AI applications that execute code in real-time, from coding agents to LLM-powered interpreters, require secure, scalable infrastructure that can handle unpredictable workloads. Streaming code execution presents unique challenges: untrusted AI-generated code must run in isolated environments, scale instantly to meet demand, and deliver results with minimal latency. Choosing the right sandbox platform determines whether your AI application can execute code safely, handle concurrent sessions at scale, and access GPU acceleration when ML workloads require it.
This guide examines seven sandbox platforms for streaming code execution in 2026, starting with Modal, a serverless compute platform that combines secure code execution at massive scale with GPU-enabled sandbox environments.
Key Takeaways
- Security isolation is non-negotiable for AI-generated code: Sandboxes protect against malicious or buggy code from LLM agents. Modal uses gVisor-based containers, while E2B and Vercel employ Firecracker microVMs for hardware-level isolation
- GPU support varies across platforms: Modal offers GPU-enabled sandboxes with GPU types from T4 through B200/B200+, enabling agents to run inference alongside code execution. Daytona and Northflank also document GPU sandbox types, while E2B has no publicly documented GPU sandbox offering
- Concurrency limits vary by provider and plan: Modal's Sandboxes product page advertises 100k+ concurrent sandboxes. E2B publishes lower public-plan limits (20 on Hobby, 100 on Pro, with add-ons up to 1,100), while Blaxel and Northflank advertise higher-scale capacity
- Cold start performance impacts real-time AI applications: Blaxel supports resume from standby and Daytona supports cold starts, while Modal emphasizes fast cold starts, with enabling techniques such as memory snapshotting and an optimized filesystem able to reduce initialization overhead for suitable workloads
- Code-first SDKs accelerate development: Modal is code-defined with no YAML configuration, providing code-first SDKs in Python, TypeScript, and Go for sandbox operations and resource management. Code running inside a sandbox is not limited to one language; sandboxes can run whatever runtime or language the workload requires
1. Modal
Modal delivers serverless compute infrastructure purpose-built for AI workloads, with secure sandboxes that handle streaming code execution at massive scale. The platform combines gVisor-based isolation with GPU-enabled sandboxes, letting teams run untrusted code securely and attach GPUs for ML workloads in the same Modal environment.
Core Capabilities
- gVisor container isolation: Secure sandboxed execution for AI-generated code, with comprehensive security practices including TLS 1.3 for APIs and encryption for data in transit and at rest
- GPU-accelerated sandboxes: Access to T4, L4, A10, L40S, A100 variants, RTX PRO 6000, H100, H200, and B200/B200+ within sandbox environments
- Scale-to-zero architecture: Pay for compute you use, with instant autoscaling to thousands of containers and no idle infrastructure costs
- Fast cold starts: Engineered for fast cold starts and faster feedback loops, with an optimized filesystem that helps containers come online quickly without letting large images slow startup down
- Code-first SDKs: Define sandbox environments programmatically with no YAML configuration, using code-first SDKs in Python, TypeScript, and Go for sandbox operations and resource management. Sandboxes themselves can run code in any language or runtime the workload requires
- Memory snapshotting: Modal offers filesystem snapshots, and Sandbox memory snapshots are in early preview, capturing filesystem and memory state. Memory snapshotting can reduce initialization overhead for suitable workloads
Security and Compliance
Modal has successfully completed a SOC 2 Type 2 audit, with the report available through its Security Portal, and Modal supports HIPAA-compliant workloads on Enterprise plans via a Business Associate Agreement. The platform's security architecture includes gVisor-based sandboxing for compute isolation, TLS 1.3 for public APIs, and encryption for data at rest and in transit.
Production-Proven Scale
Modal powers cloud infrastructure for over 10,000 teams, including companies running production coding agents:
- Lovable uses Modal Sandboxes to generate applications at scale, describing Modal as "the only infrastructure provider that enabled us to reliably run tens of thousands of app creation sessions." Modal's case study reports that Lovable used Modal Sandboxes for every app generation session, ran over 1 million sandboxes during a promotional weekend, and peaked at 20,000 concurrent sandboxes
- Ramp uses Modal Sandboxes to power Inspect, an internal background coding agent that Modal says writes over half of all merged pull requests at Ramp
- Quora uses Modal Sandboxes to securely execute LLM-generated code in Poe, its AI chatbot platform, and stress-tested Sandbox creation throughput to 1,000 Sandboxes per second
What Makes Modal Unique
- GPU-enabled sandboxes: Modal supports GPU-enabled sandboxes with GPU types from T4 through B200/B200+, so agents can run ML inference, fine-tuning, or compute-intensive analysis in the same environment as code execution. GPU availability varies across providers: Daytona and Northflank also document GPU sandbox types, while E2B has no publicly documented GPU sandbox offering
- Dynamic runtime environment definition: Sandbox environments can be defined programmatically at creation time, enabling LLMs to specify their own execution environments
- Unified AI infrastructure: Inference, training, batch processing, notebooks, and sandboxes are part of the same Modal platform
Best For: Teams building AI applications that need secure code execution at scale, with on-demand GPU access for ML inference, model fine-tuning, or compute-intensive analysis alongside code generation.
