This guide examines seven code execution sandbox platforms for LlamaIndex workflows in 2026, starting with Modal, a serverless compute platform built for secure execution at massive scale with broad GPU support.
Key Takeaways
- Secure isolation protects against untrusted code: LlamaIndex agents that run generated code do so autonomously, making sandboxed execution critical. Modal uses gVisor containers for isolation, while E2B employs Firecracker microVMs. These technologies are designed to isolate untrusted code and reduce escape risk rather than to guarantee that escape is impossible
- Broad GPU support enables ML-intensive LlamaIndex tools: Modal offers broad GPU access (T4 through B200+) for embedding generation, inference, and fine-tuning within sandbox environments, and Modal Sandboxes can also run with GPUs. Among the non-Modal platforms reviewed here, Daytona also documents GPU sandbox options
- Massive concurrency handles production workloads: Modal supports 100k+ concurrent sandboxes, essential for LlamaIndex applications serving many users simultaneously
- Code-first SDKs accelerate development: Modal is code-first and avoids YAML configuration, with SDKs and code-defined infrastructure available in Python, TypeScript, and Go for building applications, running Sandboxes, and calling Functions
- Enterprise compliance meets security requirements: Modal has completed a SOC 2 Type 2 audit and supports HIPAA-compliant workloads on Enterprise plans via a BAA
1. Modal
Modal delivers serverless compute for secure code execution at scale, the core sandbox workload for LlamaIndex agents, with on-demand GPU access for workloads that require acceleration. The platform takes your code, containerizes it, and executes it in the cloud with automatic scaling. Modal is code-first and provides SDKs and code-defined infrastructure in Python, TypeScript, and Go for building applications, running Sandboxes, and calling Functions (the TypeScript and Go SDKs are in Beta). Code running inside a sandbox is not limited to one language; a sandbox can run whatever runtime the workload requires.
Core Capabilities
- gVisor container isolation: Secure sandboxed execution for running AI-generated code, essential for LlamaIndex agents that execute untrusted code
- 100k+ concurrent sandboxes: Production-scale concurrency with fast scheduling, strong cold-start performance, and observability features including health and lifecycle state, resource utilization, logs, metrics, and live statuses
- 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
- Broad GPU support: Access to T4, L4, A10, L40S A100 variants, RTX PRO 6000, H100, H200, and B200 GPUs for ML-intensive LlamaIndex workflows
- Code-first SDKs: Define compute, storage, and networking in code without YAML configuration, using SDKs available in Python, TypeScript, and Go
- Memory snapshotting: Technology that can reduce cold-start latency for initialization-heavy workloads. CPU Memory Snapshots capture CPU memory state, and Sandbox memory snapshots capture both memory and filesystem state
Security and Compliance
Modal has completed a SOC 2 Type 2 audit 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. See Modal's security documentation and HIPAA post for details.
LlamaIndex Integration
Modal Sandboxes can serve as an execution backend for agent architectures that need to run AI-generated code in isolated containers. Agents can spawn sandboxes to execute generated code, access Volumes for persistent data, and leverage GPU acceleration for embedding generation or local model inference within the same platform. Modal's Sandboxes documentation covers running arbitrary commands and controlling sandboxes through the Python, TypeScript, and Go examples.
Best For: Teams building LlamaIndex agents that need secure code execution at massive scale, with on-demand GPU access for ML inference, embedding generation, or compute-intensive analysis.
2. E2B
E2B specializes in secure sandboxes for AI agents, focusing on ephemeral code execution with Firecracker microVM isolation. The platform reports that it has started over 1 billion sandboxes and that 94% of Fortune 100 companies use or have engaged with the platform. An earlier July 2025 Series A post said 88% of the Fortune 100 had signed up.
Core Capabilities
- Firecracker microVMs: Hardware-level isolation for running untrusted AI-generated code
- Cold starts: E2B supports sandbox cold starts
- Open-source option: Self-hosting available under Apache 2.0 license for organizations with data sovereignty requirements
- SDKs and framework examples: SDKs plus framework integration patterns and examples, including LlamaIndex, LangChain, and CrewAI
- MCP tool integrations: Support for 200+ MCP tools through the Docker MCP Catalog and custom MCP servers
Use Case Focus
E2B excels at ephemeral code execution, spinning up isolated environments for LlamaIndex agents to run generated code, then tearing them down. The platform focuses on CPU-based workloads without documented general-purpose GPU sandbox allocation.
