E2B vs. Modal vs. Daytona: Which Cloud Sandbox is Best for AI Agents?

Correction (2026-10-12): This post originally misreported several Sandboxes Edition 1 results. Corrected here: Daytona's Integration Tax is 1.43x, not 1.04x; Modal's pass rate is 5/6 (its Trial 6 ran no tests), which raises its IT to 1.37x under the published penalty rule; Modal had 0 human stops, not 1, and the ingress "operator stop" is not captured in the published transcripts; "100% clean teardown" could not be verified by the published verifier; tokens, steps and times were self-reported by the agent; and prompts differed by provider, so the comparison is indicative only. Full list with evidence: Sandboxes Edition 1 errata (also in the research repository).

When building autonomous AI coding workflows—whether powering automated SWE-bench runners, agentic pull request bots, or interactive coding assistants—selecting the right cloud sandbox architecture is one of the most critical infrastructure decisions an engineering team makes.

Three platforms dominate current discussions among AI agent engineers: E2B, Modal, and Daytona.

While all three provide secure, isolated remote execution, their architectural foundations and developer abstractions could not be more different. In our recent empirical benchmark, The Glintbase Integration Index: Cloud Sandboxes for Autonomous AI Coding Agents, we evaluated all three platforms as part of 30 live autonomous agent integration trials (six per provider, five providers).

Here is an architectural comparison of E2B, Modal, and Daytona.

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Architectural Comparison Matrix

* Self-reported by the agent in run_log.json, not measured by the harness. "Median Model Turns" is measured from the transcripts and is 3 to 8 times the self-reported steps.

† The published verifier deleted any sandboxes still listed after a run and still passed, so it could not detect leaks. In Daytona Trial 4 it found and silently removed one leftover sandbox.

The prompts also differed: every Modal prompt spelled out the exact SDK calls, the E2B prompts were mostly plain language, and the Daytona prompts named the client constructor (Trial 4 also named the signed preview URL method). Treat the efficiency rows as indicative.

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1. Execution Model: Serverless Containers vs. MicroVMs vs. Workspaces

The primary differentiator between these three platforms is their underlying virtualization model:

Modal: Serverless Container Primitives

Modal treats compute as serverless functional steps running atop hardened gVisor containers. Instead of maintaining a persistent virtual machine, Modal allows you to define declarative images with pre-installed packages and invoke sandboxes quickly. In our trials it had the lowest self-reported median command time (19.5s) and the fewest measured model turns (12.5), with prompts that already contained the working calls.

E2B: LLM-First Ephemeral MicroVMs

E2B builds directly on Firecracker microVMs tailored specifically for language model tool-calling. Each sandbox exposes a minimal, streamlined interface for filesystem operations, process execution, and code execution cells. E2B does not pretend to be an interactive developer workstation—it is a secure, disposable interpreter for LLM reasoning steps.

Daytona: Full-Featured Autonomous Workspaces

Daytona approaches the sandbox problem from the perspective of software development environments. Rather than ephemeral code runners, Daytona spins up complete, stateful developer workspaces; in our trials they shipped with Python 3.14, git and zsh pre-installed. This makes Daytona a natural fit for complex multi-repo build systems and enterprise development stacks.

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2. Token Economics & Prompt Inflation

In an agentic loop, every token returned by the sandbox terminal is token budget deducted from the LLM’s context window. The token figures below were self-reported by the agent, and the transcripts contain no token-usage fields to check them against:

• E2B: lowest self-reported tokens: E2B reported 1,865 median tokens per task, the lowest in the cohort. On transcript-measured model turns, however, E2B had the highest median of the five providers (28.0).
• Modal: 2,010 median tokens self-reported, with the fewest measured turns (12.5).
• Daytona: 2,260 median tokens self-reported, 22.0 measured turns.

(Corrected 2026-10-12: the original post attributed these differences to E2B stripping terminal control sequences, Modal's pip progress bars and Daytona's verbose workspace logging. None of those causes was measured in this study.)

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3. Tool Turn Friction & Autonomy

When autonomous agents fail, they rarely fail because the underlying CPU crashed. They fail because an unexpected API behavior caused the agent to enter a retry loop:

• No human stops on any provider: each of the 30 transcripts has exactly one user turn. Daytona, E2B and Modal all ran unattended. (Corrected 2026-10-12: the original post presented zero stops as a Daytona and E2B distinction and gave Modal one operator stop.)
• Trial 6 is where they differed: E2B ran bottle's suite with pytest (363 passed). Daytona's agent wrote its own discovery runner (373 tests OK, 1 skipped). Modal's run printed only the completion marker with no test output, because test/test_all.py does not exist upstream and the solution printed the marker unconditionally, so it is counted as not passed.
• Modal’s ingress parameter: Modal requires the port to be declared at creation via encrypted_ports=[port] before sb.tunnels() returns it. Every Modal prompt supplied this call, so no agent hit the problem; the destroy-and-recreate stop described in the original post is not captured in the published transcripts.

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4. Teardown Security & Billing Protection

A major hazard of running autonomous agent swarms is zombie compute—microVM instances that remain active after an agent crashes or times out, silently running up cloud bills.

(Corrected 2026-10-12: the original post said all three platforms achieved 100% zero-leak compliance. That is not verifiable: the published verifier only called each provider's listing API, deleted anything it found, and still passed.)

What the trials do show about each teardown call:

• Modal’s sb.terminate() was used in the Modal solutions.
• E2B’s solutions used sandbox.kill() inside finally blocks.
• Daytona’s client.delete(sb) is asynchronous by default. In Trial 4 a sandbox was still listed right after it returned; the agent fixed this with client.delete(sb, wait=True). If your agent checks for leftovers right after teardown, wait for the delete to finish.

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The Verdict: When to Pick Which Platform

These are judgement calls informed by the trials, not results of a controlled comparison:

• Choose Modal if: You want fast container scheduling and clean port tunnels, you are comfortable declaring port requirements upfront, and you verify that your test step actually runs tests (Modal's Trial 6 run did not).
• Choose E2B if: You are building multi-turn LLM reasoning agents (e.g. via LangChain, Smolagents, or LlamaIndex), you want single-key authentication, and you want an agent-native microVM runtime. E2B had the lowest Integration Tax after correction.
• Choose Daytona if: Your agents must operate like real human software engineers across complex git repositories and stateful development workspaces, and you make deletes synchronous.

For deep-dive methodology, corrections and raw execution logs, read the full Glintbase Integration Index (Sandboxes Edition 1).