OmniRun vs Modal: Sandboxes vs Serverless -- When to Use What

Different Tools for Different Jobs

Modal is a broad compute platform. It handles ML inference, model training, batch processing, GPU workloads, cron jobs, and web endpoints. It is designed to replace your infrastructure layer entirely -- you write Python functions, decorate them, and Modal handles the rest.

OmniRun is laser-focused on sandboxed code execution. It does one thing: give your AI agent (or your users) an isolated environment to run arbitrary code safely. No GPU scheduling, no training pipelines, no batch orchestration. Just sandboxes that boot in 250ms and are fully isolated at the hardware level.

Use Case Fit

Isolation Model

OmniRun uses Firecracker microVMs -- the same technology that powers AWS Lambda. Each sandbox gets its own Linux kernel, its own network namespace, and hardware-enforced isolation via KVM. A vulnerability in one sandbox cannot affect another because the CPU itself enforces the boundary.

Modal uses container-based isolation with gVisor for additional sandboxing. This is reasonable for first-party code where you control what runs. But for untrusted code -- the kind AI agents generate on the fly -- containers share a kernel, and kernel exploits can break the isolation boundary. Modal was not designed as a security sandbox; it was designed as a compute platform.

Developer Experience

The API philosophies are fundamentally different.

OmniRun has a minimal, imperative API: create a sandbox, run commands, kill it when done. You have full control over the sandbox lifecycle. The TypeScript SDK, Python SDK, and CLI all follow this pattern. There is no magic -- you manage the sandbox explicitly.

Modal uses a decorator-based approach. You annotate Python functions with @app.function() and Modal handles provisioning, scaling, and teardown. This is powerful for serverless workloads but means you work within Modal's execution model. You define functions, not sandboxes. Modal also has a Sandbox API for running arbitrary code, but it is a secondary feature rather than the platform's core focus.

Language Support and SDKs

Modal is Python-first. Its SDK, decorator model, and documentation all center on Python. You can run other languages inside Modal containers, but the orchestration layer is Python.

OmniRun is language-agnostic at the SDK level. The TypeScript and Python SDKs are first-class citizens, and the CLI works from any environment. Inside the sandbox, you can run anything -- Python, Node, Go, Rust, shell scripts -- because it is a full Linux VM, not a function execution environment.

Pricing Model

Both platforms bill per second. Modal charges for CPU, memory, and GPU separately, with pricing that scales based on the compute profile. OmniRun charges per vCPU-second starting at $0.000125/sec with volume tiers.

The real cost difference is not in the per-unit price but in what you are paying for. Modal bills for the full compute stack -- networking, GPU scheduling, and orchestration. OmniRun bills for sandbox time only. If you just need isolated execution, OmniRun is the simpler cost model.

The Bottom Line

OmniRun and Modal are not competitors -- they solve different problems.

If you need to deploy ML models, run GPU workloads, or build serverless data pipelines, Modal is the right choice. It is a complete compute platform with an excellent developer experience for Python-centric teams.

If you need to run untrusted code safely, sandbox AI agents, or provide isolated environments for users, OmniRun is purpose-built for that. You get Firecracker-level isolation, E2E encryption, and a dead-simple API without paying for compute features you do not need.