RLMs are an inference-time framework in which a language model can inspect, decompose, and recursively process context through a REPL-like environment. Some implementations execute code, evaluate results, and iterate on their own outputs in recursive loops, making sandboxed infrastructure relevant. RLM systems that execute untrusted code or run production workloads may require isolated sandbox environments with the performance needed for iterative execution. Choosing the right code execution sandbox determines whether your RLMs can operate safely, scale dynamically, and access GPU acceleration when complex workloads require it. This guide examines seven sandbox platforms serving different RLM needs in 2026, starting with Modal, a serverless compute platform built for secure AI code execution at scale with broad GPU support.
Key Takeaways
- Secure isolation is non-negotiable for RLM workloads: RLMs generate and execute code autonomously, making sandboxed execution critical. Modal uses gVisor containers for isolation, while E2B employs Firecracker microVMs; both approaches prevent AI-generated code from affecting other workloads
- GPU-enabled sandboxes unlock advanced RLM capabilities: Many code-execution sandbox providers remain CPU-oriented, while some now offer GPU support with important limitations around availability, persistence, and deployment model. Modal Sandboxes can be configured with GPUs, including T4, L4, A10, L40S, A100 variants, RTX-PRO-6000, H100/H100!, H200, and B200/B200+, enabling RLMs to run local inference, fine-tuning, and compute-intensive analysis within sandboxes
- Unified platforms reduce operational complexity: Modal combines sandboxes with inference, training, batch processing, and notebooks in one serverless platform, eliminating the need to manage multiple vendors for RLM infrastructure
- Cold start performance impacts RLM iteration speed: Sandbox startup times vary across platforms. Modal offers fast cold starts supported by optimized filesystems and snapshot capabilities, while Blaxel supports resume from standby. Faster cold starts enable tighter feedback loops for recursive model execution
- Enterprise compliance enables production deployment: Modal has completed a SOC 2 Type II audit and supports HIPAA-compliant workloads on Enterprise plans via a BAA, helping satisfy the security due diligence that enterprise RLM deployments require
1. Modal
Modal delivers serverless compute for secure code execution at scale, the core sandbox workload for RLMs, with on-demand GPU access for workloads requiring acceleration. The platform takes your code, containerizes it, and executes it in the cloud with automatic scaling; all defined through native Python, TypeScript, and Go SDKs. While the SDKs are code-first, sandboxes can run workloads in any programming language, not just Python.
Core Capabilities
- gVisor container isolation: Secure sandboxed execution for running AI-generated code, providing the isolation RLMs need to safely execute recursive code generation
- 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 snapshots can further reduce startup latency for returning sessions
- 100k+ concurrent sandboxes: Massive concurrency support enables RLMs to spawn thousands of parallel execution environments for distributed recursive processing
- Broad GPU support: Access to T4, L4, A10, L40S, A100 variants, RTX-PRO-6000, H100/H100!, H200, and B200/B200+ GPUs within sandboxes, enabling local model inference and GPU-accelerated code execution
Security and Compliance
Modal has completed a SOC 2 Type II audit and supports HIPAA-compliant workloads on Enterprise plans via a Business Associate Agreement. The platform uses gVisor-based sandboxing for compute isolation, TLS 1.3 for public APIs, and encryption for data in transit and at rest. These security practices help teams in regulated industries address security and compliance due diligence for sensitive workloads.
Unified Platform Advantage
Unlike sandbox-only platforms, Modal provides a complete AI infrastructure stack:
- Inference: Deploy low-latency inference endpoints on the same Modal platform, reducing cross-vendor integration overhead
- Training: Fine-tune models using GPU-backed training on the same platform as your sandboxes
- Batch processing: Queue up to 1M inputs for large-scale recursive processing
- Notebooks: Develop sandbox logic using hosted GPU Jupyter environments that can call Modal Functions and share resources such as Volumes and Secrets
Production-Proven Results
Modal serves startups, scale-ups, and enterprises across AI agent, coding-agent, RL rollout, sandboxed code, and AI-generated-code workloads, as documented on the Modal customers page. Ramp, for example, uses Modal Sandboxes for background coding agents that generate code changes and write them back into commits or pull requests (see Ramp's engineering post). The platform's serverless architecture means teams pay only for active compute time, with automatic scaling from zero to thousands of containers based on demand.
Best For: Teams building RLMs that need secure code execution at scale, with on-demand GPU access for ML inference, model fine-tuning, or compute-intensive recursive analysis, especially those seeking a unified platform that handles the entire AI infrastructure stack.
2. E2B
E2B specializes in secure sandboxes for AI agents, focusing on ephemeral code execution with Firecracker microVM isolation. E2B reports that its platform is used by 94% of Fortune 100 companies. Customers include Groq, Lindy, and Manus.
