# Render Sandboxes > Render Sandboxes provides each AI agent with its own isolated Linux computer that starts in under a second. It is for developers running agents like Claude Code or Codex who need to test changes with Docker Compose and control network access. Idle... - Type: AI tool - Page: https://completeaitraining.com/ai-tools/render-sandboxes/ - Published: 2022-11-14 - Updated: 2026-11-05 - Author: Jeroen Erne - Jobs: IT and development - Topics: Developer Tools, Coding, Productivity, API - Reading time: 2 min ### About Render Sandboxes Render Sandboxes is a feature within the Render cloud platform that gives each AI agent its own isolated Linux environment. Each sandbox runs inside a Firecracker microVM, starts in under a second, and can execute agent harnesses like Claude Code or Codex. When an agent goes idle, the sandbox suspends - stopping compute charges while preserving files and memory - and wakes on the next command. ### Review Render Sandboxes tackles a specific infrastructure problem: running AI coding agents safely and at scale without them stepping on each other. The isolation model means one agent hitting a memory limit won't crash its neighbors, and the suspend/wake cycle keeps costs tied to actual usage rather than uptime. It's a beta product, so some edges are still being smoothed, but the core mechanics are functional and well-documented. #### Key Features - **Firecracker microVM isolation:** Each sandbox gets the isolation properties of a real VM, not a shared container. If one sandbox exhausts its memory, other sandboxes continue running unaffected. - **Sub-second startup:** A sandbox is ready for its first command in under a second, which keeps agent workflows from stalling on cold starts. - **Suspend and wake with state intact:** When an agent goes idle, the sandbox suspends. Files and memory persist, and the sandbox resumes on the next command. Compute charges stop during suspension; only snapshot storage continues to bill. - **Outbound traffic controls:** You can configure a sandbox to allow all outbound traffic, restrict it to a list of domains you specify, or block outbound traffic entirely. - **Credential brokering (early access):** Rather than storing API keys inside the sandbox, this mechanism attaches them to outgoing requests. The feature is in early access and requires contacting Render for access. #### Pricing and Value Render Sandboxes bill based on the CPU a sandbox actually uses and the memory it allocates. When a sandbox suspends, both CPU and memory charges stop; only snapshot storage continues to incur cost. There is no time limit on how long a sandbox can run. Sandboxes live in the same workspace as other Render resources - web services, background workers, and Render Postgres databases - and appear on a single bill with one API key and one SDK. #### Pros - Isolation at the VM level prevents noisy-neighbor problems when running many agents concurrently. - Suspend/resume with state preservation means long-running agent tasks don't lose context between bursts of activity. - Multiple sandboxes can spin up from the same snapshot, reducing setup time for repeated environments. - Outbound network controls let operators lock down what external resources an agent can reach. - Unified billing and a single SDK mean sandboxes don't add a separate platform to manage. #### Cons - Suspend performance scales with disk and memory size; a 20 GB disk can take tens of seconds to suspend, which may affect workflows with large dependencies like an 18 GB node\_modules directory. - Credential brokering, a feature that keeps API keys out of sandbox environments, is only available in early access and requires contacting Render directly. - The product is not well suited for teams that need sandboxes to wake themselves on a schedule. A sandbox requires an external command to wake; it cannot self-trigger on a cron-like timer. Render Sandboxes fits teams already deploying AI agents who need per-task isolation and don't want to manage the underlying compute lifecycle themselves. It's most practical for workloads where agents run in bursts - coding sessions, test runs, code review tasks - rather than continuous always-on processes. The beta status means some performance characteristics are still evolving, but the isolation model and suspend behavior address real failure modes that arise when multiple agents share a single machine. --- This is the Markdown form of https://completeaitraining.com/ai-tools/render-sandboxes/. The whole site is mapped at https://completeaitraining.com/llms.txt.