Open-source AI infrastructure
Alternatives to E2B for running AI-generated code, compared on isolation primitive, whether self-hosting is real, how concurrency is priced, and which projects are…
E2B caps concurrent sandboxes at 20 on Hobby and 100 on Pro, purchasable to 1,100, with sessions capped at 1 hour and 24 hours respectively. Agent workloads are bursty, so the concurrency ceiling is usually the first constraint teams hit, and hitting it is a procurement conversation rather than a scaling one.
Daytona carries 71,871 GitHub stars and a README stating that as of June 2026 core development moved to a private codebase and the repository will receive no further updates, fixes or releases. Check the LICENSE file too: it was Apache-2.0 at the first commit in February 2024, AGPL-3.0 from April 2025, and deleted outright on 23 June 2026, so GitHub now reports the repository licence as none and the last open release, v0.190.0, is the AGPL-3.0 tag the README points forks at. For a sandbox, whose entire job is to be a security boundary, running an unmaintained snapshot is a serious position. Their hosted product is unaffected and is genuinely fast, documenting sub-90ms starts.
For untrusted, model-generated code, containers alone are a weaker boundary than most teams assume, since container escapes are a recurring vulnerability class. MicroVMs and gVisor are where this category has converged: E2B and Everstack use Firecracker, Modal uses gVisor. Daytona is the case for pinning it down, because its documentation says default sandboxes are Linux containers while its README claims a dedicated kernel. If a vendor is vague about which primitive they use, settle that before reading anything else on their page.