AI Agent Code Execution Sandboxes: E2B vs. Modal vs. Daytona

Most enterprise sandboxing was designed around one of two problems: multi-tenant SaaS isolation, where the code is known and versioned, or CI/CD pipelines, where a human merged the code that eventually runs. AI agent execution breaks both assumptions. An autonomous coding agent, a data analysis assistant, or a computer-use agent can generate a script at runtime, in response to a live prompt, and execute it immediately with no diff for anyone to read first.

That changes what the sandbox has to defend against. It is no longer just "contain a known workload"; it is "contain code whose intent you cannot verify in advance, that may attempt to read environment variables, reach internal network ranges, exhaust CPU or memory, or attempt a container or hypervisor escape." The blast radius of a single compromised or careless agent execution now includes anything reachable from the sandbox: cloud credentials, internal APIs, other tenants' data, and the host itself.

This is also why the isolation technology underneath the SDK matters more here than in most infrastructure decisions. A convenient API wrapped around weak isolation is a convenient way to expose your infrastructure to code you never read.

The rest of this guide walks through why these differences exist and which ones should actually change a buying decision.

The isolation layer is the part of this decision that is hardest to paper over with a good SDK, because it is the part that determines what happens when the untrusted code you cannot review turns out to be malicious.

E2B: Firecracker microVMs. Each E2B sandbox runs in its own Firecracker microVM with a dedicated guest kernel. Firecracker, originally built at AWS for Lambda and Fargate, uses hardware virtualization (KVM) to enforce the boundary between guest and host, rather than relying on the host kernel's namespace and cgroup mechanisms alone. A process that exploits a kernel bug inside an E2B sandbox is still trapped inside its own virtualized kernel, not the shared host kernel other tenants depend on. This is the strongest isolation boundary of the three by design, at the cost of losing the flexibility that comes with running directly against the host kernel (some kernel-dependent tooling behaves differently under a microVM guest kernel).

Modal: gVisor. Modal isolates sandboxes with gVisor, a user-space kernel developed by Google that intercepts and re-implements Linux syscalls rather than passing them straight to the host kernel. This narrows the attack surface presented to untrusted code without the overhead of a full virtual machine per sandbox, which is part of how Modal supports very high concurrency. The tradeoff is that gVisor's isolation guarantee is a software boundary (a hardened syscall interception layer) rather than the hardware-enforced boundary a microVM provides; gVisor has had escape-class vulnerabilities patched in the past, as has every isolation technology in this category, but the security model is meaningfully different from Firecracker's.

Daytona: OCI/Docker containers by default, Kata Containers as an option. Daytona's own documentation describes sandboxes as having "complete isolation, a dedicated kernel, filesystem, and network stack," but independent technical comparisons describe the default runtime as standard Docker/OCI containers, which share the host kernel via Linux namespaces and cgroups, with Kata Containers (which does add a lightweight VM boundary) available as an explicit configuration rather than the default. That is an important distinction for a threat model built around unreviewed AI-generated code: a shared-kernel container is a meaningfully weaker boundary against a kernel-level exploit than a microVM or a gVisor-style syscall interception layer, even though all three can look identical from the SDK call that spins one up. Buyers evaluating Daytona for this use case should confirm directly with Daytona which isolation mode a given plan actually provisions by default, rather than relying on the marketing description alone.

The practical takeaway: for a threat model where the code itself is the untrusted input, isolation technology is not a footnote, it is close to the whole decision. Firecracker and gVisor were both designed from the ground up to run untrusted code; a Docker container was not, and stronger container isolation modes need to be explicitly selected and verified, not assumed.

All three platforms expose sandboxing as an API call an agent framework can invoke mid-conversation, but the surrounding platform differs.

E2B ships Python and JavaScript/TypeScript SDKs plus CLI and SSH access, and includes a Jupyter-based code interpreter out of the box, which is a common integration point for agent frameworks that need to execute and return the output of generated code (LangChain, custom agent loops, and similar patterns). E2B's deployment model is managed SaaS, with a bring-your-own-cloud option on AWS available to enterprise customers.

