This guide examines seven code execution sandbox platforms serving different OpenInspect-style use cases in 2026, starting with Modal, a serverless compute platform built for secure sandboxed execution at scale with GPU support.
Key Takeaways
- Secure isolation is foundational for inspection workflows: OpenInspect-style workflows run untrusted code that requires strict sandboxing. Modal uses gVisor-isolated containers, while platforms like E2B employ Firecracker microVMs for hardware-level separation
- GPU access expands inspection capabilities: Modal offers GPU-backed sandboxes with documented options including L4, A100, H100, H200, and B200, enabling ML-powered code analysis and inspection models to run alongside standard execution. Several competitors, including Daytona and Northflank, also advertise GPU-capable sandbox or workload infrastructure
- Massive concurrency supports production-scale inspection: Modal advertises 100k+ concurrent sandboxes with sub-second scheduling and strong cold-start performance, enabling OpenInspect-style workflows to process thousands of inspection jobs simultaneously, with higher concurrency available on Enterprise plans
- Code-first SDKs accelerate development: Modal's SDKs let teams define sandboxes directly in code without YAML or infrastructure management, with code-defined infrastructure available in Python, TypeScript, and Go. Code running inside a sandbox is not limited to one language and can use whatever runtime the workload requires
- Enterprise compliance matters for sensitive workloads: Modal has completed a SOC 2 Type II audit and supports HIPAA-compliant workloads on Enterprise plans via a BAA, meeting security requirements for regulated inspection workflows
1. Modal
Modal delivers serverless compute for secure sandboxed execution at massive scale, the core requirement for OpenInspect-style workflows that run untrusted code. The platform reports 1 billion+ sandboxes run and supports on-demand GPU access for inspection workloads that require ML acceleration.
Core Capabilities
- gVisor container isolation: Secure sandboxed execution protects against untrusted code, with each sandbox running in an isolated environment with controlled filesystem and network access
- 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
- Sandbox snapshotting: Modal supports Sandbox snapshotting to reduce startup latency, including filesystem and directory snapshots, with memory snapshots available in alpha
- GPU support inside sandboxes: Modal supports GPU-enabled sandboxes, with documented GPU options including L4, A100, H100, H200, and B200, enabling ML-powered inspection and code analysis
- 100k+ concurrent sandboxes: Architecture advertised to scale to hundreds of thousands of isolated environments simultaneously with sub-second scheduling
Security and Compliance
Modal has completed a SOC 2 Type II audit with no deviations and undergoes annual audits, 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, and encryption for data in transit and at rest. Comprehensive security practices include documented vulnerability remediation timeframes by severity and audited access controls.
Production-Proven Results
Modal powers production sandbox workloads for notable AI companies:
- Ramp uses Modal Sandboxes to power a background coding agent that generates code changes and writes them back into commits and pull requests
- Lovable reports Modal was "the only infrastructure provider that enabled us to reliably run tens of thousands of app creation sessions in an instant"
- Applied Compute describes sandboxes as "one of the most important building blocks for RL," noting Modal's flexibility and focus on performance and reliability
What Makes Modal Unique
- Unified AI platform: Sandboxes integrate seamlessly with Modal Functions, Volumes, and networking primitives, reducing vendor complexity for inspection workflows
- Code-first SDKs: Define sandbox environments, scaling behavior, and compute requirements directly in code, with SDKs available in Python, TypeScript, and Go and no configuration files required. Sandboxes can run workloads in any language, not just Python
- Multi-cloud capacity pool: Modal pools GPU and CPU capacity across major cloud providers so teams do not need to manage cloud-provider reservations directly
Best For: OpenInspect-style teams that need secure code execution at massive scale, GPU-accelerated inspection models, and a unified platform that handles sandboxes, inference, and batch processing together.
2. E2B
E2B specializes in secure sandboxes for AI agents, focusing on ephemeral code execution with Firecracker microVM isolation. The platform self-reports usage by 94% of Fortune 100 companies. E2B also self-reports 1B+ started sandboxes.
Core Capabilities
- Firecracker microVMs: Hardware-level isolation provides strong security boundaries for running untrusted AI-generated code
- Cold starts: E2B supports cold starts for its sandbox environments
- Multi-language support: Native SDKs for Python, JavaScript, and TypeScript with compatibility across LLM frameworks like LangChain and LlamaIndex
- Template system: Reproducible sandbox environments with versioning for consistent inspection configurations
Use Case Focus
E2B excels at ephemeral code execution, spinning up isolated environments for agents to run generated code, then tearing them down. E2B's Hobby/free tier supports up to 20 concurrently running sandboxes; higher concurrency is available on paid plans.
