A coding agent is asked to update a repository, run the tests, open a preview, and explain why one browser check failed. The first command succeeds in all three environments. A failure exposes whether workspace state and evidence survive long enough to resume and diagnose the run. Can the agent reconnect to the same workspace? Can a reviewer see the failed command beside the changed files? Does the work run as one durable session, a disposable function, or a developer-oriented computer?
Tangle Sandbox, Daytona, and Modal can all run code for an AI workflow. They are not interchangeable products. Daytona provides programmable sandboxes that can behave like development computers, including container, virtual-machine, Windows, and GPU options. Modal provides code-defined serverless functions, jobs, GPU workloads, and its own sandbox products. Tangle Sandbox provides an isolated computer per agent with files, shell, ports, snapshots, selectable coding backends, durable sessions, and traces.
The right comparison is the unit of work. Choose Daytona when you need a composable development environment that humans or agents can use. Choose Modal when the unit is a function, batch job, inference endpoint, or GPU workload. Choose Tangle Sandbox when the agent session itself must persist, reconnect, produce reviewable evidence, and fit into a broader Tangle workflow.
This is a product-fit comparison. It is not a current price, cold-start, or success-rate benchmark.
Sandbox, runtime, and agent profile
A sandbox is an isolated environment in which a program can run without sharing the application’s ordinary files, process space, or permissions. The exact boundary may be a container, a virtual machine, or a provider-managed computer. The word alone does not tell you how long the environment lives or what survives after the process exits.
A runtime is the software that creates the environment, starts the work, enforces limits, streams events, and cleans up or resumes the session. An agent runtime adds the model loop, tool permissions, task state, and stop rules. An agent profile is the configuration that selects the model, tools, permissions, budget, and policy for one agent. An evaluation is a repeatable task set that checks the result, cost, and policy behavior rather than trusting one successful run.
The products emphasize different boundaries:
| Product | Primary unit | Strong question to ask |
|---|---|---|
| Daytona | Composable sandbox or workspace | What kind of computer should the developer or agent control? |
| Modal | Function, job, endpoint, GPU task, or sandbox | What code should run, at what scale, and for how long? |
| Tangle Sandbox | Agent session in an isolated computer | What should the agent be able to do, resume, and prove? |
The unit of work determines which lifecycle, state, and artifacts matter before any feature comparison begins. A feature such as “snapshots” is useful only when you know whether it restores a developer workspace, a prebuilt function image, or an agent session.
What the public products document
The current Daytona sandbox documentation describes isolated runtimes with a dedicated kernel, filesystem, network stack, and allocated resources. It documents Linux containers by default, Linux and Windows virtual-machine options, and GPU sandboxes. That is broader than the old idea of a developer-only workspace.
The Modal introduction describes serverless execution, code-defined container environments, low-latency inference, large parallel jobs, training, notebooks, and thousands of isolated sandboxes for generated code. Its GPU guide documents selecting GPU types and counts in Python. Modal supports agent code through functions and isolated sandboxes, and model serving through code-defined containers and GPU resources.
Tangle’s Sandbox documentation describes a dev container or microVM, a lightweight virtual machine, with a shell, filesystem, ports, snapshots, optional GPU, and a selectable coding backend. It also documents durable agent sessions, per-second metering, isolated outbound network access, and traces that can be sent to Tangle Intelligence. A trace is the structured record of what a run received, did, and returned. It gives a reviewer a way to inspect a failure after the stream is gone.
The differences are about workflow state:
| Need | Daytona | Modal | Tangle Sandbox |
|---|---|---|---|
| Interactive computer for a person or agent | Strong fit | Available, but not the main abstraction | Strong fit |
| Code-defined function or batch job | Possible through the environment | Primary fit | Possible when the agent owns the workflow |
| GPU selection for a controlled job | Documented GPU sandboxes | Primary GPU and inference surface | Optional GPU lease around an agent step |
| Durable agent session and reconnect | Check the chosen lifecycle API | Depends on the product surface | Documented durable session API |
| Browser plus code plus review artifacts | Build the composition | Build the composition | Natural fit with Sandbox and Browser Agent |
| Tangle protocol service handoff | External integration | External integration | Adjacent Blueprint and payment paths |
A Tangle Blueprint is a reusable service definition. It declares the jobs, inputs, outputs, artifacts, and execution requirements that an operator can run. An operator is the infrastructure provider that runs a live service instance from that definition. The Blueprint documentation explains why a Blueprint is a template and a service instance is the running unit.
x402 is a payment protocol that lets software pay for an HTTP request through a 402 response and a signed payment payload. It is relevant only when the agent’s result becomes a paid service. It does not turn a sandbox into an evaluation or prove that the output is correct.