Modal supports Python, JavaScript, and Go, and integrates sandbox execution alongside its broader serverless compute primitives, GPU-backed inference, and batch processing. For teams already running training or inference workloads on Modal, adding agent sandboxes to the same platform avoids standing up a second vendor relationship. Modal is managed-only; there is no self-hosted or BYOC option.

Daytona supports the widest language spread across its SDKs (TypeScript, Python, Ruby, Go, Java) and is built around persistent, resumable workspaces rather than purely ephemeral executions, which fits agent architectures that need a workspace to survive across multiple turns of a conversation. Daytona's deployment model changed materially in 2026: after moving its production codebase to closed source in June 2026, self-hosting the core platform is no longer available; enterprise customers get partial Kubernetes integration via Helm charts for runner infrastructure, but the control plane remains Daytona-hosted. Any evaluation that assumed Daytona's earlier open-source, self-hostable positioning should re-verify current terms directly with Daytona before treating self-hosting as an option.

None of these three require you to operate the underlying isolation infrastructure yourself; all three are managed services at their core, which is a meaningfully lower operational lift than running your own Firecracker or gVisor fleet. Where operational effort diverges is in what you have to build around the sandbox, not the sandbox itself.

E2B's session-scoped model (up to 24 hours on paid tiers) pushes state management back to the calling application: if an agent workflow needs results to persist beyond a session, that persistence has to be built outside the sandbox. Modal's idle-timeout-plus-snapshot model similarly requires the calling code to handle save/restore explicitly for anything long-running, though Modal's broader platform (queues, volumes, scheduled functions) gives teams already on Modal more native primitives to do that with. Daytona's persistent-workspace model reduces that burden for workflows that genuinely need a long-lived environment, but it shifts operational responsibility toward monitoring workspace sprawl and lifecycle, since "no forced session termination" also means nothing stops an idle workspace from running (and billing) indefinitely unless the calling application enforces its own cleanup policy.

Across all three, the recurring operational task security and platform teams should plan for is not infrastructure upkeep, it is monitoring: egress from sandboxes toward internal networks or unexpected external endpoints, resource ceilings per sandbox so a runaway agent loop cannot exhaust a shared quota, and credential scoping so that anything injected into a sandbox (API keys, service tokens) carries the minimum privilege the task actually needs.

All three vendors publish at least a partial pricing model; none require a sales call just to see a number, though enterprise tiers on all three are custom quotes.

E2B publishes a free Hobby tier (no credit card, includes free usage credits, up to 20 concurrent sandboxes, 1-hour sessions) and a Pro tier at $150 per month (customizable CPU/RAM, up to 24-hour sessions, 100 concurrent sandboxes, additional concurrency purchasable), on top of per-second usage pricing for CPU, RAM, and storage. Enterprise pricing is custom.

Modal publishes a free Starter tier (monthly free credits, 3 seats, 100 containers, 10 GPU concurrency) and a Team tier at $250 per month base plus compute, with per-second pricing published for CPU, memory, storage, and each GPU class it offers (from T4 up through B200). Enterprise pricing is custom, with volume discounts and startup/academic credit programs available.

Daytona publishes free signup credits and a pay-as-you-go compute rate, with volume discounts as usage scales and a startup credit program; enterprise pricing (including any BYOC or SSO/audit-log add-ons) is custom.

Third-party cost comparisons exist that model a fixed number of sandboxes against each vendor's published rate card and arrive at total-cost figures, but those figures depend heavily on assumed session duration, resource size, and concurrency, and at least one such comparison was published by a competing infrastructure vendor. Treat any specific total-cost-of-ownership number you see in a comparison blog as a modeled estimate built on that author's assumptions, not a quote, and run your own estimate against each vendor's published calculator using your actual expected session length and resource footprint before budgeting.

