E2B vs Modal: comparing AI code execution sandboxes in 2026

E2B provides isolated Linux microVM sandboxes for AI agents to execute code safely. You define an environment via a custom template, and your agent provisions sandboxes on demand via a Python or JavaScript/TypeScript SDK. Each sandbox has a defined lifecycle: created, used, then torn down.

The platform exposes SSH access, an interactive terminal (PTY), lifecycle webhooks, and the ability to connect to running sandboxes. Common use cases include coding agents, computer use agents, and CI/CD pipelines. A Bring Your Own Cloud option is available, currently limited to AWS and enterprise customers only.

Modal is an AI infrastructure platform covering inference, training, batch processing, notebooks, and sandboxes. Modal Sandboxes are specifically for running untrusted or agent-generated code, isolated using gVisor.

What makes Modal Sandboxes distinct is how environments are defined. Rather than pre-built templates, you define the container image, dependencies, and configuration in code at runtime; the environment is assembled at the point of sandbox creation.

Here's how the three platforms stack up across the dimensions that typically drive the decision.

The table above captures the what. Here's the why behind the differences that actually drive decisions.

E2B runs sandboxes inside Firecracker microVMs, providing hardware-level isolation between workloads and the host. Each sandbox runs in its own VM with a separate kernel. Modal Sandboxes run on gVisor, a container runtime by Google that intercepts system calls to prevent malicious code from reaching the host kernel.

Both approaches are stronger than standard container isolation. The practical difference is in the mechanism: microVMs provide hardware-level VM boundaries per sandbox; gVisor intercepts system calls. Teams with strict compliance requirements or specific threat models should evaluate both directly.

Both are primarily session-scoped. Modal's snapshot feature lets you save and restore filesystem and memory state, but restoring creates a new sandbox from that snapshot rather than resuming the original.

E2B uses SDK-defined custom templates: you build a template with the required dependencies, version and cache it, and sandboxes spawn from that template consistently.

Modal takes a different approach: environments are defined dynamically in code at the point of sandbox creation. You can pass any valid container image, including ones assembled from requirements at runtime. This means the environment definition can itself be generated by an LLM.

Both approaches are SDK-driven; the difference is when the environment is assembled: at template-build time for E2B, or at runtime for Modal.

E2B fits when you need microVM-isolated, reproducible execution environments for agent workloads. Use it when:

• Your agents generate code that needs a fresh, hardware-isolated Linux environment each time
• You want SDK-driven sandbox creation with consistent, versioned templates
• You need SSH access, PTY, or lifecycle webhooks for sandbox observability and control
• Each task is stateless or self-contained within a session
• You need a BYOC option for AWS (enterprise customers)

Modal fits when sandboxes are one part of a wider ML compute stack, or when you need very high concurrency. Use it when:

• You need to scale to very high concurrency of simultaneous sandbox sessions
• Your agent or LLM needs to define its own execution environment dynamically at runtime
• You want inference, training, batch processing, and sandboxes in a single platform
• gVisor-based isolation is sufficient for your threat model
• You need GPU sandboxes alongside other GPU workloads

Northflank's Secure Sandboxes provide microVM-based isolation for running untrusted code safely, with both ephemeral and persistent environments, in managed cloud or your own infrastructure.

Where it goes further is in what surrounds the sandboxes: the same platform also runs agents, APIs, workers, databases, and both CPU and GPU workloads, so teams don't need a separate system as their requirements grow.

Here's how it compares:

• MicroVM sandboxes: Kata Containers, Firecracker, and gVisor isolation depending on workload. Sub-second cold starts. Built for running untrusted, LLM-generated code safely at scale with true multi-tenant isolation.
• Ephemeral and persistent, same control plane: Short-lived execution pools and long-running stateful services run together. No need to choose one model or stitch two tools.
• Self-serve BYOC: Deploy in your own infrastructure on AWS, GCP, Azure, Oracle, Civo, CoreWeave, or on-premises. Enterprises can run sandboxes entirely within their own infrastructure, which is important for teams with compliance or data residency requirements.
• On-demand GPUs without quota requests: Self-service provisioning for inference, training, and compute-heavy agent work. No waiting on allocations.
• Full workload runtime alongside sandboxes: Agents, APIs, workers, background jobs, databases, and inference run in the same platform. Teams that outgrow sandbox-only tools don't need to migrate.
• End-to-end sandbox creation in 1-2 seconds: The full creation process, not just VM boot.
• In production since 2021: Multi-tenant microVM workloads across startups, public companies, and government deployments. For a concrete example, cto.new uses Northflank's microVMs to scale secure sandboxes in production.
• Pricing: CPU at $0.01667/vCPU-hour, memory at $0.00833/GB-hour. Full details on the Northflank pricing page.

