AI Code Sandbox 2026: Cloudflare vs Modal vs E2B vs Daytona

TL;DR + decision tree

• If the agent runs short, latency-sensitive code at request time and you already live on the Cloudflare developer platform, pick Cloudflare Container Sandbox. Containers run alongside Workers at the edge, bill in 10 ms increments, and sleep to zero when idle — the shape favours per-request execution close to the user.
• If the agent needs serverless GPUs or heavy Python workloads, pick Modal. A single @modal.function decorator gives you T4, L4, A10, A100, and H100 access with public per-second pricing — by a wide margin the deepest serverless GPU catalogue in this group.
• If the agent is the use case — a code interpreter, an LLM-generated-code executor, a notebook-in-a-loop — pick E2B. It was designed for that pattern from line one, ships first-class Python and JS SDKs, and the Apache 2.0 repo lets you self-host if hosted SaaS limits get in the way.
• If the agent needs a persistent workspace (clone a repo, install deps, work across many turns), pick Daytona. It’s closer in spirit to GitHub Codespaces than to a per-call sandbox — a different shape of problem these platforms can also serve.

These four are not direct substitutes. Cloudflare and E2B both fit a per-call execution shape, but at different latency regimes (Cloudflare = ms at the edge, E2B = sandboxes that hold state for hours). Modal is the only realistic answer for serverless GPU. Daytona is the only one optimised for long-lived workspaces. Plenty of production agents end up using two of these together — for example, E2B for code interpretation and Modal for model inference — and the cost ceiling matters less than picking the right shape for each call.

What an AI-agent sandbox is for

Three things have to be true before an agent can execute code safely: the code runs in isolation (you don’t trust it with your host filesystem, your network, or your credentials), it runs with bounded resources (CPU, memory, disk, wall-clock) so a hallucinated infinite loop can’t bankrupt you, and it runs with controlled egress so the model can’t accidentally exfiltrate a secret over an outbound HTTP call. Every platform in this comparison gives you all three; they differ in how they arrange the primitives.

The canonical use cases break into four shapes. Code interpreter — the LLM writes a snippet of Python that computes an answer, the sandbox runs it, the output goes back in the next turn. This is the OpenAI Code Interpreter pattern, and E2B is the platform built around it. Per-request execution — the agent calls a tool that needs to compile, transform, or otherwise compute something during a user-facing request. Cloudflare Container Sandbox shines here because the compute is co-located with the edge Worker that serves the user. Heavy task offload — the agent has identified a job (run this notebook, fine-tune this model, transcode this video) that doesn’t need to complete during the request. Modal’s decorator API and deep GPU catalogue dominate this shape. Persistent workspace — the agent works across many turns inside a stable filesystem and process tree, the way a human developer uses a Codespace. Daytona is shaped for this.

The honest answer for most production agents is: you’ll end up running two of these. A coding agent typically pairs a code-interpreter (E2B) with a persistent workspace (Daytona or a self-managed VM). A research-assistant agent pairs Modal (model inference, dataset processing) with an edge sandbox (Cloudflare) for the user-facing request handler. The mental model here is “match the platform to the shape of the call,” not “pick a single winner.”

The four axes that matter

Every benchmark comparing these platforms is a benchmark of four variables. Get them right and the rest follows; get them wrong and you’ll be reading apples-to-oranges latency charts forever.

1. Cold start. The time from “agent asked to run code” to “first line of user code executing.” This is a complex number because it varies by image size, region, warm-pool depth, and recent invocation history. Cloudflare claims sub-second starts at the edge because Containers ride the Workers cold-start path. Modal advertises sub-second on warm pools and multi-second on cold pulls of a fresh GPU. E2B’s default code-interpreter template starts in the low hundreds of milliseconds. Daytona’s pitch is “sandboxes in milliseconds” for its sandbox primitive, with workspace-creation taking longer because a workspace provisions a full dev image.

2. GPU access. Is GPU a first-class primitive? Modal is the unambiguous winner — its decorator API exposes T4, L4, A10, A100 (40GB and 80GB), and H100 with public per-second pricing. E2B’s standard plans don’t expose GPUs; that’s an enterprise conversation. Daytona is CPU-first; you’d attach an external GPU runner. Cloudflare Containers, as of the 2026 pricing docs we checked, don’t list GPU instance types — for GPU workloads at the edge you reach for Cloudflare Workers AI instead, which is a different product.

