Best Sandboxes for Tool-Use RL Environments in 2026

1. Modal

Modal delivers an AI infrastructure platform where sandboxes, training, inference, batch processing, and notebooks coexist in a single code-first environment. For RL workloads specifically, Modal places sandbox execution and GPU training infrastructure on the same platform, which reduces cross-vendor integration overhead between environment rollouts and policy updates.

Key Capabilities

Why It Leads for RL

Modal's architecture addresses the core challenge of tool-use RL: running many concurrent trajectories while keeping GPU utilization high. The platform has customer evidence directly in this shape. Applied Compute runs thousands of Modal Sandboxes in parallel during RL training and connects Sandbox startup latency directly to idle inference-GPU time, and Applied Compute said Modal was the only option among the providers it evaluated that supplied appropriate primitives across its RL loop while keeping the boundaries between them low-cost. On the production-agent side, Lovable reached up to 20,000 concurrent Sandboxes at peak during a 48-hour promotional weekend in which Modal ran more than one million Sandboxes, and Modal Sandboxes now serve every Lovable app-generation session. Ramp uses Modal to power Inspect, its internal background coding agent, which has scaled to hundreds of concurrent sessions with a full-stack development environment inside each Sandbox. The code-first SDK means RL researchers define environments, reward functions, and training loops in normal application code without YAML configuration or Kubernetes manifests, with composable primitives that specify logic and hardware in one place and a developer experience Modal describes as feeling local. Modal routes workloads across clouds and regions automatically, with explicit region selection available when placement matters, and region pinning holds the chosen region rather than silently scheduling elsewhere. Best For: Teams that want CPU and GPU sandbox execution, large-scale parallel rollouts, integrated training and inference, and enterprise compliance artifacts in one code-first stack.

2. Daytona

Daytona provides a sandbox platform that supports cold starts, with multi-language SDK support spanning Python, TypeScript, Ruby, Go, and Java. The platform publishes RL-specific documentation and customer material, including guides for launching thousands of environment instances.

Key Capabilities

RL-Specific Strengths

Daytona's provisioning model is aimed at the RL provisioning pattern, and its OpenEnv FinQA reinforcement learning guide documents launching large numbers of environment instances in parallel. Computer Use is available for Linux and Windows, with macOS support currently in private alpha. Named customer material includes LangChain, SambaNova, Turing, and Trajectory. Best For: Teams prioritizing polyglot SDK coverage and high-volume parallel rollouts, with an actively developed core that is now maintained privately.

3. CoreWeave Sandboxes

CoreWeave launched its Sandboxes product on May 14, 2026, explicitly positioned for reinforcement learning, agent tool use, and model evaluation. The differentiator is placement: sandboxes can run in the same CoreWeave environment as GPU training workloads, reducing cross-cloud transfers and network round trips between RL rollouts and policy updates. Availability note: CoreWeave Sandboxes and W&B Serverless Sandboxes are in public preview as of July 2026.

Key Capabilities

RL-Specific Strengths

IBM Research reports using the system at meaningful scale: Brian Belgodere, Senior Technical Staff Member at IBM Research, describes RL workflows that spin up thousands of sandboxes in parallel per training step. Mistral separately reports running hundreds of concurrent sandboxes, on CPU nodes alongside Slurm training jobs on GPU nodes. Running sandboxes in the same CoreWeave environment as training workloads can reduce cross-cloud transfers and network round trips. The documented architecture includes a control plane, a data plane, a gateway, managed runners, and Kubernetes pods. Best For: Enterprises already on CoreWeave infrastructure that are comfortable with public-preview support expectations.

4. E2B

E2B offers Firecracker microVM sandboxes focused on code execution for AI agents. Its public material describes broad adoption among large enterprises and more than one billion started sandboxes.

Key Capabilities

RL-Specific Strengths

E2B documents reward-function execution for Hugging Face's Open R1 work, and separately reports customer deployments reaching 10,000 to 15,000 concurrent sandboxes. Current public resource and pricing material concerns CPU and RAM, which suits code execution and tool-use evaluation where accelerated work runs on separate training and inference services. Best For: Teams focused on CPU-bound code-execution sandboxes for reward evaluation, where GPU work runs on separate training and inference services.

5. Google Kubernetes Engine (GKE) Agent Sandbox

GKE Agent Sandbox provides Kubernetes-native sandbox orchestration for isolated agent code execution and computer use, with native gVisor support and pluggable Kata Containers support.