E2B provides on-demand Linux microVM sandboxes for AI agents to execute code safely. Common use cases include coding agents, computer use agents, data analysis pipelines, and CI/CD workflows where each job needs an isolated execution environment, managed via Python or JavaScript/TypeScript SDKs.

Modal is an AI infrastructure platform covering inference, training, batch processing, notebooks, and sandboxes. Unlike E2B, which is focused specifically on sandboxing, Modal Sandboxes are one product within a broader ML platform. The other key difference is isolation: E2B uses microVM-based isolation; Modal uses gVisor, a container runtime that intercepts system calls for stronger-than-standard container isolation.

Modal does not use pre-built templates in the same way as E2B. Instead, environments are defined dynamically in code at runtime: you specify a container image and configuration when creating the sandbox. E2B uses SDK-defined custom templates that are built, versioned, and cached ahead of time, so each sandbox spawns from a consistent, pre-warmed environment.

Modal runs sandboxes on gVisor, which provides stronger isolation than standard containers by intercepting system calls. E2B uses Firecracker microVMs, which provide hardware-level VM boundaries with a separate kernel per sandbox. Both are stronger than standard container isolation; the difference is in the mechanism.

E2B offers a BYOC option on AWS for enterprise customers only. Modal is a managed service. Northflank offers self-serve BYOC across AWS, GCP, Azure, Oracle, Civo, CoreWeave, and on-premises.

Beyond isolation model, look at whether the platform supports your deployment model (managed vs. your own infrastructure), whether you need ephemeral, persistent, or both environment types, GPU availability, and whether you'll need additional infrastructure running alongside sandboxes. Platforms like Northflank combine microVM-based sandboxes with a full production runtime, reducing the number of tools you need as requirements grow.

If you're evaluating sandbox platforms or digging deeper into the architecture, these articles cover the adjacent decisions and trade-offs:

• Top Modal Sandboxes alternatives for secure AI code execution - Covers the strongest alternatives to Modal Sandboxes for teams evaluating other options for secure code execution.
• E2B vs Sprites dev: comparing AI code execution sandboxes - A direct comparison of E2B and Sprites dev across isolation model, persistence, and developer experience.
• E2B vs Modal vs Fly.io Sprites for AI code execution sandboxes - A three-way comparison across three of the most discussed platforms in the AI sandbox space.
• The best alternatives to E2B.dev for running untrusted code in secure sandboxes - If E2B isn't the right fit, this covers the strongest alternatives with a focus on isolation and security.
• Top Fly.io Sprites alternatives for secure AI code execution and sandboxed environments - Covers platforms with a similar persistent microVM model to Sprites.
• What is an AI sandbox? - A foundational explainer on what sandboxes are, why isolation is required, and how different approaches compare.
• How to spin up a secure code sandbox and microVM in seconds with Northflank - A practical walkthrough of launching a Northflank microVM sandbox using Firecracker, gVisor, or Kata Containers.
• Top AI sandbox platforms, ranked - A broader ranked overview of the AI sandbox market as it stands in 2026.
• How to sandbox AI agents: MicroVMs, gVisor and isolation strategies - A technical deep-dive into isolation approaches and trade-offs between Firecracker, gVisor, and Kata Containers for agent workloads.
• Self-hosted AI sandboxes: guide to secure code execution - Useful if you're evaluating whether to run sandbox infrastructure inside your own infrastructure rather than using a managed service.
• What's the best code execution sandbox for AI agents - A decision-focused guide for teams actively choosing a sandbox platform for their AI agent stack.
• Secure runtime for codegen tools: microVMs, sandboxing, and execution at scale - Covers the infrastructure considerations for code generation tools that need to run LLM-generated code at scale.