Best For: Teams building LlamaIndex agents focused on code execution and testing where GPU acceleration is not required, particularly those valuing enterprise security validation.
3. Daytona
Daytona provides persistent development environments with sandbox creation times. The platform's public open source repository has accumulated approximately 72,500 GitHub stars and offers both GPU support and configurable runtime persistence.
Core Capabilities
- Cold starts: Daytona supports sandbox cold starts
- GPU support: Available NVIDIA H100 and NVIDIA RTX Pro 6000 GPUs for ML workloads
- Open-source legacy repository: The public repository remains available under its license, but as of June 2026 core development moved to a private codebase and the public repo is no longer maintained, though it remains public, free to use, and forkable
- Unlimited runtime: Sandboxes can be configured for indefinite runtime with persistent workspaces
- Docker/OCI compatibility: Standard container image support for flexible environment configuration
Architecture Approach
Daytona focuses on persistent workspaces that maintain state across sessions. This approach benefits LlamaIndex agents that need to preserve context, cached dependencies, or intermediate results without recreation overhead.
Best For: Teams building LlamaIndex agents that require persistent development environments and workspace continuity over ephemeral execution, with the understanding that core Daytona development moved to a private codebase as of June 2026.
4. Cloudflare Sandboxes
Cloudflare Sandboxes is a code execution environment exposed through the Sandbox SDK. Cloudflare positions it for running Python and Node.js workloads, executing commands, managing files, and supporting agent-style workflows through a TypeScript API.
Core Capabilities
- Python and Node.js execution: Support for running Python scripts, Node.js applications, and data-processing workloads
- TypeScript-first SDK: API for sandbox lifecycle management, command execution, file operations, and terminal access. July 2026 implementations should account for Cloudflare's June 2026 Sandbox SDK deprecation and migration changes, which affect certain features after July 9, 2026, including HTTP/WebSocket transports, desktop support, and `exposePort`, plus changes around default sessions and streaming APIs
- Edge network distribution: Global edge proximity for distributed execution
- Isolated Linux containers: Each sandbox has an isolated filesystem and runs in a dedicated Linux container
- Cloudflare ecosystem integration: Works with R2, D1, and Workers AI services
Use Case Focus
Cloudflare Sandboxes is oriented toward secure code execution and programmable sandbox workflows. The platform's edge-native architecture provides global distribution for LlamaIndex applications serving users across regions.
Best For: Teams building LlamaIndex agents that benefit from edge-distributed execution, prefer a TypeScript-first development model, and work within the Cloudflare ecosystem.
5. Vercel Sandbox
Vercel Sandbox is an isolated code execution environment built for running untrusted code in isolated Firecracker microVMs. Sessions are time-limited, while sandbox state can persist by default through snapshots. Vercel positions it for AI agents, code execution, testing, and development workflows requiring secure isolated environments.
Core Capabilities
- Firecracker microVMs: Each environment runs in an on-demand Linux microVM with isolated filesystem, network, and process space
- Persistent-by-default runtime model: Sessions are time-limited and auto-stop, but sandboxes are persistent by default, with filesystem and configuration snapshotted on stop and restored on resume
- Developer-friendly Linux access: Each sandbox includes sudo access, package managers, and standard command-line workflows
- State persistence options: Automatic persistence saves filesystem state when stopped and restores it when resumed
- Session duration flexibility: Support for sessions ranging from 45 minutes to 24 hours depending on plan
Architecture Approach
Vercel Sandbox serves as an execution layer for secure, isolated code running rather than a full infrastructure platform for GPU-heavy AI workloads. Its fit is strongest for agent workflows involving repeated start-run-stop cycles or short-lived tasks, with state preserved across stops by default.
Best For: Teams building LlamaIndex agents that need isolated environments for code execution, especially when the priority is secure isolated execution and Vercel ecosystem integration.
6. CodeSandbox
CodeSandbox, now part of Together AI, provides cloud development environments with sandboxed execution capabilities. In December 2024, Together AI said CodeSandbox was used by more than 4.5 million developers every month and offered snapshot/restore functionality.
