Key Takeaways
- Isolation technology matters for untrusted code: Sandboxes use different isolation approaches: Modal uses gVisor containers, E2B employs Firecracker microVMs, and Daytona uses Docker/OCI-compatible images with isolated sandbox instances. The choice affects security boundaries and performance characteristics for AI-generated code execution.
- Cold start performance varies by platform: Daytona supports cold starts for sandbox creation, Modal is engineered for fast cold starts for relevant coding-agent workloads, and RunPod supports cold starts that vary by configuration, with pre-warmed and FlashBoot options available. Faster cold starts benefit interactive coding workflows, while factors like GPU availability and configuration can also influence startup behavior.
- GPU access separates general sandboxes from AI-native platforms: Modal provides extensive GPU support from T4 through B200, while E2B focuses on CPU-only sandboxes. Teams augmenting code with ML models need platforms that combine secure execution with on-demand GPU acceleration.
- Network controls protect production deployments: Modal Sandboxes can block all outbound networking, expose sandbox services through Connect Tokens or encrypted tunnels, and use Modal Proxies for static egress IPs on supported plans. These controls matter for running AI-generated code in multi-tenant production environments.
- Enterprise compliance requirements vary by platform: Modal maintains SOC 2 Type II certification and supports HIPAA-compliant workloads on Enterprise plans via a BAA, while other platforms offer self-hosting options for data sovereignty needs.
1. Modal
Modal delivers serverless compute for secure code execution at scale, with gVisor-based sandboxing that supports 100,000+ concurrent sandboxes for appropriate production-scale deployments, with actual workspace limits depending on plan and capacity. The platform powers cloud infrastructure for over 10,000 teams including AI companies building coding agents, code interpreters, and AI-augmented development tools.
Core Sandbox Capabilities
- gVisor container isolation: Secure sandboxed execution using gVisor, which provides application-level kernel isolation for running untrusted AI-generated code
- 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
- Massive concurrency: Scale to 100,000+ concurrent sandboxes for appropriate production-scale deployments with full observability for monitoring sandbox behavior; actual limits depend on plan and capacity
- Network controls: Sandboxes can block all outbound networking, expose services through Connect Tokens or encrypted tunnels, and use Modal Proxies for static egress IPs on supported plans for running untrusted code in production multi-tenant environments
GPU Support for Code Augmentation
Unlike CPU-only sandbox platforms, Modal provides extensive GPU support spanning T4, L4, A10, L40S, A100 variants, RTX PRO 6000, H100, H200, and B200. This enables coding tools to:
- Run ML models for code generation and analysis
- Execute compute-intensive code augmentation workflows
- Access GPU acceleration on-demand without managing infrastructure
Security and Compliance
Modal maintains SOC 2 Type II certification and supports HIPAA-compliant workloads on Enterprise plans via a BAA. The platform uses:
- gVisor-based sandboxing for compute isolation
- TLS 1.3 for public APIs
- Encryption for data in transit and at rest
- Comprehensive security practices including external pen testing
Developer Experience
Modal's code-first SDKs eliminate YAML configuration overhead. Teams define sandbox environments, compute requirements, and scaling behavior directly in code:
- Code-first SDKs across Python, TypeScript, and Go: Modal provides code-defined infrastructure with SDKs in Python, TypeScript, and Go for interacting with Modal resources such as Functions and Sandboxes; code running inside a sandbox is not limited to one language, so a sandbox can run whatever runtime or language the workload requires
- Memory snapshotting: Memory Snapshots can reduce cold starts for initialization-heavy Functions and Sandbox workflows; GPU Memory Snapshots are currently in Alpha
- Rich observability: Per-input monitoring and logging for debugging sandbox behavior
Best For: Teams building coding agents, code interpreters, or AI-augmented development tools that need secure execution at scale with on-demand GPU access, particularly those requiring enterprise-grade security and compliance.
2. E2B
E2B specializes in secure sandboxes for AI agents, focusing on ephemeral code execution with Firecracker microVM isolation. The platform raised $21M in Series A funding in 2025 and positions itself around lightweight sandboxes for agent code execution.
Core Capabilities
- Firecracker microVMs: Hardware-level isolation using the same technology that powers AWS Lambda, providing strong security boundaries for untrusted code
- Cold starts: Supports cold starts for spinning up isolated environments
- Open-source option: Self-hosting available for organizations with data sovereignty requirements
- Multi-language support: Python, JavaScript/TypeScript, R, Java, and Bash execution environments
Session and Concurrency Limits
E2B structures its offerings around session duration and concurrency:
- Hobby tier includes up to 20 concurrent sandboxes with 1-hour sessions
- Pro tier supports up to 100 concurrent sandboxes with 24-hour sessions
- Pro users can purchase additional concurrency up to 1,100 sandboxes, while Enterprise terms are custom
Use Case Focus
E2B excels at ephemeral code execution: spinning up isolated environments for agents to run generated code, then tearing them down. The platform's Firecracker-based isolation provides strong security for running untrusted code from AI systems.
