Build vs Buy:
AI Agent Infrastructure in 2026

Three paths, three tradeoffs

Every company deploying AI agents faces the same infrastructure decision. The agents need somewhere to run — isolated compute for code execution, browsers for web automation, desktops for GUI applications. Three options exist, and each optimizes for a different priority:

The managed platform market: who's who

Six funded startups are competing to be the "AWS for AI agents." Each started with a different wedge and is expanding toward the same vision. Here's the honest landscape:

E2B — the code execution specialist

Raised: $14.4M. Focus: Firecracker-based code execution sandboxes with ~300ms cold start. Best at: pure code execution, fastest startup time in the market, excellent Python/TypeScript SDKs. Doesn't have: browser containers, desktop containers, persistent storage (beyond session). Best for: coding agents that only need a terminal.

Modal — the ML infrastructure play

Raised: $113M. Focus: Serverless compute with native GPU scheduling. Best at: ML inference, batch processing, container image caching, Python-first developer experience. Doesn't have: browser containers, desktop containers, SSH access, interactive terminals. Best for: data science and ML workloads, not interactive agent tasks.

Daytona — the open-source dev environment

Raised: $5M. Focus: Devcontainer-compatible development workspaces, self-hostable. Best at: open-source, full control, standardized dev environments. Doesn't have: browser containers, desktop containers, warm pools. Best for: teams that want to self-host on their own infrastructure.

Fly.io — the general-purpose compute layer

Raised: $120M+. Focus: Global app deployment with the Machines API for programmable VM lifecycle. Best at: always-on VMs, global edge compute, cheapest per-hour pricing. Doesn't have: browser/desktop containers (build-it-yourself), warm pools, agent-specific features. Best for: teams that want raw VMs and are willing to build the agent layer themselves.

Runloop — the AI agent sandbox

Focus: AI-agent-specific sandboxes with persistent storage. Best at: long-running agent tasks, persistent workspaces, good Python SDK. Status: browser support in beta. No desktop containers. Best for: coding agents that need persistence.

Sandbox Platform (us) — the full workstation

Focus: Terminal + browser + desktop. The complete AI employee workstation. Best at: the combination — 6 browser types, 4 desktop types, EC2 sandboxes, warm pools, cost tracking, multi-tenancy. Tradeoff: EC2-based sandboxes have slower cold start than Firecracker-based E2B. Best for: teams that need agents operating web portals, GUI applications, AND executing code.

The honest feature comparison

The real cost of building it yourself

Every infrastructure team considers "just building it on AWS." Here's the honest assessment of what that entails.

A basic sandbox (launch an EC2 instance, run a command, return output) takes 2–3 weeks. It feels fast. You have a demo. Leadership is impressed.

Then the requirements pile up:

• "We need browser containers for web automation." — 4–6 weeks (cloud containers + CDP + proxy routing).
• "We need desktop containers for ERP automation." — 4–6 weeks (VNC + noVNC + display server + session management).
• "We need warm pools so browsers start instantly." — 3–4 weeks (state machine, health recycling, claim/release).
• "Finance wants per-department cost tracking." — 2–3 weeks.
• "Security wants egress control and audit logging." — 3–4 weeks.
• "We need multi-tenancy for the new team." — 3–4 weeks.
• "The proxy routing keeps breaking." — ongoing.

Total: 6–10 months, $200K+ in engineering time, and a permanent 0.5–1 FTE maintenance burden.

The bundled compute trap

Devin, Codex, and Cursor each bundle their own compute. It's convenient — the sandbox comes with the agent. But it creates lock-in:

• Devin's sandbox only runs Devin. If you want to switch to Claude Code or a custom LangChain agent, you need new infrastructure.
• Codex's containers only run GPT. You can't use Claude, Gemini, or open-source models in Codex's sandbox.
• Cursor's VMs only serve Cursor. Your ops team can't use them for AP automation or competitive intelligence.

A managed platform decouples the compute from the agent. Run Claude Code, Codex CLI, Aider, a LangChain agent, or a custom script — all on the same infrastructure. Switch models without switching infrastructure. Use the same sandbox for coding agents and business automation agents.

When to choose what

The question that decides it

The build-vs-buy decision comes down to one question: is sandbox infrastructure your product, or is it infrastructure for your product?

If sandbox infrastructure IS your product (you're building the next E2B, the next Devin, the next Cursor), build it. You need the control, the customization, and the deep understanding of the stack.

If sandbox infrastructure is infrastructure FOR your product (you need agents to run somewhere so they can do useful work), buy it. Every month spent building infrastructure is a month not spent deploying agents and capturing value. The platform costs $30/month for your first 7 agents. The build costs $200K before you deploy a single agent.

That's not a close call.

What's next