3. Persistence. Does state survive between calls? Cloudflare sandboxes scale to zero and start fresh — state goes in a Durable Object, KV, or R2 alongside, not in the container itself. Modal Functions are similarly stateless by default, with persistent volumes available as an opt-in. E2B sandboxes hold state for up to 1 hour (Hobby) or 24 hours (Pro) — long enough for a multi-turn coding session but not permanent. Daytona is the only one in this group where the default is persistent — a workspace exists until you delete it, like a Codespace.

4. Language model. What can the agent actually execute? Cloudflare and Daytona are language-agnostic — you bring an image, you run anything. E2B’s SDKs are Python and JS first; the underlying sandbox can run anything you install. Modal is the most opinionated: you’re writing Python at the boundary even if the function shells out to Node or Rust. This matters more than vendors imply — if your agent generates Rust code, Modal’s Python-first ergonomics will frustrate you fast.

Side-by-side matrix

Every cell below is sourced from the official repo, vendor docs, or current pricing page as of May 2026. Treat numeric values as a snapshot — confirm at the source before signing a purchase order or making an architectural commitment.

Four takeaways. First, only two are open-source (E2B, Daytona) — both Apache 2.0. Second, only Modal lists public GPU instance pricing, and the catalogue is deep. Third, Daytona is the outlier on persistence — the workspace abstraction implies a different shape of agent than the other three. Fourth, every platform here can carry an MCP integration, but the path differs: Cloudflare ships its own, Modal expects HTTPS over its SDK, E2B is the agent-native option, and Daytona has community MCP wrappers.

Cloudflare Sandbox

Cloudflare Container Sandboxcloudflare

Official1-Click ReadyRemote

Cloudflare Container Sandbox lets your MCP client run secure, sandboxed LLM code in Node or Python. Run code safely in t

4031

What it does best

Cloudflare Container Sandbox is the right answer when your agent runs per-request code that needs to be close to the user. Containers run alongside Workers across Cloudflare’s 300+ edge locations, billing in 10 ms increments, with a sleep-to-zero behaviour that makes idle containers free. The killer feature is co-location: the Worker that handles the request, the Durable Object that holds session state, the R2 bucket that stores artifacts, and the Container that runs user-submitted code can all be the same region, the same network, and even the same cold-start path. For a developer already building on Cloudflare, that’s a stark contrast to firing a request from a Worker to an E2B sandbox three hops away.

Pick this if you...

• Already deploy to Cloudflare Workers and want sandbox compute on the same network, billing surface, and observability stack
• Run per-request code that has to feel instant to the user — edge co-location wins this round
• Want a single billing relationship for compute, storage (R2), KV, DO, and egress, instead of a fan-out across multiple SaaS bills
• Don’t need GPUs in the sandbox itself (you reach for Workers AI for that)

Recipe: edge sandbox from a Worker

The minimal pattern: a Worker handles the request, spawns a Container via the Containers SDK, runs untrusted code, returns the result. The container image is your responsibility; the Worker glue stays terse.

// worker.ts — runs at the edge, spawns a Container sandbox
import { Container } from "cloudflare:containers";

export default {
  async fetch(req, env) {
    const { code } = await req.json();

    const sb = await env.SANDBOX.create({
      image: "registry.cloudflare.com/your-account/python-sandbox:latest",
      memory: "512MiB",
      timeoutMs: 30_000,
    });

    const result = await sb.exec(["python", "-c", code]);
    await sb.destroy();

    return Response.json({
      stdout: result.stdout,
      stderr: result.stderr,
      exit: result.exitCode,
    });
  },
};

Wire the Container binding in wrangler.toml, build the image (Cloudflare ships a managed registry), deploy with wrangler deploy. Total deployment surface area: one Worker file, one Dockerfile, one wrangler config. The hot path stays on Cloudflare metal end-to-end.

Skip it if...

Your sandbox needs a GPU, or your team isn’t already on Cloudflare. The platform’s value is integration with the rest of Workers; if you’re paying AWS or GCP for everything else, you’re trading away one of the main reasons to be here. There’s also no public free tier for Containers — the $5/mo Workers Paid plan is the floor.

Modal

Modalmodal-labs

Official1-Click Ready

Modal: serverless cloud compute for running Python functions, training models, and deploying GPU workloads from AI assis

cloud infrastructure2.6k165

What it does best

Modal is the cleanest serverless GPU experience in the category. The decorator API (@modal.function) means a Python function with the right GPU annotation goes from local script to running on an H100 in one deploy. Modal lists public per-second prices for six GPU SKUs — T4 ($0.000164/sec), L4 ($0.000222/sec), A10 ($0.000306/sec), A100 40GB ($0.000583/sec), A100 80GB ($0.000694/sec), and H100 ($0.001097/sec) — and the Starter plan ships with $30/month in free credits and zero minimum spend. For agents doing model inference, fine-tuning runs, RAG indexing on big corpora, or any GPU-bound batch work, the ergonomics are hard to beat.

