Modal vs E2B vs Daytona vs Vercel Sandbox: AI Code Execution

Why Agent-Generated Code Needs a Sandbox: The Threat Model

Start with the threat model, because it determines everything downstream. When a model writes code and your system executes it, three concrete attack classes open up.

Remote code execution. This is the obvious one and also the one teams underrate. If your agent runs generated code in-process, an attacker who can influence the prompt can influence the code. Prompt injection is not theoretical; it is the dominant attack surface for agentic systems. A poisoned document, a malicious tool result, or a crafted user message can all steer the model into emitting code you never intended to run. Once that code runs in your runtime, the attacker has your runtime.

Data exfiltration. Even code that is not trying to crash anything can quietly read os.environ, grab your database credentials or API keys, and POST them to an external host. This is the attack that survives naive sandboxing, because process isolation does not stop outbound network calls. A sandbox that isolates CPU and memory but allows arbitrary egress is a sandbox that exfiltrates data politely.

Privilege escalation. Generated code that can write to the host filesystem, mount volumes, or reach the container runtime can attempt to break out of its boundary. Hardware-virtualized microVMs (Firecracker, which E2B uses) raise the bar here dramatically compared to shared-kernel containers, because the guest kernel is genuinely separate.

The mitigation hierarchy is straightforward. Never execute model output in your application process. Run it in a disposable, isolated sandbox. Then, critically, restrict what that sandbox can reach. The first two steps are what these four products sell. The third step is on you, and we will get to it. For the adjacent surface where most agent breaches originate, see the MCP server security hardening checklist.

The Four Bets: What Each Platform Is Actually For

These four products are not four flavors of the same thing. They are four different architectural bets, and the bet determines the workload each one fits.

E2B is the code-execution-first specialist. It exists to run untrusted, agent-generated code in Firecracker microVMs. The whole product is shaped around that: fast sandbox creation, strong process isolation, SDKs that make "create sandbox, run this code, read the result, destroy sandbox" a three-line operation. If your workload is a code interpreter, a data-analysis agent, or a system that runs generated scripts and reads back stdout, E2B is purpose-built for it. Cold start sits around 150ms.

Daytona is the latency-optimized execution environment. Daytona's headline number is the roughly 90ms cold start, the fastest of the four in 2026 third-party benchmarks. That speed is the bet. For agents that create and destroy a sandbox on every single tool call, the per-call latency is the dominant cost, and 60ms of savings per call compounds across thousands of executions. Daytona prices at the same roughly $0.0504 per vCPU-hour as E2B, so on raw compute they are a tie and you choose Daytona specifically when latency is the constraint.

Modal is the serverless compute platform with a sandbox primitive, and the only one with GPU-in-sandbox. Modal did not start as a sandbox company; it is a broad serverless compute platform that added a sandbox API. Its defining, genuinely unique capability is running a GPU inside the isolated sandbox. No other option here does that. If your agent-generated code needs to fine-tune a model, run batch inference, or do anything GPU-bound inside the secure boundary, Modal is the answer and the others are not. Modal sandboxes default to a 5-minute lifetime, configurable up to 24 hours, which fits longer-running GPU jobs better than ephemeral per-call patterns. The GPU economics carry roughly a 3x pricing multiplier, which is expected for GPU instances.

Vercel Sandbox is the ecosystem-native option. It is the tight fit when your agent already lives in a Next.js app deployed on Vercel. The bet is integration, not raw flexibility: one platform, one bill, one auth model, no second vendor to operate. For a team whose entire stack is Vercel, that operational simplicity is worth a lot, and it is the pragmatic default for the Next.js-native builder.

These bets are not converging. E2B is doubling down on isolation, Daytona on cold-start speed, Modal on GPU and broader compute, Vercel on ecosystem fit. Pick the bet that matches your workload's binding constraint.

Cold-Start Latency: The Number That Actually Compounds

Cold start is the time from your sandbox-create call to a ready-to-execute environment. It matters more than almost any other metric for one specific but extremely common pattern: an agent that spins up a fresh sandbox per tool call and tears it down after.

The numbers come from third-party 2026 benchmarks (Superagent and Northflank published comparisons in the category). Treat any single benchmark with healthy skepticism: cold start varies with sandbox image size, region, warm-pool configuration, and how the vendor counts "ready." Run your own benchmark with your actual sandbox image before you commit, because a fat custom image can erase a vendor's headline advantage.

Why the compounding matters: if your agent makes 5,000 sandbox-creating tool calls a day, the difference between 90ms and 150ms is 5 minutes of pure latency per day, and that is latency the user feels on every step. For a batch job that creates one long-lived sandbox and runs everything inside it, cold start is a one-time cost and effectively irrelevant. So the honest framing is: cold start dominates if you create sandboxes frequently, and it barely matters if you create them rarely. Know which pattern you are in before you let the 90ms-vs-150ms number drive the decision.