Core Capabilities
- Firecracker microVM isolation: Hardware-level isolation for running untrusted AI-generated code, providing strong security boundaries
- Cold start support: Sandbox initialization enables responsive RLM iteration cycles
- AI-agent-first SDK design: Purpose-built APIs for LLM code execution patterns, simplifying integration for agent workflows
- Multi-language SDKs: Support for Python and TypeScript/JavaScript integration patterns
Use Case Focus
E2B excels at ephemeral code execution, spinning up isolated environments for RLMs to run generated code, then tearing them down. The platform supports up to 100 concurrent sandboxes on Pro tier with 24-hour maximum session lengths.
Architecture Approach
E2B's Firecracker-based isolation provides hardware-virtualized security boundaries that may be preferable to plain shared-kernel containers for scenarios requiring strong isolation of untrusted code; this is distinct from a categorical comparison against all container-adjacent isolation systems such as gVisor. The platform's template system enables reproducible sandbox environments with versioning for consistent RLM execution contexts.
Best For: Teams building RLMs focused on code execution and testing where GPU acceleration is not required, particularly those needing strong microVM isolation and cold start support.
3. Daytona
Daytona provides development environments with sandbox creation and open-source flexibility. The platform's GitHub repository has accumulated significant community traction and supports GPU sandboxes for ML workloads as well as configurable persistence for non-GPU workloads.
Core Capabilities
- Sandbox cold starts: Supports cold starts that enable RLM iteration
- GPU support: Daytona supports NVIDIA GPU sandboxes for ML workloads; GPU sandboxes must be ephemeral
- Open-source and enterprise options: Self-hosting available with enterprise features for organizations requiring infrastructure control
- Git and LSP integration: Built-in developer tooling for coding workflows that benefit RLMs working with repositories
- Unlimited persistence: Daytona advertises unlimited sandbox persistence
Architecture Approach
Daytona focuses on persistent workspaces that maintain state across sessions. This approach benefits RLMs that need to preserve context, cached dependencies, or intermediate results without recreation overhead. The platform uses container-based isolation with Docker/OCI compatibility.
Self-Hosting Option
For organizations with data sovereignty requirements, Daytona's open-source codebase enables self-hosted deployments. This flexibility addresses compliance scenarios where managed cloud services may not be suitable.
Best For: Teams building RLMs that require persistent development environments and prefer workspace continuity over ephemeral execution, particularly those valuing open-source flexibility and self-hosting options.
4. Northflank
Northflank offers production-grade infrastructure with full bring-your-own-cloud (BYOC) capabilities and multiple isolation technologies. The platform serves enterprise and regulated industries requiring infrastructure control alongside modern sandbox capabilities.
Core Capabilities
- MicroVM and gVisor isolation: Northflank supports microVM-backed sandboxes and gVisor-based isolation modes, applying the appropriate technology based on workload
- Full BYOC deployment: Run sandboxes in your own AWS, GCP, Azure, or on-premises environment
- GPU support (L4 through H200): Broad hardware options for compute-intensive RLM workloads
- Unlimited session duration: No time restrictions on sandbox lifespan for long-running recursive processes
- SOC 2 compliance: Enterprise security attestation for regulated deployments
Architecture Approach
Northflank positions itself as production-grade infrastructure rather than a developer-focused tool. The platform's BYOC model provides complete control over data location and security posture, addressing compliance requirements that managed-only platforms cannot satisfy.
Enterprise Focus
For organizations requiring infrastructure sovereignty, Northflank's self-hosted deployment model enables sandbox execution within existing cloud accounts or on-premises data centers. This approach benefits RLM deployments in regulated industries with strict data residency requirements.
Best For: Enterprise teams building RLMs that require BYOC deployment, multiple isolation modes, and infrastructure control, particularly those in regulated industries with data residency or compliance requirements.
5. Blaxel
Blaxel is a sandbox platform built specifically for AI agents, emphasizing persistent "agent computers" that stay on standby and resume when needed. The platform focuses on secure sandboxed compute runtimes for agents that need to run commands, manage files, and preserve execution state.
Core Capabilities
- Resume from standby: Supports state restoration from standby mode, enabling RLM continuation
- Up to 100,000+ concurrent sandboxes: Tiered capacity for distributed recursive processing workloads, per Blaxel's advertised pricing tiers
- Standby model: Sandboxes scale to zero when idle with no active compute charge
- Persistent storage volumes: Storage that survives sandbox destruction and recreation for maintaining RLM context
- Template system: Reusable sandbox configurations for standardized RLM execution environments
Architecture Approach
Blaxel emphasizes persistent state rather than purely ephemeral execution. The platform recommends treating sandboxes as persistent computers that retain shell history, installed dependencies, and context over time, beneficial for RLMs that need continuity across recursive workflows.
Y Combinator Backing
Blaxel emerged from Y Combinator with a focus on the AI agent infrastructure market. The platform's architecture specifically addresses the state persistence challenges that recursive language models face when executing multi-step code generation workflows.
Best For: Teams building RLMs that need persistent sandbox environments, resume support, and secure code execution with continuity across sessions, particularly for agents requiring state preservation between recursive iterations.
6. Runloop
Runloop provides enterprise-grade devboxes for AI coding agents, with SOC 2 compliance and blueprint-based environment standardization. The platform raised $7M in seed funding to bring enterprise infrastructure to AI coding agents.
