Top OpenComputer alternatives for AI agent sandboxes in 2026

Not all sandbox platforms are built for the same use case. When evaluating alternatives, the following dimensions determine whether a platform fits production agent infrastructure.

• Isolation model: Full KVM VMs, microVMs (Firecracker, Kata Containers), and gVisor offer different trade-offs between boot time and isolation strength. Shared-kernel containers are weaker for truly untrusted code.
• Session persistence: Some platforms impose hard session time limits. Agents that maintain state across user sessions or multi-day workflows need a platform without artificial cutoffs.
• BYOC support: For regulated industries or teams with data residency requirements, workloads must stay inside the company's own cloud account. Most platforms in this space are managed-only.
• GPU availability: Agents that run inference, fine-tuning, or compute-intensive tasks need GPU access on the same platform as sandbox execution.
• Platform completeness: Sandboxes alone are rarely enough. Production agent platforms typically also need databases, background workers, CI/CD, and observability in the same control plane.
• Pricing transparency: Billing models vary significantly across platforms. Some charge for provisioned resources; others charge for active usage only. Cost at scale can differ by 5x or more between providers.

The platforms below cover the main use cases for persistent VM and microVM sandbox infrastructure: production agent deployments, fast SDK integration, long-running coding environments, enterprise agent infrastructure with benchmarking, ML-heavy workloads, and snapshot-first workflows.

Northflank provides microVM-backed sandbox infrastructure alongside a full production stack: databases, APIs, workers, CI/CD pipelines, GPU workloads, and observability, all running either on Northflank's managed cloud or inside your own VPC.

Sandboxes on Northflank boot in under a second using Kata Containers, Firecracker, or gVisor depending on the workload's isolation requirements. Each isolation technology offers different trade-offs between boot time and isolation strength, giving teams flexibility to match the runtime to their threat model. For a technical comparison, see the guides on Kata Containers vs Firecracker vs gVisor and Firecracker vs gVisor.

A key architectural differentiator is self-serve BYOC. Northflank supports deployment into AWS, GCP, Azure, Oracle, CoreWeave, Civo, bare-metal, and on-premises without requiring a sales call. This is particularly relevant for regulated industries and any deployment where data residency is a hard requirement. For setup details, see deploying sandboxes in your cloud.

Northflank also supports on-demand GPU workloads running alongside sandboxes in the same platform. A range of GPUs including L4, A100 (40GB and 80GB), H100, H200, and others are available without quota requests. See GPU workloads on Northflank for full hardware details.

• Both ephemeral and persistent sandbox environments with no forced session time limits
• Multi-tenant microVM isolation via Kata Containers, Firecracker, and gVisor
• Self-serve BYOC across AWS, GCP, Azure, Oracle, CoreWeave, Civo, bare-metal, and on-premises
• On-demand GPUs (L4, A100, H100, H200) without quota requests
• Full workload runtime: APIs, workers, databases, CI/CD, and observability in one control plane
• API, CLI, and SSH access
• In production since 2021 across startups, public companies, and government deployments. SOC 2 Type 2 certified.

For API-driven sandbox creation, see creating sandboxes with the SDK. For a full product overview, see the Northflank sandboxes page.

Best for: Teams that need production-grade microVM isolation, unlimited session lengths, self-serve BYOC, GPU workloads, or a complete infrastructure stack beyond just sandboxes.

Pricing (PaaS): CPU at $0.01667/vCPU-hour, memory at $0.00833/GB-hour, billed per second. H100 at $2.74/hour. Full details on the Northflank pricing page.

E2B provides sandbox infrastructure for AI agents with Python and TypeScript SDKs and Firecracker microVM isolation. The SDK supports integration with LangChain, OpenAI, and Anthropic tooling.

• Firecracker microVM isolation with a dedicated kernel per sandbox
• Python and TypeScript SDKs with AI framework integrations
• Pause and resume: state is preserved with no compute cost while paused; storage included free
• Default 2 vCPUs / 1GB RAM, configurable up to 8 vCPUs / 8 GiB on Pro
• No GPU support
• BYOC available for enterprise customers only, not self-serve
• 24-hour session limit on Pro

Best for: Teams building AI coding agents or code interpreter experiences who need SDK integrations and sessions under 24 hours.

Fly.io Sprites provides stateful sandbox environments for AI coding agents. Each Sprite is a persistent Linux VM running on a Firecracker microVM with hardware-level isolation.

