E2B vs Modal vs Daytona: AI Agent Code Execution Sandboxes in Production (2026)

Six months ago I shipped ContentForge AI Studio, one of the AI-powered tools we run at Warung Digital Teknologi. ContentForge generates marketing collateral and one of its agents writes Python data-cleaning scripts on the fly, then executes them against client CSVs. The first version ran that code in a thread inside our Laravel API. It took exactly one client uploading a malformed pickle file with a __reduce__ payload before I ripped that out at 2 a.m. and started shopping for a real sandbox.

That hunt is the reason I have opinions about E2B, Modal Sandboxes, and Daytona. I have run all three in production for parts of our AI stack — ContentForge for code generation, ServiceBot AI Helpdesk for safe tool calls, and DocSumm for PDF parsing pipelines. This guide is the comparison I wish someone had handed me before I burned a weekend on cold-start benchmarks.

Why AI Agent Code Execution Sandboxes Matter in 2026

If you ship anything that lets an LLM emit code and you run it on your own infrastructure, you are one prompt injection away from a compromised box. The 2025 wave of agent frameworks (LangGraph, CrewAI, Pydantic AI, Google ADK) shipped great planners but most teams still glue them to subprocess.run or a Docker socket and call it a day. That works until the day a customer types "Fetch this URL and parse it with eval" into your chat.

A code execution sandbox solves three problems at once: hardware or kernel-level isolation so untrusted Python cannot escape, fast cold starts so your agent UX is not stuck behind a 30-second VM boot, and a managed runtime so you do not personally babysit Firecracker. The category exists because none of those are problems a typical Laravel or FastAPI team should solve in-house. I have written enough cgroup configs in my life. I would rather pay $0.05 per agent-hour and sleep.

The three platforms below are the ones I actually evaluated in March 2026 when ContentForge needed to scale past 50 concurrent agent sessions. Each takes a meaningfully different architectural bet.

The Three Contenders, In One Glance

The three numbers I weight most when picking are cold-start latency, isolation strength, and whether I need a GPU inside the sandbox itself. If you want to skip the deep dives and just decide, the short version is: E2B for security-first untrusted code, Modal for ML/GPU agents, Daytona for long-running persistent agent workspaces. The rest of this article is why.

E2B Deep Dive: Firecracker microVMs as a Service

E2B is the platform I default to when an agent is going to run code I do not trust at all. That includes any code an LLM emits without human review, anything user-supplied that hits an interpreter, and anything that touches the filesystem. The reason is simple: each E2B sandbox is a Firecracker microVM, the same hypervisor AWS uses for Lambda. You get a dedicated kernel per session and hardware-level isolation. A guest escape would have to chain through KVM itself, which is a class of exploit I have never seen on a production engagement.

What Pricing Actually Looks Like

E2B is per-second billing on top of a plan fee. The Hobby plan is free with a one-time $100 credit and 20 concurrent sandboxes — that is genuinely enough to ship a side project to production. Pro is $150/month and lifts session caps to 24 hours. A 1 vCPU, 1 GiB sandbox costs about $0.05/hr while running. RAM is included in the CPU price, which is the kind of pricing detail that matters when you read your invoice.

For ContentForge I budgeted around 4 minutes of sandbox time per agent task. At ~$0.003 per task we pay roughly $90/month for our current volume of 30,000 tasks, including the Pro plan. That is cheap enough that I stopped trying to optimize it.

What I Actually Like

• SDK feel. The Python SDK is the most ergonomic of the three. sandbox = Sandbox(); sandbox.run_code("print('hi')") works on the first try. Streaming stdout, file mounts, and a remote filesystem API are all one method away.
• Cold start under 200 ms. Fast enough that I do not bother pre-warming pools for chat-style UX.
• 20 concurrent on the free tier means you can demo and load-test before your CFO notices.

What Bit Me

No GPU support. If your agent needs to run a transformer inference inside the sandbox itself — for example, to score a generated piece of content — you cannot do it on E2B. You either call a remote inference API (which adds latency and another vendor) or pick Modal. For ContentForge that meant our scoring step now lives outside the sandbox, which is a clean separation but adds a network hop.

Also: 24-hour max session length on Pro. For long-lived dev-environment-style agents, this is a hard ceiling. We use ServiceBot for short tool calls so it has not bitten us, but it would be a problem for a coding agent that needs to keep state across a multi-day refactor.

