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, fan out into many parallel recursive calls without performance degradation, and get the CPU and memory each REPL step needs without overprovisioning. This guide examines seven sandbox platforms serving different RLM needs in 2026, starting with Modal, a scalable, high-performance sandbox platform for executing AI-generated code at production scale.
Key Takeaways
1. Modal
Modal is a scalable, high-performance sandbox platform for executing AI-generated code, built for teams running agents and recursive model pipelines at production scale. Its sandboxes combine high concurrency, automatic CPU and memory bursting, and consistent fast starts under load, 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
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 VM and gVisor sandboxing for compute isolation, TLS 1.3 for public APIs, and encryption for data in transit and at rest. Modal supports region-pinning. 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:
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). Imbue orchestrates hundreds of isolated coding agents on Modal. Modal reports running 3 billion sandboxes as of October 2026 and regularly supports tens of thousands of concurrent customer sandboxes.
Why Teams Choose Modal for Recursive Language Models
2. E2B
E2B provides secure sandboxes for AI agents, focusing on ephemeral code execution with Firecracker microVM isolation.
Capabilities
Considerations
Why Teams Choose E2B
E2B is a purpose-built sandbox provider whose core is open source, with bring-your-own-cloud options, and it reports 1 billion sandboxes run in total. RLM teams choose E2B as a standalone point solution for the code-execution step of their recursive pipelines.
3. Daytona
Daytona offers isolated sandboxes and development environments built with a focus on persistence, and its GPU sandboxes for ML workloads are ephemeral.
Capabilities
Considerations
Why Teams Choose Daytona
Daytona is a dedicated sandbox provider that builds in persistence and offers fast starts at smaller scale. Teams choose Daytona as a point solution when an RLM's REPL state needs to carry across sessions and the workload doesn't call for high concurrency.
4. Northflank
Northflank is a cloud application platform that includes sandboxes. Each sandbox runs in its own microVM on Cloud Hypervisor, and bring-your-own-cloud (BYOC) deployment is available.
Capabilities
Considerations
Why Teams Choose Northflank
Northflank is a general-purpose cloud platform that offers sandboxes as one service among several. Teams choose Northflank when they want the REPL environments behind their RLMs deployed in their own cloud account, next to the rest of their application infrastructure.
5. Blaxel
Blaxel is a sandbox platform built for AI agents, centered on persistent "agent computers" that stay on standby and resume when needed. The platform focuses on sandboxed compute runtimes for agents that need to run commands, manage files, and preserve execution state.
Capabilities
Considerations
Why Teams Choose Blaxel
Blaxel is a sandbox point solution whose design centers on persistent standby. Teams choose Blaxel when they want the sandboxes behind an RLM to idle on standby between recursive runs and resume with their earlier state.
6. Runloop
Runloop provides devboxes for AI coding agents, using VM-based isolation and blueprint-based environment standardization.
Capabilities
Considerations
Why Teams Choose Runloop
Runloop ships benchmarking infrastructure alongside its sandboxes. Teams choose Runloop when they want that benchmarking layer ready for evaluating RLM-driven coding agents and are comfortable with environments configured like DevBoxes rather than ephemeral sandboxes.
7. Cloudflare Sandbox
Cloudflare Sandbox provides code execution environments through a TypeScript-first SDK, supporting Python and Node.js workloads on Cloudflare's infrastructure.
Capabilities
Considerations
Why Teams Choose Cloudflare
Cloudflare Sandbox is part of Cloudflare's wider infrastructure. Teams choose it when their RLM application already runs on Cloudflare and they want the code-execution layer pre-integrated into that architecture.
Why Modal Stands Out for RLM Code Execution
Concurrency for Recursive Fan-Out
Recursive processing multiplies execution environments quickly: a root model can hand slices of a long context to sub-calls, and each sub-call may run its own code. Modal has demonstrated creating 1M+ sandboxes in under a minute, exceeding 10,000 sandbox creations per second. It supports up to 1M concurrent sandboxes and regularly runs tens of thousands of concurrent customer sandboxes, with a custom-built scheduler that keeps execution reliable as recursion depth and width grow.
CPU and Memory Bursting
RLM workloads are hard to size in advance. A REPL can sit idle for long stretches while the model reasons about its next step, then load a large document into memory, run regex searches across it, or execute analysis code on the results. Fixed-reservation pricing forces teams to size every sandbox for the heaviest of those moments, so utilization stays low and costs run high relative to the work performed. On Modal, each sandbox runs from a CPU and memory baseline the team sets and bursts past it automatically when a recursive step demands more, which lets RLM teams provision for typical usage instead of peak usage. Charges accrue per second on the greater of requested and actual resources, and bursting also reduces the risk of out-of-memory failures when a step outgrows its baseline.
Purpose-Built AI Infrastructure
Modal's architecture is specifically engineered for AI and machine learning workloads. The platform's AI-native container runtime, scheduler, and optimized filesystem keep sandbox starts sub-second and consistent under heavy concurrent load, including with large custom images. That consistency matters for RLM execution, where every recursive step waits on the sandbox before the next one can begin.
GPU-Enabled Sandboxes When RLMs Need Them
Most RLM execution is CPU-based, but some recursive workflows benefit from acceleration. Modal provides GPU access within sandboxes across 10+ types, including T4, L4, A10, L40S, A100 variants, RTX PRO 6000, H100, H200, B200, and B300, without reservations. 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 platform, eliminating the integration complexity and operational overhead of managing multiple vendors.
Security Without Compromise
Modal's security practices address enterprise requirements. VM and gVisor 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. Modal also supports region-pinning. These compliance foundations help RLM systems address the security due diligence required in regulated environments.
Developer Experience Accelerates Iteration
Modal provides code-first SDKs in Python, TypeScript, and Go. Teams define compute requirements, container images, and scaling behavior directly in code. 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). Modal reports running 3 billion sandboxes as of October 2026. Its support for up to 1M concurrent sandboxes, together with active-resource billing and consistent startup under load, makes Modal a compelling choice for teams serious about deploying recursive language models.
Explore the Modal documentation to get started with secure sandboxes for your RLM workloads.