Notable Customers
E2B case studies and customer quotes state that the following companies use E2B for large-scale code execution and agent workflows:
- Perplexity is reported to use E2B to scale to thousands of concurrent sessions
- Hugging Face is reported to use E2B to launch hundreds of sandboxes for training experiments
- Groq integrated E2B for secure code execution in compound AI models
Best For: OpenInspect-style workflows focused on ephemeral code execution and testing where Firecracker-level isolation is the priority and GPU acceleration is not required.
3. Northflank
Northflank provides a full-stack infrastructure platform with sandbox capabilities, offering flexible isolation options and bring-your-own-cloud (BYOC) deployment. The platform self-reports running millions of isolated/microVM workloads monthly and supports GPU access alongside CPU sandboxes.
Core Capabilities
- Multiple isolation options: Northflank documents microVM-backed workload isolation using Kata Containers or gVisor based on security and performance requirements
- BYOC deployment: Self-serve deployment into AWS, GCP, Azure, or bare-metal infrastructure for organizations with data residency requirements
- GPU support: Northflank supports GPU workloads and advertises GPU options including H100, H200, B200, A100, L4, and L40S
- Unlimited session duration: Sandboxes can run indefinitely without platform-imposed time caps
Architecture Approach
Northflank positions itself as a comprehensive infrastructure platform where sandboxes are one component alongside databases, CI/CD, and application hosting. This approach benefits OpenInspect-style deployments that need sandboxes integrated with broader infrastructure.
Migration Evidence
The platform documented a case where cto.new migrated their entire sandbox infrastructure in two days, going from unpredictable provisioning to thousands of daily deployments.
Best For: OpenInspect-style teams with strict compliance requirements needing BYOC deployment, or those wanting multiple isolation options per workload type.
4. CodeSandbox
CodeSandbox offers microVM-based sandboxes with advanced forking and snapshotting capabilities. A Together AI company, the platform focuses on development-oriented sandbox environments with state management features.
Core Capabilities
- MicroVM isolation: Each sandbox runs in an isolated microVM environment for secure code execution
- Advanced snapshotting: Capture and restore sandbox state for reproducible inspection workflows
- Forking support: Branch sandbox environments to test variations of inspection configurations in parallel
- Dockerfile/container-based environments: Support for Dockerfile-based environment configuration
Architecture Approach
CodeSandbox emphasizes branching and forking workflows, allowing users to snapshot an inspection environment and create parallel branches for testing different code paths or configurations.
Snapshotting and Cloning
CodeSandbox supports snapshot restore and VM or snapshot cloning. This suits workflows where environment setup happens less frequently than code execution.
Best For: OpenInspect-style workflows that benefit from snapshotting and forking capabilities, particularly when testing multiple inspection configurations in parallel.
5. Fly.io Sprites
Fly.io Sprites provides sandbox environments built on Firecracker microVMs with persistent storage options. The platform offers checkpoint and restore capabilities for stateful inspection workflows.
Core Capabilities
- Firecracker microVMs: Hardware-level isolation for secure execution of untrusted code
- Persistent storage: 100GB NVMe storage per sandbox for caching inspection artifacts and dependencies
- Checkpoint/restore: Fly.io Sprites supports checkpoint and restore for sandbox state
- Usage-based billing: Billing tied to CPU time, memory, and storage; compute billing stops when a Sprite is warm-paused
Architecture Approach
Fly.io Sprites emphasizes persistent development environments that maintain state across sessions. This benefits OpenInspect-style workflows that accumulate cached dependencies or intermediate results over time.
Cold Start Characteristics
Fly.io Sprites supports cold starts, with checkpoint restore enabling resumption of previously snapshotted states.
Best For: OpenInspect-style deployments that need persistent sandbox storage and checkpoint/restore capabilities for stateful inspection workflows.
6. RunLoop.ai
RunLoop.ai is purpose-built for AI agent development and benchmarking, with sandbox infrastructure designed around agent acceleration and evaluation workflows.