Pick this if you...

• Need GPUs the agent can reach with a single function call — model inference, training, fine-tuning, embedding at scale
• Write Python and don’t want to spend Friday on Kubernetes
• Prefer pay-per-second billing with no idle charges to a reserved-instance commitment
• Want a single decorator to define the function, image, GPU, secrets, schedule, and concurrency limits in one place

Recipe: run an LLM inference from an agent

The minimal pattern: define a Modal Function that loads a model on an A10, expose it as a web endpoint, and have the agent call it over HTTPS.

# llm.py — modal deploy llm.py
import modal

app = modal.App("agent-llm")

image = (
    modal.Image.debian_slim()
    .pip_install("transformers", "torch", "accelerate")
)

@app.function(image=image, gpu="A10", timeout=300)
@modal.web_endpoint(method="POST")
def generate(payload: dict):
    from transformers import AutoTokenizer, AutoModelForCausalLM
    import torch

    model_id = "your-org/your-model"
    tok = AutoTokenizer.from_pretrained(model_id)
    model = AutoModelForCausalLM.from_pretrained(
        model_id, torch_dtype=torch.float16, device_map="cuda"
    )

    ids = tok(payload["prompt"], return_tensors="pt").to("cuda")
    out = model.generate(**ids, max_new_tokens=256)
    return {"text": tok.decode(out[0], skip_special_tokens=True)}

The agent calls the deployed URL with an HTTP POST. Modal handles the A10 provisioning, scaling, and idle teardown. You only pay while the function is actually running — there are no reserved-instance costs to absorb when traffic dips.

Skip it if...

You don’t need GPUs and you don’t live in Python. For non-Python first-class language support, Modal forces you into a Python wrapper; for CPU-only edge-latency work, Cloudflare Container Sandbox is closer to the metal. Modal also doesn’t self-host — if regulated workloads require on-prem deployment, look at E2B or Daytona instead.

E2B

E2B Sandboxese2b-dev

Official1-Click Ready

Official E2B MCP server for secure, sandboxed code execution. Run Python and JavaScript in isolated cloud sandboxes with

search web7.0k381

What it does best

E2B is the only platform here built from line one for AI agents. The headline tagline is “open-source, secure environment with real-world tools for enterprise-grade agents” — and the SDK shape reflects that. Every modern agent framework (LangChain, OpenAI Agents SDK, Anthropic SDK, AutoGPT, CrewAI) has a published E2B integration; the code-interpreter pattern is canonical. The repo is Apache 2.0 under github.com/e2b-dev, which means the sandbox infrastructure itself is open-source — a real differentiator for teams who can’t put untrusted code execution behind closed-source SaaS. Hosted Hobby starts at $100 one-time credits, 20 concurrent sandboxes, 1-hour sessions, 10 GiB storage; Pro is $150/month for 100 concurrent sandboxes and 24-hour sessions.

Pick this if you...

• Are building a code interpreter, LLM-generated code runner, or any agent whose primary tool is “execute this snippet”
• Need first-class Python and JS SDKs that read like SDKs (not bash scripts wrapping curl)
• Want a fully open-source path if hosted SaaS limits or compliance requirements force a self-host
• Use one of the major agent frameworks — published E2B integrations remove most of the integration toil

Recipe: code interpreter from an agent loop

The minimal pattern: spawn a sandbox, send LLM-generated code, get the result, return it to the model, repeat.

# pip install e2b_code_interpreter openai
from e2b_code_interpreter import Sandbox
from openai import OpenAI

client = OpenAI()
sbx = Sandbox()  # spawns a hosted E2B sandbox

prompt = "Plot the distribution of arrival delays for ORD in this CSV."
chat = [
    {"role": "system", "content": "You write Python. Output code only."},
    {"role": "user", "content": prompt},
]
resp = client.chat.completions.create(model="gpt-4o-mini", messages=chat)
code = resp.choices[0].message.content

execution = sbx.run_code(code)
print(execution.text)              # stdout
print(execution.error)             # any traceback
for f in execution.results:        # rich outputs (PNGs, dataframes)
    f.png and open("plot.png", "wb").write(f.png)

sbx.kill()

Three lines of E2B-specific code; the rest is the model call. The sandbox starts in a few hundred milliseconds, the agent gets stdout, stderr, and any rich outputs (matplotlib plots, pandas frames) as structured results. This is the pattern E2B was designed for, and it shows.