Key Capabilities

RL-Specific Strengths

For organizations already running GKE clusters, Agent Sandbox provides sandbox primitives without introducing new vendors, and the warm-pool feature helps maintain low latency for workloads requiring rapid environment provisioning. GKE can also host ADK-based agent applications while Agent Sandbox provides isolated execution environments. These components are composable within GKE. Google separately documents using GKE Pod Snapshots to save and restore Agent Sandbox environments and improve startup behavior. Best For: Enterprises with existing GKE infrastructure seeking native sandbox integration and Google Cloud enterprise support.

6. Azure Container Apps Sandboxes (Preview)

Azure Container Apps Sandboxes is a distinct Microsoft.App/SandboxGroups resource type with a user-managed lifecycle, stateful snapshots, and volumes. It is separate from Dynamic Sessions, another Container Apps compute type that provides ephemeral, prewarmed, Hyper-V isolated sessions managed through session pools for code interpretation and LLM-generated scripts. Availability note: Azure Container Apps Sandboxes is in preview and early access. Microsoft notes that sandboxes created during preview might not be compatible with future releases, that resources might need to be recreated, and that API and CLI surfaces may change.

Key Capabilities

RL-Specific Strengths

Microsoft Foundry Hosted Agents run containerized agentic applications in per-session VM-isolated sandboxes with dedicated persistent filesystems and session state restoration, which demonstrates agent-hosting integration. Microsoft documents its reinforcement fine-tuning service separately. The suspend and resume capability with state preservation enables cost optimization for long-running experiments with intermittent activity. Best For: Microsoft Azure customers requiring native sandbox integration with existing Azure security and compliance controls, at a preview stage of availability.

7. Deno Sandbox

Deno Sandbox launched in 2026, running independent Linux microVMs with hypervisor-level isolation on Deno Deploy, with a distinctive secrets architecture that keeps raw credential values outside the sandbox.

Key Capabilities

RL-Specific Strengths

Deno explicitly targets AI agents and copilots that need to run code as they reason. The secrets architecture keeps raw secret values out of the sandbox and substitutes them only for approved destinations, reducing direct credential-exfiltration risk during exploration. Overall credential security also depends on the allowlist, the behavior of approved destinations, application protocols, and the customer's threat model. Current documented sandbox lifetimes run from seconds to minutes, with a 30-minute limit described in the product limits, and the current resource model documents CPU and memory. That profile suits lightweight tool-use evaluation more than full RL training loops. Best For: TypeScript and JavaScript focused teams building lightweight tool-use agents with strict secret-isolation requirements.

Why Modal Stands Out for Tool-Use RL

Integrated Infrastructure Reduces Assembly Work

Modal places sandboxes, GPU training, inference serving, storage, and orchestration primitives on one platform. For RL workflows this reduces cross-vendor integration overhead and the number of moving parts between policy updates, environment rollouts, and reward computation. Teams can tighten latency between separately running components further through region selection and architecture choices. Ramp, for example, achieved no network hop between the agent and its test suite by placing both inside a single Sandbox, while using Functions, Dicts, and Queues for coordination outside it.

Elastic Compute Without Reservation Complexity

RL training requires compute that scales with experiment size. Modal provides elastic access to CPU and GPU resources, including H100, H200, A100, B200, B300, and other accelerators, through a multi-cloud capacity pool without customer-managed quotas or reservations. Sandboxes are billed by CPU and memory consumption by the second, and teams can tune requested CPU and memory, with bursting available above the requested amount, so Sandboxes can be sized to the workload.

Production-Proven and Demonstrated at Scale

Modal states it supports up to 50,000 concurrent Sandboxes per customer and has demonstrated one million concurrent Sandboxes created in under one minute in an engineering demonstration. On the customer side, Applied Compute runs thousands of Sandboxes in parallel during RL training, and Lovable reached a production peak of 20,000 concurrent Sandboxes during a promotional weekend. Together these are directly relevant evidence for the parallelization demands of serious RL research.

Enterprise Compliance for Regulated Industries

Modal has successfully completed a SOC 2 Type 2 audit with no deviations found, and supports HIPAA-compliant workloads on Enterprise plans via a Business Associate Agreement.

Code-First Developer Experience

RL researchers work in code, not configuration. Modal's SDKs support code-defined infrastructure in Python, TypeScript, and Go, letting teams specify everything from logic to hardware in one place, without YAML configuration, Kubernetes manifests, or Terraform scripts. Code inside the Sandbox is not limited to one language either, so environments can run whatever runtime the workload requires. Modal also provides integrated logging and visibility into every function, sandbox, and container, including log export, so the same workflow supports debugging and production operation. For teams building tool-use RL systems that need CPU and GPU compute, large-scale parallelization, and enterprise compliance artifacts, Modal provides an unusually complete infrastructure stack. Explore the sandboxes documentation to understand implementation details.