Cloudflare has separately claimed a "100x faster sandboxing" result with its Dynamic Workers approach. That is a real and interesting claim, but it is not a like-for-like comparison: Dynamic Workers use a lighter V8-isolate model rather than full microVMs, with different security boundaries and compatibility constraints. If you are running highly untrusted code, the isolation model is part of the decision, not just the speed.

Pricing: Where the Real Money Is and Isn't

The pricing headline is that Daytona and E2B are at near-parity around $0.0504 per vCPU-hour, which means on raw CPU compute the cost decision between those two is a tie. Stop comparing their per-vCPU rates and decide on latency and isolation instead.

A few patterns hold:

The cost that nobody budgets for is idle time. A sandbox left running because the agent crashed before closing it, or because nobody set a lifetime, bills the entire time it is up. Modal's 5-minute default lifetime is a sensible guardrail precisely because forgotten sandboxes are the most common surprise on the invoice. Whatever platform you choose, set an explicit maximum lifetime and close sandboxes in a finally block so a thrown exception never leaks a running environment. This is the sandbox-tier equivalent of the runaway-loop problem that drives the cost spikes we see in agentic systems.

GPU Inside the Sandbox: The Modal-Only Capability

This section is short because the answer is binary. If your agent-generated code needs a GPU, Modal is the only one of these four that runs a GPU inside the sandbox. Full stop.

The use cases are real and growing: an agent that fine-tunes a small model on an uploaded dataset, an agent that runs inference over a batch of images, a research agent that executes GPU-accelerated numerical code. With E2B, Daytona, or Vercel Sandbox, the sandboxed code would have to call out to a separate GPU service over the network, which adds a hop and, worse, breaks the isolation boundary. Routing GPU work to an external endpoint with credentials defeats the whole point of keeping untrusted code inside one secure boundary.

Modal keeps the GPU inside the boundary. The agent's generated code runs directly on an attached GPU within the same isolated sandbox, end to end. The tradeoffs are the roughly 3x GPU pricing multiplier and Modal's broader platform surface area, which is more to learn than E2B's deliberately narrow code-execution API. If GPU-in-sandbox is a hard requirement, those tradeoffs are simply the cost of the only product that meets it. If it is not a requirement, Modal's GPU capability is not a reason to choose it over a faster or simpler option.

Decision Matrix: Which Sandbox for Which Workload

Two questions decide this: what is your binding constraint (latency, isolation, GPU, ecosystem), and what is your execution pattern (frequent ephemeral sandboxes versus long-lived ones).

A few patterns worth flagging:

If your agent design also involves running many sandboxes concurrently, for example a fleet of coding agents each in its own environment, the orchestration pattern matters as much as the sandbox choice. Our writeup on the oh-my-codex worktree pattern for parallel coding agents covers how to run agents in parallel without them stepping on each other, which pairs directly with per-agent sandbox isolation.

The Capability-Permissions Layer Everyone Forgets

Here is the part the benchmarks ignore and the part that actually gets teams breached. The sandbox isolates the process. It does not, by default, restrict what that process can reach. Isolation is not least privilege, and conflating the two is the most expensive mistake in this category.

Picture an analysis agent in a perfectly isolated Firecracker microVM. The code cannot escape to the host, cannot read your application memory, cannot touch the container runtime. It can also make an outbound HTTPS call to any host on the internet, and you handed it the dataset it was asked to analyze. A prompt-injected version of that code reads the data and POSTs it to an attacker-controlled endpoint. The microVM did its job perfectly. You still got exfiltrated.

The capability-permissions layer is the allowlist that closes this. Four controls matter:

All four platforms give you some of these controls; how much, and how ergonomically, varies and is worth testing directly during evaluation. But the controls are inert until you configure them. The default posture of "isolate the process, allow everything else" is what ships, and it is not enough. This is the same least-privilege discipline that governs the rest of an agent's access surface, and the question of whether to run any of this inside your own perimeter or in a vendor's cloud connects to the broader cloud vs on-premise AI security and cost tradeoff that regulated teams have to settle before they pick a vendor.

At Particula Tech, when we design agent code-execution into a system, the sandbox vendor is usually the easy half of the decision. The hard, load-bearing half is the capability layer: getting egress allowlists, credential scoping, and resource limits right against the actual tasks the agent runs, so that even a fully prompt-injected agent cannot do meaningful damage. The vendor isolates the process; the architecture decides whether the process can hurt you.

Recommendation by Scenario

Closing the loop with concrete starting points. Every workload has wrinkles, but these hold up most often:

Pick the platform whose binding constraint matches yours: Daytona for latency, E2B for isolation, Modal for GPU, Vercel Sandbox for ecosystem fit. Then treat the sandbox as the first layer of defense, not the whole defense, and spend the rest of your effort on the permissions layer the benchmarks never measure. The strategic context for where managed execution infrastructure pays off sits in our AI Development Tools pillar.