Modal Sandboxes Deep Dive: gVisor + GPUs

Modal raised an $87M Series B in early 2026 at a $1.1B valuation, and the sandbox product is where they are pulling ahead. Modal Sandboxes use gVisor — a user-space syscall filter, not a hypervisor — and they autoscale aggressively. The headline feature, the one that pulls me toward Modal whenever the agent needs ML inside the box, is GPU access. Modal Sandboxes can attach an A100 or H100 directly to the sandboxed environment.

Pricing Reality (Read This Carefully)

Modal advertises a base CPU rate around $0.0000131 per CPU core per second. The catch is the multipliers nobody mentions on the landing page. Sandbox compute carries a 3x premium over standard Modal Functions because gVisor isolation is more expensive to run. Then there is a 1.25x US regional multiplier and a 3x non-preemptible multiplier for production workloads.

What that means in practice: a 5-minute sandbox using 1 core and 1 GiB costs about $0.014 — fine. But for steady production load of 10,000 CPU-hours per month, the advertised base cost of $471.60 lands at roughly $1,768.50 once you factor in the multipliers. I learned this the slow way watching our DocSumm GPU bill creep upward and pulling the breakdown myself. Do not benchmark Modal pricing on the marketing page; benchmark on a one-week production trace and multiply.

GPU sandboxes are where Modal becomes the only real option. An A100 40GB sandbox runs around $3.73/hour. An H100 sandbox is more. That is not cheap, but for an agent that needs to run an embedding model or a reranker inside the isolated execution context, there is currently no comparable competitor.

What I Actually Like

• Native Python autoscaling. Define a sandbox in a decorator, and Modal handles concurrency to 50,000+ sessions without the operator paging you.
• GPU attach. One line of config and your sandbox has an A100. This is genuinely a category killer for ML agents.
• Sub-1-second cold starts for warm-pool sandboxes. Cold-cold starts are slower but predictable.

What Bit Me

gVisor is weaker than Firecracker for adversarial untrusted code. It is good enough for the average LLM agent, but I would not run code from anonymous users on the internet through it without additional rate limiting. Whitelist the use case to "code my own LLM emitted" or "code my authenticated customer uploaded" and you are fine.

Also: no BYOC, no on-prem, no self-hosted. If you have compliance requirements that keep workloads in your own VPC, Modal is currently a non-starter. We use it only for non-PII workloads at Warung Digital because of this.

Daytona Deep Dive: The Persistent Agent Workspace Bet

Daytona comes from a different lineage than E2B and Modal. It started as a developer workspace product — think Coder or Gitpod — and has pivoted hard into AI agent infrastructure. The product is built around Docker containers, OCI compatibility, and persistent disk. After raising a $24M Series A in February 2026 led by FirstMark Capital, they are scaling specifically to serve coding agents that need long-lived state.

The Speed Pitch

Daytona's marketing claim is sub-90ms sandbox creation, and in my benchmarks it actually held up: I measured a median of 73ms from API call to ready-state on their us-east region. That is roughly 2-3x faster than E2B's cold start. For a chat-style agent UX where the user is staring at a "thinking..." spinner, this matters more than the per-second pricing difference.

Pricing

Daytona includes $200 in free compute on signup, and startups can apply for up to $50k in credits. Usage-based pricing lands at $0.0504 per vCPU-hour plus $0.0162 per GiB-hour, which works out to roughly $0.083/hr for a 1 vCPU + 2 GiB sandbox while running. That is more expensive than E2B per-hour but cheaper than Modal once you factor in Modal's multipliers.

What I Actually Like

• Persistent storage by default. Sandboxes can keep state across runs. For our ServiceBot helpdesk agent, this means it can build up a shared cache of customer-specific context without rebuilding every invocation.
• Docker compatibility. If you already have a Dockerfile for your agent runtime, Daytona ingests it directly. No proprietary image format.
• Sub-90ms provisioning. Best-in-class for "user clicks button, agent runs code" interactions.

What Bit Me

Container isolation is weaker than microVM isolation. Daytona uses gVisor or kata-containers under the hood depending on tier, but the shared-kernel model is a known weakness for adversarial workloads. For our use cases this is fine — I trust the code our own LLMs emit through ContentForge — but I would not put Daytona in front of a public "run any Python you want" form.

GPU support is improving but not the headline like Modal's. As of May 2026, GPU-backed sandboxes are listed but in limited regions. If GPU is your primary need, default to Modal.

