Python, with JavaScript and Go available.

Vercel Sandbox ships with a fixed set of runtimes: node24, node22, and python3.13, running on Amazon Linux 2023. Additional packages can be installed at runtime, but the base OS and available runtimes are more constrained than Modal's custom image system.

BYOC (deploying sandbox infrastructure inside your own cloud account or VPC) is relevant for teams with data residency requirements, security policies, or existing cloud spend they want to use.

Neither Modal nor Vercel Sandbox offers a BYOC deployment option. Both platforms run on managed infrastructure only.

Northflank supports bring-your-own-cloud (BYOC) on a self-serve basis across AWS, GCP, Azure, Oracle, CoreWeave, Civo, bare-metal, and on-premises. See the deploy sandboxes in your cloud documentation for setup details.

Modal supports GPU workloads including L4, A10, A100 (40GB and 80GB), L40S, H100, H200, and B200. Region selection applies a cost multiplier on top of base GPU pricing.

Vercel Sandbox does not provide GPU compute. If your workload requires GPU inference, training, or compute-intensive agent tasks alongside sandboxed code execution, you would need to provision GPU infrastructure separately.

Northflank supports on-demand GPUs without quota requests: NVIDIA L4 at $0.80/hr, A100 40GB at $1.42/hr, A100 80GB at $1.76/hr, H100 at $2.74/hr, and H200 at $3.14/hr. GPU workloads run on the same platform as sandboxes, APIs, workers, and databases.

Modal supports region selection across US, EU, AP, UK, Canada, South America, Middle East, Mexico, and Africa. Region selection adds a cost multiplier: 1.25x for US/EU/UK/AP regions, and 2.5x for CA/SA/ME/MX/AF regions. All Function inputs and outputs route through Modal's control plane in us-east-1 regardless of the selected region.

Vercel Sandbox currently runs in iad1 (US East) only. This is a meaningful constraint if your users or your agent infrastructure are based in Europe or Asia. Latency for sandbox interactions from outside the US will reflect that single-region deployment.

Northflank's managed cloud covers US West, US Central, US East, EU West, and Asia East. BYOC extends this to 600 BYOC regions via supported cloud providers and bare-metal deployments.

Modal is Python-first, with JavaScript and Go SDKs also available. Sandboxes are defined and managed entirely in code, with no UI-based management. Modal also provides fine-grained networking controls (network access can be fully blocked or restricted via CIDR allowlist) and tunnels for direct sandbox connectivity.

Vercel Sandbox provides TypeScript and Python SDKs alongside a CLI. Authentication integrates with Vercel's OIDC token system, which is generated automatically for Vercel-hosted projects. For external environments, access tokens are available as an alternative.

Northflank provides API, CLI, SSH, and UI access, with GitOps support for infrastructure-as-code workflows. The create sandbox with SDK documentation covers programmatic sandbox provisioning and lifecycle management.

Modal supports GPU workloads alongside sandboxed code execution, custom container images defined at runtime, sessions of up to 24 hours, and multi-region deployments. Networking controls allow outbound access to be fully blocked or restricted via CIDR allowlist.

See also: e2b vs modal, e2b vs modal vs fly.io sprites, and daytona vs modal for additional comparisons in this space.

Vercel Sandbox is a fit for teams already on the Vercel platform whose workloads run within the supported runtimes (node24, node22, python3.13) and the 5-hour session limit. Authentication via Vercel OIDC tokens works automatically for Vercel-hosted projects. The active CPU billing model means idle I/O time is not billed.

The single-region constraint (iad1) and the absence of bring-your-own-cloud (BYOC) are the trade-offs to factor in. See top Vercel Sandbox alternatives for a broader comparison.

Northflank covers sandbox execution as part of a broader workload platform that also runs APIs, background workers, databases, GPU inference, and CI/CD pipelines.

Key differences from Modal Sandboxes and Vercel Sandbox:

• Isolation stack: Northflank supports Kata Containers, Firecracker, and gVisor applied per workload. Modal uses gVisor only for sandboxes. Vercel Sandbox uses Firecracker. See Kata Containers vs Firecracker vs gVisor for a technical breakdown.
• Bring-your-own-cloud (BYOC): Self-serve across AWS, GCP, Azure, Oracle, Civo, CoreWeave, and bare-metal. Neither Modal nor Vercel Sandbox offers a BYOC option. See self-hosted AI sandboxes and top BYOC AI sandboxes for more on deployment models.
• Session limits: Northflank sandboxes have no platform-imposed session time limit. Sandboxes can be ephemeral or persistent.
• GPU support: On-demand GPUs including L4, A100, H100, and H200, and more, running on the same platform as sandboxes.

Modal Sandboxes use gVisor for isolation and support GPU workloads, custom container images, multi-region deployments, and sessions of up to 24 hours. Vercel Sandbox uses Firecracker microVMs, supports Node.js and Python runtimes, caps sessions at 5 hours on Pro, and is currently available in the iad1 region only. Northflank supports a broader isolation stack (Kata Containers, Firecracker, and gVisor), self-serve BYOC, and no platform-imposed session time limit.

Modal Sandboxes use gVisor, a container runtime by Google that intercepts Linux system calls in user space. This provides strong isolation without requiring a full VM per sandbox.

Vercel Sandbox does not provide GPU compute. However, Northflank supports on-demand GPU workloads including L4, A100, H100, and H200 on the same platform as sandboxes.

Neither Modal nor Vercel Sandbox offers a BYOC deployment option. Both run on managed infrastructure only.

Modal Sandboxes are billed per second at $0.00003942/core/sec (1 physical core = 2 vCPU equivalent) for CPU and $0.00000672/GiB/sec for memory. Region selection adds a cost multiplier of 1.25x for US/EU/UK/AP regions and 2.5x for other regions. GPU workloads use Modal's standard GPU pricing.

Modal Sandboxes support sessions of up to 24 hours via the timeout parameter. Vercel Sandbox supports sessions of up to 5 hours on Pro and Enterprise plans. Northflank sandboxes have no platform-imposed session time limit.