E2B vs Modal
Pick E2B if agent code execution is your core workload and you want microVM isolation, ~150ms starts, and a self-hosting path. Pick Modal if you need GPUs attached to sandboxes or you're already running ML workloads there — it's the only credible option in this pair for GPU-accelerated agent code, at the cost of container-grade isolation and a cloud-only deployment model.
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Side by side
E2B
ModalE2B and Modal both sell isolated execution for untrusted, agent-generated code, but they come at the problem from opposite directions. E2B is a purpose-built agent sandbox company; Modal is a general serverless ML/compute platform where Sandboxes is one primitive among many.
Isolation. E2B runs every sandbox in a Firecracker microVM with a dedicated kernel — a hardware-virtualization boundary. Modal uses gVisor, Google's container sandbox runtime, with default-deny network and workspace access. Both are legitimate approaches to untrusted code, but they're different classes of boundary: a microVM gives each workload its own kernel, while gVisor interposes on syscalls above a shared host kernel. If you're executing genuinely hostile code, that distinction matters.
GPUs. This is the hardest line between them. Modal attaches its full GPU lineup — T4, L4, A100, H100, up to B200, including multi-GPU — directly to Sandboxes via the same gpu= parameter as Functions. E2B has no GPU support at all: its own engineering blog notes Firecracker lacks PCIe passthrough, and GPU enablement work stopped for 2025. If your agents run inference, fine-tuning, or CUDA code, Modal wins by default.
Cold starts. E2B cites ~150ms via VM snapshot restore. Modal claims ~1s raw container boot, but its own docs note real end-to-end cold starts (including code and import init) typically run several seconds unless you use Memory Snapshots. Both figures are vendor claims, but E2B's architecture is tuned for the rapid-fire create/destroy pattern agent workloads generate.
Runtime model. Modal sandboxes default to a 5-minute timeout (max 24 hours); beyond that you're expected to snapshot the filesystem and resume in a new sandbox. E2B allows 1-hour continuous sessions on Hobby and 24 hours on Pro, with pause/resume preserving full filesystem and memory state indefinitely — a more natural fit for long-lived agent sessions, since Modal's filesystem snapshots capture disk but the memory-state equivalent is a separate cold-start optimization.
Pricing. The metered rates are close: E2B at ~$0.000014/vCPU-s plus ~$0.0000045/GiB-s versus Modal at $0.0000131/core-s plus $0.00000222/GiB-s. The plan structure differs more: Modal's Starter is $0 base with $30/mo of free credit, while E2B's Hobby is pay-as-you-go with a one-time $100 credit. Higher tiers cost $250/mo (Modal Team, includes $100/mo credit) versus $150/mo (E2B Pro, which unlocks 24-hour sessions and 100 concurrent sandboxes). GPU time on Modal is billed separately, from $0.000164/s for a T4.
Deployment and lock-in. E2B's core is Apache-2.0 with a documented Terraform self-hosting path across AWS, GCP, and Azure. Modal is a proprietary managed cloud with no self-hosted option — its client SDKs are open source, but the execution backend is not. Modal also exposes no public general-purpose REST API in its docs (Python, JS, and Go SDKs only), whereas E2B offers Python and JS SDKs plus REST.
Bottom line. These tools overlap less than the category framing suggests. Modal is what you choose when sandboxing is one part of a broader ML platform need, especially with GPUs. E2B is what you choose when sandboxing untrusted agent code is the product requirement, and kernel-level isolation, memory-preserving pause/resume, or an exit path via self-hosting outweigh GPU access.