Best For: Teams building coding agents focused purely on code execution and testing where GPU acceleration is not required, particularly those needing ephemeral code execution or self-hosting capabilities.
3. Daytona
Daytona provides development environments with sandbox creation capabilities. The platform offers both cloud and self-hosted options, positioning itself around persistent workspaces rather than purely ephemeral execution.
Core Capabilities
- Environment creation: Supports cold starts for spinning up sandbox environments
- Docker/OCI image support: Isolated sandbox instances with dedicated vCPU, RAM, and disk resources and their own Linux namespaces, built from Docker or OCI-compatible images
- Configurable persistence: Sandboxes can maintain state across sessions or run ephemerally
- Self-hosting option: Deploy on your own infrastructure for compliance requirements
- GPU support: Enterprise GPU availability is not detailed in Daytona's public documentation
Architecture Approach
Daytona focuses on persistent workspaces that maintain state across sessions. This approach benefits:
- Agents that need to preserve cached dependencies
- Workflows requiring intermediate results without recreation overhead
- Development environments that maintain context over time
Integration Patterns
Daytona supports integration through Python and TypeScript SDKs, with compatibility for standard Docker/OCI container images.
Best For: Teams building coding agents that require persistent development environments, workspace continuity across sessions, or self-hosting for compliance requirements.
4. RunPod
RunPod is a GPU cloud provider that offers serverless execution capabilities alongside its core GPU rental business. The platform announced a $20M Seed round in May 2024, co-led by Intel Capital and Dell Technologies Capital, and provides access to 25+ GPU types.
Core Capabilities
- Extensive GPU variety: A broad GPU catalog, with 25+ GPU types on its pricing materials and a longer set of GPU IDs in its technical reference, including recent hardware
- Serverless mode: Pay-per-second GPU execution without managing infrastructure
- Docker-first approach: Full container control with standard Docker workflows
- Flexible deployment: Choose between serverless, on-demand, and reserved capacity
Sandbox Considerations
RunPod's isolation model uses Docker containers, providing process-level separation. The platform is optimized for GPU workloads rather than high-concurrency code execution.
Cold Start Performance
RunPod cold-start latency varies by endpoint configuration, pre-warming, FlashBoot eligibility, and container or model size. RunPod materials describe pre-warmed and FlashBoot options, while larger model-loading workloads can take longer.
Best For: Teams with GPU-heavy code augmentation workloads who prioritize GPU variety and cost optimization over sandbox-specific features like network controls or massive concurrency.
5. Replicate
Replicate operates as a model hosting platform with a large community marketplace of pre-built models. The platform focuses on model inference rather than general-purpose code execution.
Core Capabilities
- Model marketplace: Access to a large library of community-contributed models
- Cog packaging: Python-based model packaging format for deployment
- Simple API: Straightforward model inference without infrastructure management
- Quick deployment: Models can be deployed and called via API in minutes
Sandbox Scope
Replicate's execution environment is model-centric rather than general-purpose. The platform supports custom model code packaged with Cog for model inference APIs, but it is not positioned as a general-purpose interactive code execution sandbox for agent workflows with arbitrary shell access, persistent sessions, and workspace-style filesystem operations.
Use Case Fit
Replicate works well for:
- Teams that want to access pre-trained models without hosting them
- Quick prototyping with community models
- Inference workloads where the model already exists in Replicate's marketplace
Best For: Teams focused on model inference who want access to a marketplace of pre-built models rather than running custom code or building agent infrastructure.
6. Baseten
Baseten focuses on ML model deployment for enterprise teams, providing infrastructure for serving trained models in production.
Core Capabilities
- Enterprise ML serving: Production deployment infrastructure for ML models
- Model deployment pipelines: Workflows for getting models from training to production
- Truss framework: Open-source model serving framework
- Autoscaling inference: Scale model serving based on demand
Sandbox Scope
Baseten's execution environment is oriented toward model inference rather than general code execution. The platform supports deploying custom models but isn't designed for sandbox-style arbitrary code execution or agent workflows.
Architecture Approach
Baseten emphasizes enterprise features like deployment pipelines, monitoring, and model versioning. The platform serves teams with established ML workflows looking for production serving infrastructure.