Core Capabilities
- Parallel Devbox orchestration: Runloop supports parallel Devbox execution; public official documentation references hundreds of scenarios run in parallel
- Blueprint standardization: Preconfigured environment templates ensuring consistent execution contexts
- Snapshot and resume: Save and restore Devbox state for RLM continuity
- SOC 2 compliance: Enterprise security attestation for production deployments
- VM-isolated Devboxes: Secure VM-based isolation for running AI-generated code
Architecture Approach
Runloop focuses on the specific needs of AI coding agents rather than general-purpose sandbox execution. The platform's blueprint system enables teams to define standardized environments that RLMs can reliably execute within, reducing environment-related failures in recursive workflows.
Enterprise Positioning
Runloop targets enterprise teams building production AI coding systems. The platform's compliance attestations and standardization features address the governance requirements that large organizations face when deploying autonomous code-generating systems.
Best For: Enterprise teams building AI coding agents that require standardized environments, snapshot capabilities, and SOC 2 compliance, particularly those prioritizing blueprint-based consistency for RLM execution.
7. Cloudflare Sandbox
Cloudflare Sandbox provides code execution environments through a TypeScript-first SDK, supporting Python and Node.js workloads with Cloudflare's edge infrastructure. The platform integrates with Cloudflare's broader developer ecosystem for teams already invested in their stack.
Core Capabilities
- Python and Node.js execution: Support for common RLM implementation languages
- TypeScript-first SDK: Primary API for sandbox lifecycle management, command execution, and file operations
- Isolated Linux containers: Each sandbox has dedicated filesystem and process space
- Configurable keep-alive: KeepAlive behavior prevents containers from automatically sleeping for long-running workloads; persistent state exists while the container is active, while durable persistence requires separate storage patterns
- Edge network integration: Leverage Cloudflare's global infrastructure for distributed execution
Architecture Approach
Cloudflare Sandbox centers around a TypeScript API for programmatic sandbox control. The platform supports AI code execution workflows, with Cloudflare providing tutorials for building AI code executors and coding agents using the OpenAI Agents SDK.
Ecosystem Integration
For teams already using Cloudflare Workers, Pages, or other Cloudflare services, the Sandbox product provides native integration. This ecosystem fit benefits organizations seeking to consolidate their infrastructure within Cloudflare's platform.
Best For: Teams building RLMs within the Cloudflare ecosystem, particularly those preferring a TypeScript-first development model and needing isolated code execution with edge network integration.
Why Modal Stands Out for RLM Code Execution
Purpose-Built AI Infrastructure
Modal's architecture is specifically engineered for AI and machine learning workloads. The platform's AI-native container runtime and optimized filesystem are designed for fast cold starts and dynamic AI workloads, meeting the demands of RLM execution: fast cold starts, secure sandboxed code execution, GPU-accelerated computation, and dynamic scaling that recursive workflows require.
GPU-Enabled Sandboxes Enable Advanced RLM Capabilities
While many code-execution sandbox providers remain CPU-oriented, some now offer GPU support with varying limitations around availability, persistence, and deployment model. Modal provides broad GPU access within sandboxes, including T4, L4, A10, L40S, A100 variants, RTX-PRO-6000, H100/H100!, H200, and B200/B200+. This capability expands what RLMs can accomplish: running local model inference, executing GPU-accelerated code analysis, and performing compute-intensive operations without external API calls. For recursive language models that need to evaluate their own outputs using ML models, GPU-enabled sandboxes are valuable.
Unified Platform Eliminates Vendor Sprawl
RLM systems typically require multiple infrastructure components: sandboxes for code execution, inference endpoints for model serving, training infrastructure for fine-tuning, and batch processing for large-scale operations. Modal provides all of these in a single serverless platform, eliminating the integration complexity and operational overhead of managing multiple vendors.
Security Without Compromise
Modal's security practices address enterprise requirements without sacrificing developer experience. gVisor-based sandboxing provides compute isolation, a completed SOC 2 Type II audit demonstrates operational security practices, and HIPAA support via BAA on Enterprise plans addresses healthcare and regulated industry requirements. These compliance foundations help RLM systems address the security due diligence required in regulated environments.
Developer Experience Accelerates Iteration
Modal's code-first SDKs in Python, TypeScript, and Go eliminate infrastructure configuration overhead. Teams define compute requirements, container images, and scaling behavior directly in code, with no YAML or config files required. For RLM development, where rapid iteration on recursive logic is essential, this approach enables faster feedback loops and more experiments per day. This can reduce infrastructure setup and operational overhead compared with managing traditional clusters, while providing the security isolation that RLM workloads demand.
Production-Proven at Scale
Modal's customer page documents production use cases across language models, fine-tuning, batch processing, sandboxed code, and coding agents. Ramp uses Modal Sandboxes for background coding agents that generate code changes and write them back into commits or pull requests (see Ramp's engineering post). The combination of massive concurrency support (100k+ concurrent sandboxes), serverless economics, and proven enterprise scale makes Modal a compelling choice for teams serious about deploying recursive language models.