The same workload reveals the difference
Use a workload that forces state to survive:
- Create a fresh environment from a known image.
- Check out a small application.
- Install one dependency.
- Make two edits.
- Run a test that fails because of the first edit.
- Inspect the failure and repair the code.
- Start a preview server.
- Run one browser assertion.
- Return the diff, test output, preview evidence, and failure history.
- Reconnect after the client process is stopped.
The workload is intentionally ordinary. Persistent files, processes, ports, and reconnectable sessions turn the code runner into an agent computer.
The expected result should be recorded as an artifact:
{
"task": "repair-preview",
"workspace": {
"image": "known-image",
"filesChanged": 2,
"snapshot": "available-or-not"
},
"execution": {
"firstTest": "failed",
"repairTest": "passed",
"browserCheck": "passed"
},
"review": {
"logs": "attached",
"screenshots": "attached",
"trace": "attached",
"resumeAfterDisconnect": true
},
"cost": {
"computeSeconds": "recorded",
"gpuSeconds": 0
}
}This is an application-level test record. It is not a shared vendor response format. Its value is that a team can compare the same evidence across products.
Daytona should be tested as the full computer that a person or agent controls. Modal should be tested both as a function or job and, when relevant, as its sandbox product. Tangle should be tested as a durable agent session with the model backend and evidence flow enabled. Do not compare a serverless function with an interactive workspace and call the shorter setup the winner.
Choose the lifecycle before the product
The same command can appear in all three systems while its lifecycle means something different. A function usually ends when it returns a value. A workspace usually remains available for another command or a human inspection. An agent session may need both properties: short commands inside a computer that remains available after the client disconnects.
Write that expectation down before running a comparison:
| State | Function or job | Workspace | Agent session |
|---|---|---|---|
| Started | Invocation accepted | Computer provisioned | Session ID created |
| Disconnected | Query job state | Reconnect to environment | Reconnect to the same task |
| Failed | Preserve input and logs | Preserve files and process state | Preserve events, files, and decision history |
| Completed | Return output artifact | Leave or tear down by policy | Return result and review record |
| Retried | New invocation or deduplicated job | Continue or restore environment | Query the original session first |
This table is an application contract, not a claim that every provider exposes these exact states. It gives the test a concrete question for each product: what identifier does the caller store, what can it query after a lost response, and which artifacts survive cleanup?
For the repair-preview workload, persist the environment identifier before the first command. Persist the process or job identifier before starting the preview. Persist the browser evidence reference before returning the final result. If any of those identifiers is missing, the caller should report unknown state rather than start a second repair.
Missing workspace, command, and artifact identifiers force duplicate work or unknown state after a disconnect. If the product needs a developer to reopen the same computer, a function-only design creates recovery work around the function. If the product only needs a bounded transformation, keeping an interactive computer alive adds state that the result contract does not use.
For a fair trial, give each product the same recovery budget. Allow one client disconnect, one failed test, and one retry. Count a run as recovered only when the final artifact names the original environment or job and includes the first failure. This prevents a fast clean rerun from looking equivalent to a slower repair that preserved the investigation.
Tangle’s small public example
Tangle’s current quickstart uses the public @tangle-network/sandbox package:
npm install @tangle-network/sandbox
export TANGLE_API_KEY=sk-tan-...import { Sandbox } from '@tangle-network/sandbox'
const client = new Sandbox({
apiKey: process.env.TANGLE_API_KEY,
baseUrl: 'https://sandbox.tangle.tools',
})
const box = await client.create({
image: 'universal',
name: 'repair-preview',
backend: { type: 'opencode' },
})
try {
const result = await box.exec('node --version && npm --version')
console.log(result.exitCode, result.stdout)
} finally {
await box.delete()
}The Tangle quickstart documents this client shape, the create and delete lifecycle, and the coding-backend option.
The SDK reference documents exec, agent prompts, durable sessions, batch runs, snapshots, and GPU leases.
For a long-running agent, use a stable server-derived session identifier:
const { sessionId } = await box.dispatchPrompt(
'Repair the failing test and leave the preview ready for review',
{ sessionId: 'server-owned-task-17' },
)
for await (const event of box.session(sessionId).events()) {
console.log(event)
}
const final = await box.session(sessionId).result()
console.log(final)The session identifier example is illustrative. The public SDK documentation says a repeated session identifier returns the in-flight or completed session rather than running the work twice. That property matters for a webhook or payment-triggered task where the caller may retry after a lost response.
Where Daytona belongs
Start with Daytona when users need files, terminals, services, and pause or resume controls. A developer may need to inspect files, install packages, start services, use a terminal, and pause or resume a computer. An agent may need the same controls with an explicit resource and network policy.
