Agent 37 vs E2B vs Daytona: which one does your AI agent actually need?

People search for "Agent 37 vs E2B" and "Agent 37 vs Daytona" expecting a feature grid between three products in the same category. They are not in the same category. E2B and Daytona sell isolated sandboxes that execute code an agent wrote. Agent 37 sells an always-on computer that runs the agent itself. Comparing them on startup time or SDK coverage misses the question that actually decides the purchase: which layer of the stack are you missing?

This post lays out the three layers, says plainly what each vendor is good at, and explains where Server4Agent sits, because it is a fourth shape that many teams reach for once they have tried the other three.

Quick answer

Pick E2B or Daytona when your own application owns the agent loop and needs a safe, disposable place to run generated code. Pick Agent 37 when you want a hosted, always-on instance of an open-source assistant such as Hermes or OpenClaw, managed from a browser or an API, with memory and connected accounts that survive between tasks. Pick a persistent build server like Server4Agent when the agent's job is to produce working software that keeps running at a URL after the conversation ends. The last two combine well: an agent hosted on Agent 37 can use Server4Agent as its build and deploy target.

Key takeaways

• Sandboxes answer "where does generated code run safely." Agent hosting answers "where does the agent live." Build servers answer "where does the finished app live."
• E2B and Daytona are the closest pair. Both give you a fast, isolated Linux environment per run with SDKs to drive it from your backend.
• Agent 37 is not a sandbox. It keeps files, memory, sessions, and integrations on a per-user instance until you delete it.
• The dividing question is what should still exist after the run: nothing, a computation result, a conversational agent, or a hosted app.
• Most production setups end up with two of these layers, not one.

The three layers, defined

Layer 1: code execution sandboxes. E2B describes its product as isolated sandboxes that let agents safely execute code, process data, and run tools. Each sandbox is a Linux VM created on demand. Daytona describes its sandboxes as composable computers with a dedicated kernel, filesystem, network stack, and allocated vCPU, RAM, and disk, and emphasises spinning up in well under a second. In both cases the customer is a developer whose application calls an SDK, runs something, collects the output, and moves on.

Layer 2: hosted agent runtimes. Agent 37 gives every user their own hosted agent computer. The default template runs Hermes, and the platform also offers OpenClaw instances. Files, connected accounts, and memory stay on the instance until you delete it. You provision an instance with one API call, then talk to the agent through a responses endpoint that keeps conversation history on the instance, so you can reuse a session id instead of resending prior messages. There is a white-label dashboard for people who do not want to build a chat UI, and support for custom Docker images for people who do.

Layer 3: persistent build servers. This is where Server4Agent lives. The agent connects over MCP or a plain HTTP API, provisions a sized server, creates a project, writes code, installs dependencies, runs the app, reads its own logs, fixes what broke, and publishes to a stable URL that is private by default. The deliverable is not a transcript or a computation. It is a hosted app someone else can open tomorrow. We describe the model in what is an AI agent server.

Agent 37 vs E2B

This is the comparison people search for most, and it is the most lopsided, because the two products barely overlap.

E2B is built for the case where you are shipping an AI product and the model inside it needs to run code. Your backend creates a sandbox, streams commands in, reads results out, and tears it down or pauses it. E2B's pause and resume feature saves both the filesystem and memory of a sandbox, and paused sandboxes are kept until you kill them, which softens the "everything disappears" objection for longer tasks. The SDKs are Python and JavaScript, and templates define what environment a sandbox starts with.

Agent 37 is built for the case where you do not want to write an agent loop at all. You want a capable open-source assistant running around the clock, with a task board, a terminal, a file browser, scheduled jobs, and integrations to the tools your users already use. Agent 37's own writing draws the line clearly: sandbox vendors spin up short-lived compute for a code run and tear it down after, whereas client agents need files, memory, sessions, and channel connectors that survive between tasks.

So the question is not which is better. It is whether you are building the agent or renting one.

If you are already on E2B and hitting the edge of what a sandbox should do, E2B alternatives for AI agents walks through the options by category, and Server4Agent vs E2B is the direct head-to-head.

Agent 37 vs Daytona

Everything above applies, with one nuance that matters for anyone choosing between these two specifically.

