Containers vs VMs vs Cloud Sandboxes

This market is being decided less by whether a sandbox can run code, and more by what kind of computer it really gives an agent. In practice, developers are choosing between a fast disposable container, a longer lived VM that keeps files and sometimes memory, and a cloud native feature inside a broader platform. That choice determines security posture, cold start behavior, whether an agent can pick work back up mid task, and whether the team pays mainly for active compute or for always available state.1 2 3 4 5 6

• Isolation is not uniform across vendors. E2B describes each sandbox as a Firecracker microVM with its own kernel. Daytona now documents microVM isolation in v2, but also still offers Linux container sandboxes that share the host kernel, which matters for teams running untrusted code or customer data.1 4 5
• Session persistence is where products separate most clearly. Daytona says persistence is on by default, with files surviving stop and start, and VM sandboxes able to pause and resume with memory intact. E2B positions multi day persistent sessions, pause and resume, and deployment choices across managed cloud, BYOC, and self hosted runtime.2 3 6
• The competitive set is widening beyond specialists. Vercel Sandbox packages isolated execution inside a larger deployment platform and prices active CPU rather than just reserved machines. That pulls the buying decision toward total workflow cost, especially for teams that already deploy app backends on the same platform.7 8

Going forward, the winners are likely to be the platforms that make an agent sandbox feel less like rented compute and more like a resumable remote computer. That pushes E2B and Daytona toward richer state, stronger isolation, and more deployment flexibility, while cloud platforms keep bundling sandboxing into the rest of the AI app stack.2 3 7 8