Core Capabilities
- Startup paths: CodeSandbox supports snapshot restore, VM or snapshot cloning, and cold starts from scratch
- Together AI integration: The Together AI acquisition brought CodeSandbox's sandboxing technology into Together's code execution platform
- Firecracker-based isolation: Secure execution environment for untrusted code
- Collaborative features: Real-time collaboration and shareable environments
- Browser and microVM options: Dual environment support for different use cases
Use Case Focus
CodeSandbox serves teams that need prototyping and collaborative development environments. The Together AI acquisition brought CodeSandbox's sandboxing technology into Together's code execution platform.
Best For: Teams building LlamaIndex applications that benefit from collaborative development environments and prototyping with shareable sandbox links.
7. Blaxel
Blaxel is a sandbox platform built specifically for AI agents, focusing on persistent "agent computers" that stay on standby and resume when needed. The platform emphasizes secure sandboxed compute runtimes that resume from standby state.
Core Capabilities
- Resume from standby: Blaxel supports resume from standby state
- Perpetual sandboxes: Sandboxes remain on automatic standby rather than being torn down after each task
- Scalable concurrency: Blaxel's pricing table lists sandbox tiers ranging from 10 sandboxes on the free tier to 100,000+ on its highest listed tier
- Persistent storage: Volumes that survive sandbox destruction and recreation
- Template support: Reusable sandbox templates for standardized environments
Architecture Approach
Blaxel emphasizes persistent state rather than purely ephemeral execution. Sandboxes are treated as persistent computers that retain shell history, installed dependencies, and context over time, benefiting LlamaIndex agents that need continuity across workflows.
Best For: Teams building LlamaIndex agents that need persistent sandbox environments, resume from standby, and continuity across sessions rather than clean-room execution.
Why Modal Stands Out for LlamaIndex Workflows
Broad GPU Support for ML-Intensive Tasks
Modal offers broad GPU access spanning T4, L4, A10, L40S, A100 variants, RTX PRO 6000, H100, H200, and B200, and Modal Sandboxes can also run with GPUs. For LlamaIndex workflows that involve embedding generation, local model inference, or fine-tuning, this GPU breadth enables ML-intensive operations within the same sandbox environment where code execution occurs.
Production-Scale Concurrency
Modal supports 100k+ concurrent sandboxes with fast scheduling and strong cold-start performance, plus observability features including health and lifecycle state, resource utilization, logs, metrics, and live statuses. This scale matters for LlamaIndex applications serving production workloads where many agents execute code simultaneously.
Unified AI Infrastructure Platform
Unlike point solutions, Modal provides a unified platform combining sandboxes, GPU inference, batch processing, and persistent storage. LlamaIndex workflows can execute code in sandboxes, run inference on GPUs, process data in batch jobs, and store results in Volumes, all within a single vendor relationship.
Code-First Development Experience
Modal lets teams define container environments and compute specifications in code, with no YAML configuration required. Modal provides SDKs and code-defined infrastructure in Python, TypeScript, and Go for building applications, running Sandboxes, calling Functions, and interacting with resources like Volumes, Secrets, and Queues.
Enterprise Security and Compliance
Modal provides enterprise-relevant security and compliance features, including SOC 2 Type 2 audit completion, HIPAA BAA support for eligible Enterprise workloads, TLS 1.3 for public APIs, encryption in transit and at rest, and gVisor-based compute isolation. Modal's current security documentation and contract terms cover workload-specific compliance requirements.
Proven Scale and Reliability
Modal reports powering cloud infrastructure for over 10,000 teams and over 1 billion launched sandboxes. Production coding-agent workloads run on Modal Sandboxes today: Ramp built a full-context background coding agent on Modal that generates code changes and writes them back into commits or pull requests (see Ramp's engineering write-up), and Lovable uses Modal Sandboxes as preview environments for generated apps and websites. These are strong production-scale indicators for handling enterprise-scale agent workloads.
For teams building LlamaIndex agents that require secure code execution, production-grade reliability, and on-demand GPU access, Modal's combination of AI-native infrastructure, massive concurrency, and broad GPU support makes it a strong choice.
Explore the Modal documentation to get started with LlamaIndex workflow integration.
View Modal Docs