Real Numbers from Production

Across our 6 AI products at Warung Digital we have logged roughly 380,000 sandbox invocations in the last 90 days. Three patterns I can share that are not in any vendor blog:

1. Cold-start variance matters more than median. E2B's median is ~180ms but the p99 was 740ms during a regional outage in March. Daytona's p99 was 220ms during the same window. If your UX has a 1-second budget for "agent acknowledged the request", build around p99 not median.
2. The pricing winner depends on session shape. For short tasks under 30 seconds, E2B's per-second billing wins. For long agent sessions over 5 minutes, Daytona's flat hourly rate is cheaper per task. We split traffic accordingly: ContentForge code-gen on E2B, ServiceBot multi-turn on Daytona.
3. Egress is the sleeper cost. All three platforms charge bandwidth separately. For DocSumm parsing 50MB PDFs on average, egress was 18% of the total bill last month. Compress before download.

Decision Matrix: Pick the Right One

If you are starting from scratch, run through this checklist in order. Stop at the first match.

• Do you need a GPU inside the sandbox? → Modal Sandboxes. There is no second choice in 2026.
• Are you running code from completely untrusted sources (public API, anonymous users)? → E2B. The microVM isolation is worth the GPU tradeoff.
• Do your agents need persistent state across sessions and you want sub-100ms provisioning? → Daytona. The dev-workspace heritage shines here.
• Do you need BYOC or on-prem for compliance? → E2B Enterprise (the only one with a self-hosted option) or roll your own with Firecracker.
• Are you cost-sensitive and your code-gen is short-lived? → E2B per-second billing.
• Do you already have a Docker-based agent runtime? → Daytona ingests Dockerfiles natively, less migration work.

Hidden Costs and Gotchas

A few things I wish someone had told me before I signed contracts:

Concurrency limits. All three platforms cap concurrent sandboxes per account. E2B's free tier is 20, Modal scales but gets expensive past 100, Daytona's quota varies by plan. Hit your cap and your agent UX starts queueing — which feels like an outage to your users. Negotiate this number into your enterprise contract.

Sandbox warm pools. Cold starts are advertised as the worst case. In production, you almost always pre-warm. Pre-warming costs money. Budget for at least 2-3x your steady-state concurrency in warm pool capacity if you want consistent latency.

Logs and observability. None of the three platforms ship great built-in observability. We pipe sandbox stdout/stderr to Langfuse and metrics to our own Prometheus stack. Plan for a logging budget.

Vendor lock-in via SDK. All three SDKs are different enough that switching is a 1-2 week project. Wrap them in your own thin client from day one if you anticipate multi-vendor.

Frequently Asked Questions

Can I just use Docker on my own VPS instead?

You can, and many teams do for the first 6 months. The break-point in my experience is when you hit 5+ concurrent agent sessions and need to actually think about isolation, autoscaling, and cleanup. At that point you are reinventing what these vendors sell, and your time is more expensive than $0.05/hr.

What about Vercel Sandboxes or Cloudflare Workers as alternatives?

Vercel Sandbox is a credible E2B alternative if you are already on Vercel and want one bill. Cloudflare Workers are a different category — V8 isolates, not microVMs — and are too restrictive for general Python execution. Use them for trusted code only.

Is Firecracker really more secure than gVisor in practice?

For known-untrusted code, yes. The attack surface to escape Firecracker is dramatically smaller than gVisor. For LLM-emitted code that you have at least minimal control over (your prompt, your model), gVisor is usually enough.

How do I handle secrets inside a sandbox?

Inject them at runtime via the SDK, never bake them into the image. All three platforms support env-var injection and ephemeral mounts. Rotate aggressively — assume the sandbox is one prompt-injection away from leaking whatever it can read.

Which one ships fastest from zero?

E2B. The Python SDK plus the Hobby tier means you can have a working sandbox-backed agent in under an hour. Modal is close behind if you are already a Modal user. Daytona has the steepest onboarding because the workspace concepts take time to internalize.

Verdict

If I were starting a new AI agent project in May 2026 with no prior commitments, I would default to E2B for the first six months. The SDK is the most pleasant, the security story is unmatched, and the pricing is forgiving while you find product-market fit. I would migrate to Modal the day I needed a GPU inside the sandbox, and to Daytona the day I needed multi-day persistent agent workspaces.

The mistake I made in February 2026 was running ContentForge in a thread for too long because I underestimated the attack surface. Do not repeat that. Pick a sandbox vendor on day one. The cheapest of the three options is still cheaper than one incident response.

For deeper context on the agent frameworks that pair well with these sandboxes, see my comparison of LangGraph vs CrewAI vs AutoGen and the PydanticAI migration writeup from last month.