Daytona leans harder into persistence than a classic sandbox. It offers stateful environment snapshots intended for persistent agent operations across sessions, and its SDK coverage is broad: TypeScript, Python, Ruby, Go, and Java, plus a REST API and CLI. That makes Daytona a stronger fit than E2B for teams who want sandbox isolation but also want to bring a workspace back tomorrow in the state they left it.

It is still a sandbox, though. The thing that persists is an environment your code drives. On Agent 37, the thing that persists is an agent with its own memory and its own connected accounts, waiting for the next message. If your users expect to talk to something that remembers them, Daytona is the wrong layer. If your engineers expect to orchestrate many parallel executions of generated code from a service, Agent 37 is the wrong layer.

For the sandbox side of this decision, Daytona alternatives for AI agents and Server4Agent vs Daytona cover the tradeoffs in detail.

Where Server4Agent fits, and why it pairs with Agent 37

Here is the gap none of the three products above is designed to close. Suppose the agent, wherever it lives, has just written a working internal tool: a form, a database, a small dashboard. On a sandbox, the run ends and the app is gone unless you build a deploy pipeline around it. On a hosted agent runtime, the app is running on the agent's own box, tied to that instance and that user, which is fine for the agent's own tooling but awkward as a product a team shares.

Server4Agent gives the agent a persistent server with projects, each with its own files, its own process, and its own stable URL. The agent does the provisioning and the deploying itself over MCP, which means any assistant that speaks MCP can use it, including the assistants Agent 37 hosts. We have written setup guides for exactly those two: OpenClaw with Server4Agent and Hermes Agent with a build server. The pattern is the same in both: the assistant keeps its memory and its chat channels where it already runs, and gains tools to create a server, write code, build, and publish to a live URL.

The practical consequences of that split:

• The agent and the app have separate lifecycles. You can delete or rebuild an agent instance without taking down what it shipped.
• There is something to hand over. A developer can open the project files, read the logs, and continue the work, which is the argument in why AI agents need persistent workspaces.
• Review happens before exposure. Projects stay private until you decide to publish, so an always-on agent acting on your behalf cannot put something public by accident.
• Spend has a ceiling. Account-wide budget caps stop autonomous work at the number you set, which matters more for an agent that runs all month than for a sandbox you tear down after each call.

A decision checklist

Answer these in order and the shortlist usually collapses to one or two products.

• Who writes the agent loop? If you do, start with a sandbox. If you would rather rent a finished assistant, start with a hosted agent runtime.
• What should exist after the run? Nothing: E2B. A resumable environment: Daytona or a paused E2B sandbox. A remembering agent: Agent 37. A hosted app at a URL: a build server.
• Who opens the result? A service that parses output, or a person who clicks a link. The second answer pushes you toward a build server regardless of what runs the agent.
• How many executions run in parallel? High fan-out favours the sandbox vendors, whose whole design is many short isolated runs.
• Where do secrets and integrations live? On the agent's instance if the agent uses them, in a project secret store if the shipped app uses them. Often both.

FAQ

Is Agent 37 an E2B alternative?

Only in the loose sense that both give an agent a Linux machine. E2B is a sandbox your code controls per run. Agent 37 is a hosted assistant that persists per user. If your search started because a sandbox kept forgetting things, look first at whether you need a hosted agent or a persistent build server, because those are different fixes.

Is Daytona more persistent than E2B?

Daytona positions snapshots as a first-class feature for carrying state across sessions, and E2B offers pause and resume that preserves filesystem and memory. Both are ways of keeping a sandbox around. Neither turns a sandbox into an always-on agent or into a hosted app with a stable public URL.

Can I use Agent 37 and Server4Agent together?

Yes. An assistant hosted on Agent 37 that supports MCP can connect to Server4Agent and use it as the place where the software it writes gets built and published. The agent keeps its memory and channels on Agent 37. The apps it ships live on Server4Agent at their own URLs.

Which one is cheapest?

It depends entirely on the shape of the workload, and published prices change, so check each vendor's pricing page. The useful rule is to price the thing you actually need to keep alive: seconds of sandbox time, a month of agent uptime, or a month of hosting for a shipped app.

Related reading

• E2B alternatives for AI agents in 2026
• Daytona alternatives for AI agents in 2026
• Sandboxes vs build servers
• What is an AI agent server? A server with an agent inside
• OpenClaw + Server4Agent: your personal AI assistant can ship real tools now

External references

• E2B documentation
• Daytona
• Agent 37 documentation
• Model Context Protocol documentation