Best For: Enterprise teams focused on deploying and serving ML models in production, rather than running arbitrary code or building agent-based systems.
7. Fly.io
Fly.io is a general-purpose edge compute platform that runs containerized apps close to users globally as hardware-virtualized Fly Machines backed by Firecracker microVM isolation. Its core platform is general-purpose, though Fly now also offers Sprites, a Firecracker-based sandbox product for arbitrary and AI-generated code.
Core Capabilities
- Global edge deployment: Run containerized apps in data centers worldwide for low-latency access
- Hardware-virtualized execution: Deploy standard Docker containers as Fly Machines backed by Firecracker microVMs
- Persistent volumes: Attach storage to running machines
- General serverless compute: Not AI-specific but flexible for various workloads
Sandbox Considerations
Fly.io provides hardware-virtualized isolation through Firecracker microVMs, and its positioning relative to AI-specific sandbox platforms has shifted in 2026:
- GPU support: Fly.io currently offers GPU support across NVIDIA A10, L40S, A100 40GB PCIe, and A100 80GB SXM, though its GPU documentation states GPUs are deprecated and unavailable after August 1, 2026
- Sandbox controls: Fly now offers Sprites, Firecracker-based sandboxes for arbitrary and AI-generated code with persistence, checkpoints, isolated networking, and fine-grained Layer 3 network egress policies
- Orchestration scope: The core Fly Machines product remains general-purpose rather than purpose-built for high-concurrency AI sandbox orchestration or integrated AI/ML framework workflows
Use Case Fit
Fly.io works for teams that need general container hosting with global distribution, and via Sprites it now offers persistent Firecracker-based sandboxes for arbitrary code. For integrated GPU acceleration and AI-native serverless orchestration at scale, purpose-built platforms offer better-suited features.
Best For: Teams with general edge-deployed apps and, via Sprites, persistent Firecracker-based sandboxes for arbitrary code; less suitable than Modal where teams need integrated GPU acceleration, AI-native serverless orchestration, and enterprise-scale sandbox and GPU workflows in one platform.
Why Modal Stands Out for Code Augmentation Sandboxes
Purpose-Built AI Infrastructure
Modal's architecture is specifically engineered for AI workloads. The platform's custom container runtime, scheduler, and file system are optimized for the unique demands of secure code execution, GPU-accelerated computation, and dynamic scaling that code augmentation tools require.
Production-Grade Sandbox Security
Modal's sandboxes use gVisor isolation, providing strong security boundaries for running untrusted AI-generated code. The platform supports 100,000+ concurrent sandboxes for appropriate production-scale deployments, with actual limits depending on plan and capacity, including:
- The ability to block all outbound networking, plus Modal Proxies for static egress IPs on supported plans
- Full observability for monitoring sandbox behavior
- Connect Tokens and encrypted tunnels for secure connectivity patterns
GPU Access When Code Augmentation Needs It
Code augmentation often requires ML models for code generation, analysis, or understanding. Modal provides extensive GPU support from T4 through B200, letting coding tools access acceleration on-demand without managing GPU infrastructure.
Developer Experience Without Compromise
Modal's code-first SDKs eliminate configuration overhead. Teams define sandboxes, compute requirements, and scaling behavior directly in code, with no YAML or infrastructure configuration required. Modal offers SDKs across Python, TypeScript, and Go, and code running inside a sandbox can use whatever runtime or language the workload requires. This enables faster iteration cycles for coding tool development.
Enterprise Security and Compliance
With SOC 2 Type II certification, HIPAA-compliant workloads on Enterprise plans via a BAA, and comprehensive security practices including gVisor sandboxing and TLS 1.3, Modal meets the compliance requirements that enterprise code augmentation deployments demand.
Proven Scale
Modal powers cloud infrastructure for over 10,000 teams, demonstrating the platform's ability to handle production-scale workloads reliably. Production coding-agent users include Ramp, which runs background coding agents on Modal Sandboxes to generate code changes and write them back into commits and pull requests, and Lovable, which uses Modal Sandboxes as preview environments for generated apps and websites. This track record provides confidence for teams building coding tools that need to scale.
For teams building code augmentation tools that require secure execution, production-grade reliability, and on-demand GPU access, Modal's combination of AI-native infrastructure, sandbox security features, and proven enterprise scale makes it the clear choice. Explore the Modal documentation to get started with secure sandboxes for code augmentation.
Explore the Modal documentation to get started with secure sandboxes for code augmentation.
View Modal Docs