Build vs Buy:
AI Agent Infrastructure in 2026

The decision every team faces

Your engineering team wants AI agents. The agents need compute: sandboxes for code execution, browsers for web automation, desktops for GUI applications. The question is where that compute comes from.

Three options:

1. Build it yourself on AWS/GCP with EC2, cloud containers, and custom orchestration.
2. Use a sandbox-as-a-service platform (E2B, Modal, Daytona, Runloop, or us).
3. Use the agent's built-in compute (Devin's sandbox, Codex's containers, Cursor's cloud VMs).

Each has tradeoffs. This guide is honest about all three — including where we're not the right choice.

Option 1: Build it yourself

What it takes

Building AI agent infrastructure from scratch means building a sandbox orchestration layer. Here's the bill of materials:

Plus ongoing maintenance: security patches, AWS API changes, scaling issues, on-call. Expect 0.5–1 FTE of permanent platform engineering time.

When to build

• You're a platform company and agent infrastructure is your product (you're building the next E2B).
• You have strict data residency requirements that no managed platform can meet (government, defense, certain healthcare).
• You already have a container orchestration team and the marginal effort to add sandbox capabilities is small.
• You need capabilities no platform offers (custom hardware, bare-metal GPU, exotic network topologies).

When not to build

If you're building agent infrastructure so your team can run Claude Code on cloud VMs, you're spending 6 months and $200K+ in engineering time to solve a problem that a managed platform solves in an afternoon. The opportunity cost — 6 months of your platform team not building your actual product — is the real expense.

Option 2: The managed platform landscape

The sandbox-as-a-service market has grown fast since 2024. Here's the honest comparison as of March 2026:

Where each platform wins

E2B

Best-in-class for pure code execution sandboxes. Fastest cold start in the market (~300ms for their Firecracker-based VMs). Strong Python/TypeScript SDK. If your agents only need to execute code in a terminal and you don't need browsers or desktops, E2B is a strong choice. Raised $14.4M, well-funded, growing fast.

Modal

Best for ML/GPU workloads. Native GPU scheduling, container image caching, and a Python-first SDK that makes serverless GPU feel native. If your primary use case is ML inference or training, Modal is purpose-built for it. Not designed for interactive agent workloads (no SSH, no browser, no desktop).

Daytona

Open-source, self-hostable dev environments. Best if you want to run the platform on your own infrastructure with full control. Devcontainer-compatible. Doesn't have browser or desktop containers — it's focused on coding environments.

Fly.io

General-purpose container hosting with the Machines API for programmatic lifecycle control. Cheapest option for always-on VMs. You can build browser/desktop support yourself using their containers, but it's DIY. Best if you want low-level control and don't mind building the agent-specific layer.

Runloop

AI-agent-specific sandbox platform. Similar to E2B but with persistent storage and a focus on long-running agent tasks. Browser support in beta. Good Python SDK.

Where we win

We're the only platform that gives the agent the complete workstation: terminal + browser + desktop. If your agents need to:

• Execute code AND browse the web AND operate GUI applications
• Run 50 Chrome instances in parallel with CDP control
• Open LibreOffice on a desktop and interact via computer-use vision
• Use warm pools for instant browser/desktop claiming
• Track costs per department with spending caps

…then no other platform covers all of these today. If your agents only need terminal execution, E2B or Modal may be simpler and cheaper for that specific use case.

Option 3: Use the agent's built-in compute

Some agents bundle their own compute:

The tradeoff is clear: bundled compute is convenient but creates lock-in. You can't run Claude on Devin's sandbox. You can't use Codex's containers with a LangChain agent. Each tool built its own walled garden. A managed platform gives you any agent on any compute.

The decision matrix

The real cost of building

The direct cost (engineering time) is the visible part. The invisible cost is opportunity: your platform team spent 6 months building sandbox infrastructure instead of building your product. That's the cost that matters most for startups and growth-stage companies.

What's next