Skip it if...

Your sandbox call is a one-shot per-request job tied to a user-facing latency budget — paying for a session-shaped sandbox to run code that takes 80ms is overkill, and Cloudflare Container Sandbox is a better fit. Also skip if you need GPUs on standard tiers; that’s an enterprise conversation with E2B today.

Daytona

What it does best

Daytona is the Codespaces-shaped option in this group. Its core abstraction is a workspace — a persistent environment that an agent (or human) connects to over SSH, the web IDE, or the API. Sandboxes are also part of the offering — Daytona advertises “sandboxes in milliseconds” — but the gravity of the product is on long-lived workspaces. For an agent that needs to clone a repo, install dependencies once, and then work across many turns (running tests, building, grepping, editing) that’s the right shape. The cloud ships $200 in free compute credits and pay-as-you-go after that (compute at $0.0504/vCPU-hour, memory at $0.0162/GiB-hour, storage at $0.000108/GiB-hour after the first 5 GiB). The Apache 2.0 repo at github.com/daytonaio/daytona lets you self-host the entire control plane.

Pick this if you...

• Run an agent that needs a persistent workspace it returns to — coding agents, project assistants, multi-day research agents
• Want a Codespaces alternative without binding to a single cloud vendor — Daytona runs on AWS, GCP, Azure, Hetzner, bare metal, or on-prem
• Need on-prem or air-gapped deployment for regulated workloads; the self-host path is real
• Are okay with the per-call latency tradeoff (a workspace takes longer to create than a per-call sandbox spins up)

Recipe: agent gets a workspace

The minimal pattern: use the Daytona CLI or API to create a workspace from a git repo, hand the agent an SSH or API endpoint, let it work.

# Daytona CLI sketch
daytona create https://github.com/your-org/agent-target \
  --name agent-workspace-1 \
  --idle-timeout 8h

# Get the workspace's API endpoint, give it to the agent
daytona info agent-workspace-1 --json

# The agent then issues shell commands via the API:
#   POST /workspaces/agent-workspace-1/exec
#   { "command": "npm test", "timeout": 60 }
#
# Workspace persists between calls — install once, run many.

This is the shape that matters: the agent doesn’t pay a cold-start price for every tool call, because the workspace is already there. Costs accumulate at the workspace-running rate rather than the per-call rate. Set an idle timeout so the agent doesn’t leave a forgotten workspace burning credits.

Skip it if...

Your agent only needs per-call code execution and never returns to the same environment — paying workspace prices for one-shot calls makes no sense. Skip also if you need GPUs as a first-class primitive; Daytona’s cloud is CPU-first.

Cold start vs warm pool

Cold start is the most-debated and least-honest number in this category. Every vendor has a low number; every customer has a high one. The reason is that “cold start” is an umbrella over at least three distinct events: the control-plane decision to allocate, the runtime image pull, and the user-code initialisation. Vendors quote the fastest of those; users measure the sum.

Cloudflare Container Sandbox wins the geographic dimension because the container starts at the same edge node serving the user request. That cuts the control-plane round-trip out of the equation — a request that lands in Frankfurt gets a container in Frankfurt. Image-pull time still dominates if your image is large; Cloudflare bills in 10 ms increments, so a 1.5-second pull on a 200 MiB image is a real cost. Optimise the image like you would a Lambda layer: minimal base, only essential layers, no dev dependencies.

Modal distinguishes between “warm” calls (a container with your image is already running and just gets a new request) and “cold” calls (Modal has to pull the image, attach a GPU, and start the Python process). Warm calls are sub-second end-to-end; cold calls on H100 can be 10+ seconds because GPU attachment is non-trivial. The fix is warm pools — Modal lets you configure min_containers to keep some warm, trading idle cost for latency. For an interactive agent loop, set this above zero; for batch jobs, leave it at zero and accept the cold start.

E2B ships with a default template that starts in the low hundreds of milliseconds. Custom templates — particularly ones with large data science dependencies baked in — can take seconds. The trick is to lean on the default template and install extras at runtime inside the sandbox if you don’t need them on every call.

Daytona distinguishes its sandbox primitive (advertised milliseconds) from its workspace primitive (seconds because it provisions a full dev image). For the workspace shape this matters less because the cost amortises over a long session.

The honest measurement: spin up a 100-call loop on each platform from the region you actually deploy in, record p50 and p99 of the first byte of user-code output. Numbers from a vendor blog at the platform’s home data centre will not match your production numbers.