Core Capabilities
- Agent-focused design: Infrastructure specifically designed for AI agent development, testing, and benchmarking use cases
- Suspend/resume devboxes: RunLoop devboxes can be suspended to stop compute and memory charges while preserving disk state
- MicroVM isolation: Secure execution environment for running agent-generated code
- VPC deployment: RunLoop offers Deploy to VPC for enterprise customers, supporting deployment into the customer's AWS environment
Use Case Focus
RunLoop.ai positions itself around agent performance benchmarking and evaluation, making it relevant for OpenInspect-style workflows that assess agent behavior or code generation quality.
Integration Model
The platform provides SDKs for programmatic sandbox control, with configurable runtime persistence and resource allocation per sandbox.
Best For: OpenInspect-style teams focused on agent benchmarking, evaluation workflows, or quality assessment of AI-generated code.
7. Daytona
Daytona provides open-source development environment infrastructure with sandbox capabilities. The platform's GitHub repository has accumulated significant community interest, and it offers both self-hosted and managed options.
Core Capabilities
- Docker/OCI-compatible infrastructure: Daytona provides Docker/OCI-compatible sandbox infrastructure with isolated compute resources. Sysbox may apply to specific self-hosted runner configurations rather than as the general sandbox architecture
- Configurable persistence: Sandboxes can run indefinitely, with auto-stop after 15 minutes of inactivity by default
- GPU support: Available for ML workloads alongside persistent storage options
- Open-source option: Self-hosting available for organizations requiring full infrastructure control
Architecture Approach
Daytona focuses on persistent workspaces that maintain state across sessions. Sandboxes preserve context, cached dependencies, and intermediate results without recreation overhead, benefiting inspection workflows that build on previous state.
Docker/OCI Compatibility
Standard container image support enables flexible environment configuration using existing Dockerfiles and OCI images.
Best For: OpenInspect-style teams that prefer open-source infrastructure, need self-hosting options, or want persistent development environments with GPU access.
Why Modal Stands Out for OpenInspect-style Workflows
GPU-Enabled Sandboxes for ML-Powered Inspection
Modal stands out for combining GPU-backed sandbox execution with a unified serverless AI platform, code-first development, and massive sandbox concurrency. Documented GPU options include L4, A100, H100, H200, and B200. For inspection workflows that run ML models for code analysis, vulnerability detection, or automated review, this enables running inference directly within the secure sandbox rather than making external API calls. Several competitors also advertise GPU-capable sandbox or workload infrastructure, so the differentiation should be framed around Modal's platform integration, scale, and developer experience rather than GPU exclusivity. The deep GPU capacity pool across multiple cloud providers helps ensure availability without managing cloud-provider reservations directly.
Massive Concurrency for Production-Scale Inspection
Modal advertises 100k+ concurrent sandboxes with sub-second scheduling and strong cold-start performance. For deployments processing thousands of inspection jobs simultaneously, this scale helps address infrastructure bottlenecks, with higher concurrency available on Enterprise plans. Companies like Lovable run "tens of thousands of app creation sessions" on Modal's sandbox infrastructure.
Unified Platform Reduces Complexity
Modal Sandboxes work seamlessly with the broader Modal platform, including Volumes for persistent storage, Secrets for credential management, and networking primitives for controlled connectivity. This unified approach means teams manage one platform rather than stitching together separate services for sandboxes, storage, and compute.
Code-First Development Experience
Modal's SDKs let teams define sandbox environments directly in code, with code-defined infrastructure available in Python, TypeScript, and Go. Container images, resource requirements, networking rules, and scaling behavior are all specified programmatically without YAML configuration or infrastructure-as-code overhead. Code running inside a sandbox is not limited to one language and can use whatever runtime the workload requires. This code-first approach accelerates iteration for inspection workflow development.
Enterprise Security and Compliance
For deployments handling sensitive code or operating in regulated environments, Modal provides SOC 2 Type II audit completion and supports HIPAA-compliant workloads on Enterprise plans via a BAA. The platform's security practices include gVisor-based sandboxing, TLS 1.3 encryption, and documented vulnerability remediation timeframes by severity.
Production-Proven Reliability
Modal powers cloud infrastructure for over 10,000 teams, including AI companies like Ramp, Lovable, and Applied Compute. This production track record demonstrates the platform's ability to handle enterprise-scale sandbox workloads reliably, reducing operational risk for inspection deployments.
Modal is the strongest fit for OpenInspect-style production workloads when teams need secure sandboxed execution, GPU-backed AI workloads, code-first orchestration, and large-scale serverless infrastructure in one platform. Some competitors provide strong sandbox primitives, but Modal's advantage is the combination of sandbox scale, GPU access, AI infrastructure primitives, and low-operational-overhead deployment.