The choice is especially clear when the team wants a programmable environment but does not need Tangle’s model routing, trace destination, or protocol service model. Daytona’s container, VM, Windows, and GPU choices let the team tune the environment to the workload. The team still owns the agent loop, evidence format, and evaluation.
Do not reduce Daytona to a human-only environment. Its current documentation explicitly positions sandboxes for AI agents. The honest comparison is whether its lifecycle and observability fit the session your agent needs.
Where Modal belongs
Start with Modal when code, resources, execution, and output form one invocation. The code declares the image and resources, the platform executes the function, and the caller receives the result. The same shape works for inference, batch processing, parallel tasks, and selected GPU workloads.
A minimal Modal GPU example from the public docs looks like this:
import modal
image = modal.Image.debian_slim().pip_install('torch', 'numpy')
app = modal.App('agent-evaluation')
@app.function(gpu='A100', image=image)
def check_cuda():
import torch
return torch.cuda.is_available()This example tests a function. It does not create a durable agent workspace with an edit history, browser report, or reconnectable task session. Those things can be built around Modal, but they are additional application design.
Choose Modal when the workload is naturally a function, queue, batch, inference service, or GPU task. Choose a different abstraction when the agent needs to keep negotiating with a changing workspace for an hour and a reviewer needs the whole path.
The failure cases change the answer
A green process exit is not enough for the shared workload.
A dependency install can succeed while the lockfile is wrong. A test can pass in a fresh image and fail after the agent’s edit. A preview server can bind to a port that the browser cannot reach. A browser check can pass while the agent edited the wrong project. A disconnected client can leave the work running or accidentally start a duplicate. A GPU task can exceed its budget if the lease or function timeout is not enforced.
For each failure, ask who owns the recovery state:
| Failure | Workspace-oriented recovery | Function-oriented recovery |
|---|---|---|
| Test fails after an edit | Reconnect to files, logs, and prior commands | Rerun with an input artifact and inspect logs |
| Client disconnects | Resume the same session | Query job state before retrying |
| Preview is wrong | Preserve process, port, and browser evidence | Recreate the function or external preview state |
| Output is incomplete | Review the trace and changed files | Reconcile job result, logs, and stored artifacts |
| Budget is exceeded | Stop the sandbox or GPU lease | Cancel the job and cap retries |
Tangle’s durable session API is designed for the second row in the first column. Modal’s function and job model is designed for the second column. Daytona can occupy either side depending on the selected sandbox type and lifecycle. The actual answer needs a test against the chosen configuration.
Choose the smallest correct boundary
Use Daytona when:
- the main artifact is a developer or agent workspace;
- the environment needs composable container, VM, Windows, or GPU choices;
- the team wants to own the agent loop and review layer;
- interactive control matters more than a function result.
Use Modal when:
- the unit is a function, queue, batch, inference endpoint, or GPU workload;
- the environment should be described in code;
- parallelism and resource selection are central;
- state can be represented as input and output artifacts.
Use Tangle Sandbox when:
- the agent needs a real isolated computer rather than one command;
- the session should survive a dropped client or redeploy;
- files, commands, browser results, and traces should travel together;
- the agent profile, model route, and tool policy belong to the same workflow;
- the result may later become a discoverable or paid service.
The internal posts AI agent sandbox, agent runtime environments, and Tangle Sandbox vs E2B develop the workspace question further.
Is Tangle Sandbox a Daytona alternative?
Yes, for agent sessions that need Tangle’s model, browser, trace, or protocol surfaces. Daytona fits when the team needs a programmable computer and intends to own the surrounding model, browser, trace, and service loop.
Is Tangle Sandbox a Modal alternative?
Only when the workload is an agent-owned workspace. Modal remains the better first benchmark for functions, queues, serverless inference, batch jobs, and GPU-heavy execution.
Does Daytona only support human developers?
No. Daytona’s current documentation describes sandboxes for AI agents as well as developers. The deciding issue is lifecycle, evidence, and control policy rather than the label “developer workspace.”
Does Modal only run short functions?
No. Modal documents serverless inference, batch work, training, notebooks, GPU execution, and isolated sandboxes. The question is whether the state and recovery model match the workflow.
What should an evaluation compare?
Use the same task, inputs, model policy, resource limits, timeout, retry rules, and output contract. Compare task success, recovery after failure, evidence quality, cost, latency, and duplicate-work behavior.
The decision
Write down the artifact that must survive the run. If it is a function result, test Modal. If it is a composable computer for a developer or agent, test Daytona. If it is a durable agent session with workspace state, browser evidence, traces, and a path to a Tangle service, test Tangle Sandbox. Then run the repair-preview workload in the exact configuration you plan to ship. For browser-specific evidence and session ownership, continue with Tangle Browser Agent vs Browserbase and Browser Use.