Best Code Execution Sandbox for Bolt.new in 2026

1. Modal

Modal delivers serverless compute for secure code execution at scale, the core workload for AI app builders like Bolt.new, with on-demand GPU access for workloads requiring ML inference or model fine-tuning. The platform containerizes your code and executes it in the cloud with automatic scaling. Modal provides code-first SDKs in Python, TypeScript, and Go for defining applications and Functions, running Sandboxes, calling Functions, and managing Modal resources.

Core Capabilities

Security and Compliance

Modal maintains SOC 2 Type II certification with no deviations found during the audit. The platform supports HIPAA-compliant workloads on Enterprise plans via a Business Associate Agreement. Security practices include gVisor-based sandboxing for compute isolation, TLS 1.3 for public APIs, and encryption for data in transit and at rest.

Production-Proven Results

Modal powers production workloads for AI companies building at scale:

What Makes Modal Unique

Best For: Teams building AI app platforms like Bolt.new that need secure code execution at production scale, with on-demand GPU access for ML inference, model fine-tuning, or compute-intensive analysis, especially those requiring enterprise-grade compliance.

2. E2B

E2B specializes in secure sandboxes for AI agents and code execution, focusing on ephemeral environments with Firecracker microVM isolation. E2B and investor materials state that 88% of Fortune 100 companies have signed up with E2B, with named customers including Hugging Face, Perplexity, Groq, and Manus.

Core Capabilities

Use Case Focus

E2B excels at ephemeral code execution, spinning up isolated environments for AI-generated code, then tearing them down. The platform supports up to 100 concurrent sandboxes on Pro tier plans with 24-hour maximum session duration.

Architecture Approach

E2B's Firecracker-based isolation provides strong security boundaries for untrusted code execution. Each sandbox runs in its own microVM with a dedicated kernel, offering robust protection against code escape or cross-tenant access.

Best For: Teams building AI coding tools focused on secure code execution where GPU acceleration is not required, particularly those needing sandbox cold starts and strong microVM isolation.

3. Daytona

Daytona provides persistent development environments with sandbox cold starts. The platform's open source GitHub repository demonstrates active community engagement and offers both GPU support and configurable runtime persistence.

Core Capabilities

Architecture Approach

Daytona focuses on persistent workspaces that maintain state across sessions. This approach benefits AI app builders that need to preserve context, cached dependencies, or intermediate results without recreation overhead. The platform operates on a pure usage-based model without subscription requirements.

Best For: Teams building AI applications that require persistent development environments, prefer workspace continuity over ephemeral execution, and want sandbox cold starts.

4. Northflank

Northflank delivers a complete infrastructure platform with flexible sandbox capabilities and full bring-your-own-cloud (BYOC) deployment options. The platform processes over 2 million isolated workloads monthly and serves startups, public companies, and government deployments.

Core Capabilities

Architecture Approach

Northflank provides a comprehensive infrastructure platform rather than a focused sandbox solution. This approach suits teams that want sandboxed execution alongside databases, API hosting, and worker deployments in a single management interface, with the flexibility to run everything in their own cloud accounts.

Use Case Focus

Northflank excels when teams need complete control over deployment location while still accessing managed sandbox capabilities. Northflank supports sandbox cold starts for microVM-backed sandboxes, and the platform emphasizes deployment flexibility and full infrastructure capabilities.

Best For: Teams requiring BYOC deployment for regulatory or compliance reasons, particularly those wanting sandboxes alongside databases, APIs, and workers in a unified platform with flexible isolation options.

5. Together Code Sandbox

Together Code Sandbox provides managed sandbox environments for AI-powered coding tools, focusing on configurable VM-based development environments with snapshotting capabilities. Together Code Sandbox is currently available on custom Together plans, with self-serve access possible through CodeSandbox while the product migration into Together continues. Together positions the product around secure code execution at scale for AI development workflows.

Core Capabilities

Use Case Focus

Together Code Sandbox is geared toward building and scaling AI coding tools that need isolated development environments. The platform supports stateful development environments with snapshot capabilities for preserving execution state.