2. E2B
E2B provides secure sandboxes built specifically for AI agents, using Firecracker microVM isolation. E2B self-reports Fortune 100 adoption, with its homepage currently stating 94% (some E2B docs show 88%). Its open-source repository has approximately 12.6k+ GitHub stars.
Core Capabilities
- Firecracker microVMs: Hardware-level isolation provides strong security boundaries for running untrusted code
- Cold starts: E2B supports cold starts when spinning up new sandboxes for agent workloads
- Agent framework integrations: Native SDKs for LangChain, OpenAI Agents, and Anthropic workflows
- Template system: Pre-built and custom sandbox templates for reproducible environments
Use Case Focus
E2B excels at ephemeral code execution patterns, spinning up isolated environments for AI agents to run generated code, then tearing them down. The platform is purpose-built for agent workflows, with SDKs designed for rapid integration with popular LLM frameworks.
Best For: Teams building AI agents focused on code execution and testing who prioritize agent framework integrations, particularly for CPU-only workloads.
3. Northflank
Northflank offers a full-stack cloud platform with sandboxes as one component, and says it has been running millions of microVMs monthly since 2021. The platform provides multiple isolation technologies and self-serve bring-your-own-cloud (BYOC) deployment across major providers.
Core Capabilities
- Multiple isolation options: Northflank supports sandbox isolation using Kata Containers or gVisor, with VMM technologies such as Firecracker or Cloud Hypervisor discussed in its architecture materials
- BYOC deployment: Self-serve deployment in AWS, GCP, Azure, Oracle, CoreWeave, Civo, or bare-metal infrastructure
- Unlimited session duration: Sandboxes can run indefinitely without time limits
- GPU support: Access to L4, A100, H100, and H200 GPUs within sandbox environments
- OCI image compatibility: Run any containerized workload without SDK lock-in
Architecture Approach
Northflank positions sandboxes within a broader platform that includes databases, APIs, and job scheduling. This makes it suitable for teams that need sandboxes alongside other infrastructure components, particularly those with data residency requirements that mandate BYOC deployment.
Best For: Enterprise teams requiring BYOC deployment, multiple isolation technology options, or unlimited session duration for long-running workloads.
4. Daytona
Daytona provides persistent development environments and supports sandbox creation for AI workloads. The platform pivoted to AI agent infrastructure in 2025 and has built integrations with LangChain for coding agent workflows.
Core Capabilities
- Sandbox creation: Daytona supports sandbox creation for code execution workflows
- GPU support: Access to H100 and RTX PRO 6000 GPUs for ML workloads
- Configurable persistence: Sandboxes can be configured for extended runtime with auto-stop on inactivity
- Docker/OCI compatibility: Standard container image support for flexible environment configuration
- Open-source foundation: Self-hosting available alongside managed offerings
Architecture Approach
Daytona uses sysbox-based container isolation, a Docker-compatible container runtime. This provides a familiar containerization model, though the isolation boundaries differ from microVM-based approaches.
Best For: Teams building coding agents that prefer persistent development environments with Docker compatibility.
5. Blaxel
Blaxel is a perpetual sandbox platform built for AI agents. Blaxel supports resume from standby. The platform focuses on stateful agent environments that maintain context across sessions.
Core Capabilities
- Perpetual sandboxes: Environments remain on automatic standby rather than being torn down after each task
- Resume from standby: Blaxel supports resume from standby for perpetual sandboxes
- Persistent storage: Volumes that survive sandbox destruction and recreation
- REST API and MCP server: File system and process access exposed for agent integration
- Template support: Reusable sandbox templates for code generation agents and PR review agents
Architecture Approach
Blaxel emphasizes continuity over ephemeral execution. Sandboxes retain shell history, installed dependencies, and context over time, which benefits agents that need persistent state across workflows rather than clean-room execution on every task.
Best For: Teams building AI agents that require persistent sandbox environments with resume from standby and continuity across sessions.
6. Vercel Sandbox
Vercel Sandbox provisions isolated Firecracker-powered Linux microVM sessions on demand, while sandbox filesystem and configuration state is persistent by default through snapshot and restore. It integrates with the Vercel platform and can be used alongside Next.js applications and the Vercel AI SDK.