GPU access for agents

If your agent’s critical path is GPU inference, training, fine-tuning, or large-batch embedding, Modal is the obvious choice. It’s the only platform in this group with a public, deep, per-second GPU price list. Six SKUs, all decorator-bookable in one line of Python — T4 at $0.000164/sec for small inference, L4 at $0.000222/sec for video and recent-gen inference, A10 at $0.000306/sec for mid-tier model serving, A100 in 40GB and 80GB variants for serious training, and H100 at $0.001097/sec for cutting-edge workloads.

E2B doesn’t expose GPU sandboxes on its standard tiers. Customising CPU and RAM is a Pro feature; getting a GPU sandbox is an enterprise conversation. If your agent needs to occasionally generate a plot from a CPU pandas pipeline, E2B is fine; if it needs to run a vision model on every turn, route that work to Modal and use E2B only for the post-processing.

Daytona is CPU-first on its cloud. For GPU workspaces you attach external compute — a common pattern is a Daytona workspace acting as the agent’s persistent shell, with GPU jobs offloaded to a Modal Function or a Replicate endpoint. The agent stays in the workspace; the GPU lives elsewhere; the bill stays sane.

Cloudflare Containers, as of the 2026 pricing docs we checked, do not list GPU instance types. For GPU work on the Cloudflare platform you reach for Workers AI — a separate product that serves a curated catalogue of open models behind an inference endpoint. That’s a different shape than “run arbitrary CUDA code in my container,” and the gap is real.

Pricing shape

Numbers below are pulled from each vendor’s pricing page. Treat as a snapshot — confirm before architectural commitments.

Three things matter here. First, the units don’t match: Cloudflare bills in 10 ms increments, Modal and E2B in seconds, Daytona in hours. A workload that runs for 80ms costs differently on each platform even at apples-to-apples vCPU rates. Second, Modal is the only one with public GPU pricing. Third, free-tier credits ($30/month at Modal, $100 one-time at E2B, $200 trial at Daytona) make it cheap to validate volume on three of the four before committing. Cloudflare’s $5/month Workers Paid plan is the only forced commit.

Common pitfalls

Community signal

The agent-sandbox space is younger than the docs-RAG or observability spaces — most of these platforms shipped or pivoted into “built for agents” positioning during 2024 and 2025. That means consensus is still forming, and we won’t fabricate quotes. What is consistent across HN threads, GitHub discussions, and vendor blogs in the lead-up to 2026: production agent teams pick by shape, not by single winner, and rarely use just one of these in isolation.

E2B’s positioning in its own README is unambiguous:“open-source, secure environment with real-world tools for enterprise-grade agents” — the category-defining tagline for code-interpreter-shaped sandboxes, and the reason every major agent framework ships an E2B integration as a first-class option. Modal’s pricing page lays out the GPU economics in the open (“you always pay for what you use and nothing more. You never pay for idle resources”), which is the signal teams cite when explaining why Modal won the serverless-GPU comparison internally.

Cloudflare’s positioning leans hard on the integrated developer-platform story — the value is not Containers in isolation, it’s Containers plus Workers plus Durable Objects plus R2 plus Workers AI under one billing surface. Daytona’s repo describes the platform as a workspace manager, not a sandbox; the community framing is closest to “self-hostable Codespaces” and that’s the shape teams adopt it for. None of these positionings contradict each other; they just describe different shapes of the same underlying need.

Frequently asked questions

Sources

Cloudflare Container Sandbox

• developers.cloudflare.com/containers — platform docs
• developers.cloudflare.com/containers/pricing — current pricing (10ms billing, included resources)
• developers.cloudflare.com/workers-ai — Cloudflare’s GPU-inference path

Modal

• modal.com/pricing — per-second CPU and GPU pricing
• modal.com/docs — decorator API, images, GPUs, warm pools
• github.com/modal-labs — SDK source

E2B

• e2b.dev/pricing — Hobby and Pro tiers
• github.com/e2b-dev/e2b — Apache 2.0 sandbox infrastructure
• e2b.dev/docs — SDKs and code-interpreter pattern

Daytona

• github.com/daytonaio/daytona — Apache 2.0 workspace manager
• daytona.io/pricing — cloud pricing, $200 trial credits
• daytona.io/docs — workspace and sandbox primitives

Internal links

• /servers/cloudflare-container-sandbox
• /servers/modal
• /servers/e2b-sandboxes
• /blog/oauth-21-for-remote-mcp-servers-streamable-http-explained-2026
• /blog/what-is-mcp
• /best-mcp-servers