Best For: Teams building within the Together ecosystem that need configurable sandbox VMs, stateful development environments, and secure execution of untrusted code at scale.

6. Vercel Sandbox

Vercel Sandbox provides isolated code execution environments built for running untrusted code in on-demand Linux microVMs. Vercel positions the product for AI agents, code execution, testing, and development workflows requiring secure isolated environments.

Core Capabilities

Architecture Approach

Vercel Sandbox functions as an execution layer for secure, isolated code running rather than a full infrastructure platform for GPU-heavy AI workloads. The platform fits best for agent or developer workflows involving repeated start-run-stop cycles and short-lived tasks.

Best For: Teams that need isolated environments for code execution and testing, especially when building within the Vercel ecosystem and prioritizing secure ephemeral execution over GPU access.

7. Cloudflare Sandbox

Cloudflare Sandbox exposes code execution capabilities through the Sandbox SDK, supporting Python and Node.js workloads with file management and agent-style workflows via a TypeScript API. The platform leverages Cloudflare's global edge network for distributed execution.

Core Capabilities

Use Case Focus

Cloudflare Sandbox centers on secure code execution and programmable sandbox workflows rather than browser-based app building. Cloudflare's documentation includes tutorials for AI code executors and AI coding agents, making it relevant for teams building code execution infrastructure.

Best For: Teams building within the Cloudflare ecosystem that need isolated code execution, file handling, and agent-oriented workflows, particularly those preferring a TypeScript-first development model with edge network distribution.

Why Modal Stands Out for AI App Builder Sandboxes

GPU-Accelerated Sandboxes Set Modal Apart

Modal offers unusually broad integrated GPU access spanning T4 through B200 and is a strong fit for GPU-heavy AI workloads; some competing sandbox platforms also offer GPU-capable sandboxes, though GPU model coverage, isolation model, and availability vary. For AI app builders like Bolt.new that generate and execute ML-heavy code, this breadth reduces the need to coordinate between separate sandbox and GPU infrastructure providers. Teams can run LLM inference, vision models, and compute-intensive analysis within the same secure execution environment.

Production Scale Proven with Enterprise Customers

Modal supports 100k+ concurrent sandboxes, demonstrated with customers like Lovable running tens of thousands of containers simultaneously for AI app generation. Meta uses Modal for Code World Models with thousands of concurrent sandboxes for reinforcement learning. This production track record shows Modal can handle the scale that successful AI app platforms require.

Enterprise Compliance Without Compromise

Modal maintains SOC 2 Type II certification with no deviations found during the audit, plus HIPAA support on Enterprise plans via a BAA. For AI app builders serving enterprise customers or handling sensitive data, Modal meets compliance requirements that many sandbox platforms cannot match. The platform's security practices include gVisor-based sandboxing, TLS 1.3 for APIs, and encryption for data in transit and at rest.

Unified AI Infrastructure Reduces Complexity

Modal's platform integrates inference, training, batch processing, sandboxes, and notebooks in a single system with a shared GPU pool. AI app builders can deploy the same codebase for development sandboxes and production inference without managing multiple vendors or complex integrations. Teams use code-first SDKs in Python, TypeScript, and Go to define applications and Functions, run Sandboxes, call Functions, and manage resources, without YAML configuration files.

Memory Snapshotting Accelerates Cold Starts

Modal supports Memory Snapshots for Functions, including alpha GPU Memory Snapshots; Modal Sandboxes also support alpha memory snapshots. Memory Snapshots can reduce startup time for initialization-heavy workloads, especially imports, JIT compilation, and runtime initialization. For AI app builders with initialization-heavy ML pipelines, this means faster response times when scaling from zero without maintaining always-on infrastructure.

For teams building AI app platforms that require secure code execution, production-grade reliability, and on-demand GPU access, Modal's combination of sandboxed execution, enterprise compliance, and proven scale makes it the clear choice. Explore the Modal documentation to get started.