Core Capabilities
- Firecracker microVMs: Each sandbox runs in an isolated Linux environment with its own filesystem, network, and process space
- Persistent-by-default state model: When a sandbox stops, the SDK snapshots the filesystem and preserves configuration, then restores it on resume; the underlying microVM sessions are provisioned on demand
- State persistence options: Automatic persistence can save filesystem state when a sandbox stops and restore it on resume
- Vercel platform integration: Works with the Vercel platform, including deployment authentication, and can be used alongside Next.js applications and the Vercel AI SDK
Architecture Approach
Vercel Sandbox is positioned as an execution layer for secure, isolated code running rather than a full infrastructure platform. Its fit is strongest for agent workflows involving repeated start-run-stop cycles or safe execution of generated code within the Vercel ecosystem.
Best For: Teams already using Vercel's infrastructure who need isolated code execution environments for AI agents or development workflows.
7. Cloudflare Sandbox
Cloudflare Sandbox is powered by Cloudflare Workers and Cloudflare Containers, with user code executing in isolated Linux containers while Workers use V8 isolates for the surrounding serverless runtime. Cloudflare runs a global network across 330+ cities, though sandbox containers are placed according to container-placement rules and request geography rather than executing in every location. The platform is exposed through a TypeScript-first SDK for sandbox lifecycle management.
Core Capabilities
- Python and Node.js execution: Run scripts, compile code, and process data in isolated containers
- TypeScript-first SDK: API for sandbox lifecycle management, command execution, file operations, and WebSocket connections
- Global network: Runs on Cloudflare's network across 330+ cities, with sandbox containers placed by container-placement rules and request geography rather than guaranteed to run in every location
- Isolated Linux containers: Each sandbox has an isolated filesystem and maintains state while active
- Configurable lifecycle controls: Supports keepAlive heartbeats and configurable sleep behavior; these are lifecycle controls rather than durable persistence, and Cloudflare advises designing for ephemeral state because containers can restart and lose state
Architecture Approach
Cloudflare Sandbox is built on Cloudflare Workers and Containers: user code runs in isolated Linux containers, while Workers provide the surrounding V8-isolate serverless runtime. The platform is geared toward code execution workflows that benefit from global distribution rather than GPU-heavy AI workloads.
Best For: Teams already on Cloudflare that want sandboxed code execution close to users, provided they design around container placement and per-user or per-region sandbox locality.
Why Modal Stands Out for Streaming Code Execution
GPU Support in Sandbox Environments
Modal supports GPU-enabled sandboxes with GPU types from T4 through B200/B200+, so AI applications can run ML inference, fine-tuning, and compute-intensive analysis alongside code execution, a capability that becomes more useful as agents grow more sophisticated. GPU availability varies across providers: Daytona and Northflank also document GPU sandbox types, while E2B has no publicly documented GPU sandbox offering.
Proven Scale for Production Workloads
Modal's Sandboxes product page advertises 100k+ concurrent sandboxes and over 1 billion sandboxes run. Concurrency limits vary by provider and plan, so direct comparisons depend on the specific platform and tier. This scale is validated by production deployments: Lovable used Modal Sandboxes for every app generation session, ran over 1 million sandboxes during a promotional weekend, and peaked at 20,000 concurrent sandboxes, while Quora uses Modal Sandboxes to securely execute LLM-generated code in Poe and stress-tested Sandbox creation throughput to 1,000 Sandboxes per second.
AI-Native Infrastructure Architecture
Modal's custom-built infrastructure, including its file system, container runtime, scheduler, and image builder, is engineered specifically for AI workloads. Memory snapshotting can reduce initialization overhead for suitable workloads (Sandbox memory snapshots are in early preview), and the multi-cloud capacity pool helps with GPU availability.
Developer Experience Without Compromise
Modal is code-defined with no YAML configuration, providing code-first SDKs in Python, TypeScript, and Go for sandbox operations and resource management, while sandboxes can run code in any language or runtime the workload requires. Teams define sandbox environments, compute requirements, and scaling behavior directly in code, which enables rapid iteration and makes it possible for LLMs to generate and modify sandbox configurations programmatically.
Enterprise Security and Compliance
Modal has successfully completed a SOC 2 Type 2 audit, with the report available through its Security Portal, and Modal supports HIPAA-compliant workloads on Enterprise plans via a Business Associate Agreement. The platform's gVisor-based sandboxing, TLS 1.3 encryption, and documented security practices support enterprise compliance requirements for running AI-generated code at scale.
For teams building AI applications that need streaming code execution with GPU acceleration, production-scale concurrency, and enterprise security, Modal's combination of capabilities makes it the clear choice for 2026 and beyond.