• Firecracker microVM isolation
• 100GB NVMe-backed filesystem that persists across sessions without explicit snapshotting
• Checkpoint and restore in approximately 300ms, capturing the full VM state
• Up to 8 CPUs and 16GB RAM per Sprite
• No compute charge when idle; billing stops when the Sprite is inactive
• No GPU support
• No BYOC; all environments run on Fly.io's managed infrastructure
• $30 in trial credits available

Sprites are designed for individual developer workflows and coding agent use cases. They do not provide multi-tenant orchestration APIs or broader platform features such as databases, CI/CD, or observability.

Best for: Individual developers building coding agents who want persistent environments with idle-based billing and checkpoint/restore. Teams already operating on Fly.io.

Runloop provides microVM-isolated Devboxes for AI coding agents. Devboxes run on a custom bare-metal hypervisor with two layers of isolation: a VM layer and a container layer. The platform supports running agents against SWE-Bench Verified, SWE-Smith, R2E-Gym, and other public benchmarks directly from the platform with no setup required.

• Two-layer isolation (VM + container) on a custom bare-metal hypervisor
• Suspend and resume: compute billing stops on suspension, storage continues
• Snapshot and branch from Devbox disk state
• Repo Connections for automatic build environment inference from Git repositories
• Both arm64 and x86 architecture support
• SSH, CLI, and IDE connections to running Devboxes
• No GPU support

Best for: Teams building AI coding agents that need persistent, isolated Devboxes with suspend/resume and snapshot branching for stateful agentic workflows.

Modal is a Python-first serverless compute platform. Modal Sandboxes run on gVisor, which intercepts Linux system calls in user space rather than providing a dedicated VM kernel per workload.

• gVisor isolation (user-space kernel interception, not hardware-level microVM)
• GPU support across H100, A100 80GB, A100 40GB, L4, and others
• Persistent storage via Volumes at $0.09/GiB/month (1 TiB/month free)
• Session timeout default of 5 minutes, configurable up to 24 hours; longer workflows use filesystem snapshots
• Environments defined through Modal's Python SDK, not arbitrary container images
• No BYOC; managed infrastructure only

Best for: Python-first ML teams running inference, training, or data pipelines who need sandboxing integrated with GPU compute in one platform.

CodeSandbox, now part of Together AI, provides microVM-based sandbox environments with snapshot and forking as first-class primitives. Named checkpoints can be forked into multiple independent sandboxes or restored in under two seconds.

• microVM isolation with snapshot and fork support
• No platform-imposed session time limit on any plan
• Dev Container images and standard environment formats supported
• No GPU compute available
• No BYOC outside of enterprise dedicated cluster arrangements
• Scales to 250 concurrent VMs on the Scale plan, custom on Enterprise

CodeSandbox is web-focused in its feature set and integrations, suited more toward development and educational use cases than production agent infrastructure at scale.

Best for: Teams that need snapshot and forking as a core workflow primitive, web-focused coding agents, and educational platforms.

Pricing as of May 2026. Verify current rates on each platform's pricing page before making cost decisions.

Northflank is the only platform here that covers production microVM isolation, self-serve BYOC, GPU support, unlimited sessions, and a full platform stack in one place. For teams building multi-tenant AI platforms or agent infrastructure that needs to scale under compliance requirements, it is the platform worth evaluating first. See the Northflank AI sandbox pricing guide for a detailed cost breakdown.

No. OpenComputer runs on managed infrastructure only and has no GPU compute capability. Teams that need BYOC deployment or GPU workloads should evaluate Northflank, which supports both on a self-serve basis.

On PaaS, Northflank has the lowest published CPU rate at $0.01667/vCPU-hour among the platforms in this comparison with transparent pricing. Cost at scale varies significantly depending on workload spec, concurrency, and whether BYOC is an option. See the AI sandbox pricing guide for a detailed cost breakdown across providers.

Northflank supports both ephemeral and persistent environments with no forced time limits. Fly.io Sprites maintains a persistent 100GB NVMe filesystem across sessions with idle-based billing. E2B supports pause and resume with state preserved at no compute cost. Runloop supports suspend and resume with no compute charge while suspended, alongside snapshot and branch from Devbox disk state. CodeSandbox supports persistence via snapshots with VM restore in under two seconds. Modal supports snapshot-based state preservation across sessions up to 24 hours.

OpenComputer uses full KVM-based virtual machines, giving each sandbox a dedicated kernel, memory, and disk. Northflank supports Kata Containers, Firecracker, and gVisor, giving teams the option to choose between microVM-level hardware isolation and user-space kernel interception depending on their workload. E2B and Fly.io Sprites use Firecracker microVMs, providing a dedicated kernel per sandbox. Runloop uses two layers of isolation: a VM layer and a container layer. Modal uses gVisor, which intercepts system calls in user space without a dedicated VM per workload. For a deeper comparison, see the Northflank guide on microVM vs gVisor.