Daytona pitches itself as secure and elastic infrastructure for running AI-generated code, which puts it in direct competition with E2B, and reading both sets of documentation back to back is a genuinely useful exercise in understanding the design space. The documentation covers creating sandboxes, executing code, filesystem operations, git integration, process execution and terminal access, with SDK guides for TypeScript, Python, Ruby, Go and Java, REST APIs with OpenAPI specs, a CLI, and agent skills documentation. The breadth of language support is the first thing that stands out. Five SDKs is more than this category usually offers, and if your stack is Go or Java you have generally been excluded from the agent tooling conversation entirely.
The technical idea worth understanding is stateful snapshots. Sandboxes are not only ephemeral compute, they can be snapshotted and resumed, which means an agent can persist its environment across sessions. That sounds like a minor feature and it changes what you can build. An agent working on a task over hours or days with a filesystem, installed packages and accumulated state is a different thing from one that starts fresh every invocation.
Anyone building long-running autonomous workflows should read that section carefully, because it reshapes the architecture. Pricing transparency deserves credit. Compute at $0.0504 per vCPU hour and $0.0162 per GiB of memory per hour, storage at $0.000108 per GiB hour after 5 GiB free, Windows at a premium, and GPUs published from about $0.57 an hour for an RTX 4090 to $3.59 for a B200. Per-second billing.
$200 in free trial credit without a credit card, plus a startup programme offering up to $50,000 in credits. Publishing GPU rates openly rather than routing everyone to a sales call is the behaviour I want to reward, because it lets you cost a project before committing to it. The drawback of per-second everything is that forecasting is genuinely hard. You can compute a unit rate easily and predicting your monthly bill for an agent with variable workload is another matter.
The docs would be better with worked cost examples for a few common patterns. As education the limits are clear. This is reference documentation. There is no conceptual introduction, no learning path, no explanation of why you would want sandbox isolation in the first place, and nothing that teaches an architecture.
If you already know what you are building it is efficient and well-organised. If you are learning the space you will be lost. The sub-100ms start claim is a vendor number and I would measure it under your own conditions before designing around it. Cold start behaviour is exactly the kind of metric that looks different in a benchmark than it does in production.
The unaddressed question is positioning. Daytona and E2B solve the same problem and the documentation never engages with when you would pick which, which leaves evaluation entirely to you. Three point seven. Capable, transparent and broad, held slightly below E2B for learners because there is even less teaching in it.