E2B. Strengths: hardware-level isolation via Firecracker that is well suited to a genuinely untrusted-code threat model; sub-second cold starts; reproducible, versioned sandbox templates; a code interpreter ready out of the box for data-analysis-style agent tasks. Limitations: no GPU support, so it is not a fit for agents that need to run model inference or training inside the sandbox itself; sessions are ephemeral by design, which means persistent state has to be engineered on top rather than relied on natively; a narrower platform scope than Modal if you need more than isolated execution.

Modal. Strengths: very high concurrency ceilings, broad GPU access without the quota friction some cloud providers impose, and a single platform that covers sandboxed execution alongside inference and training, which reduces vendor sprawl for AI-heavy teams. Limitations: gVisor's software-based isolation is a different, and by most technical assessments somewhat weaker, security boundary than a microVM for the specific threat model of executing code you cannot pre-vet; environments generated dynamically by an LLM are less reproducible than E2B's templated approach; no self-hosted or BYOC option.

Daytona. Strengths: broadest SDK language coverage of the three, fast cold starts, and a persistent-workspace model that fits agent workflows spanning multiple turns without re-provisioning. Limitations: default isolation is container-based with a shared host kernel, which is a materially different (weaker) boundary than Firecracker or gVisor unless stronger isolation is explicitly configured and confirmed; the June 2026 shift to a closed-source, cloud-only control plane removed self-hosting as an option, which matters for any team that had evaluated Daytona specifically for that reason; less GPU-focused than Modal.

E2B fits teams building agent products where the code being executed is the primary untrusted input and the isolation guarantee needs to hold up on paper in a security review, not just in practice: coding assistants, computer-use agents, and data-analysis copilots that run arbitrary, unreviewed scripts against customer or internal data. It is a strong default for small-to-mid engineering teams that want a dedicated sandboxing vendor rather than a general compute platform, and do not need GPU access inside the sandbox itself.

Modal fits teams that already run (or plan to run) inference, fine-tuning, or batch ML workloads and want agent code execution to live on the same platform rather than adding a second vendor. It is a better fit for larger teams or platforms with bursty, high-concurrency agent traffic and a budget that can absorb the $250/month Team tier base cost, especially where GPU access inside the execution path (not just for the agent's own model calls, but for code the agent generates that needs a GPU) is a real requirement.

Daytona fits teams building agent workflows that need a workspace to persist across many turns or a long session rather than a single, ephemeral execution, and that have already confirmed (directly with Daytona, not from marketing copy) which isolation mode their plan actually provisions. It is a reasonable fit for teams that value fast cold starts and broad SDK language support over the strongest possible isolation guarantee, and that are comfortable with a fully managed, closed-source control plane going forward.

A dedicated AI-agent sandboxing vendor is not always the right layer to buy. It makes sense to skip all three when the code your agents execute is not actually untrusted in the way this category assumes: if every code path an agent can trigger is a pre-approved, human-reviewed function call (tool use with a fixed, audited set of functions rather than free-form code generation), a standard hardened container or existing CI sandbox may be entirely sufficient, and adding a specialized vendor is unnecessary spend and integration surface.

It also makes sense to hold off if your team is early enough that agent-generated code execution is a prototype, not a production path: standing up a self-managed, locked-down Docker container with strict network egress rules and resource limits can validate the product idea without a recurring platform bill, and you can revisit a dedicated vendor once the workload and risk are real enough to justify it.

Finally, skip all three if your actual requirement is broader than sandboxed code execution, for example a full BYOC platform spanning databases, CI/CD, and multiple cloud isolation backends (Kata Containers, Cloud Hypervisor, gVisor) behind one control plane. That is a different product category, and evaluating it as a code-execution sandbox alone will understate both its cost and its fit.

Before committing budget or wiring a sandbox vendor into an agent's execution path, validate the following directly against the vendor, not against their marketing page: