Where to Run Your AI Agents in 2026

Your agent needs somewhere to live.

Not just a place to run code for a few seconds. A real place: files that survive between sessions, tools that stay installed, credentials it can reach, browser sessions it can resume. A place that remembers what your agent did last time and shows up the same way next time.

In 2023, that place was a VPS you configured yourself and hoped wouldn't drift. In 2024, a handful of sandbox APIs made the easy case easier. In 2026, you have actual choices. The market has split into distinct products solving distinct layers of the problem, and picking the wrong one means rebuilding sooner than you'd like.

Here is an honest look at what those choices are, what each one does well, and which fits which kind of team.

First: three types of product in this space

Before picking one, it helps to know that "sandbox" means different things across this category. Three distinct product shapes exist right now.

Sandboxes give you an isolated environment to run code in. Fast to spin up, gone when the task ends. Good for executing untrusted code or running a single tool call without worrying about what it touches. E2B, Vercel Sandbox, and Cloudflare Sandbox work this way. They are good at it.

Environment platforms give you something that lasts. The files survive. The packages stay installed. The agent picks up where it left off. Sprites, OpenComputer, and Daytona sit here. More persistent, more opinionated about what the environment looks like between runs.

Runtime layers add a control plane above the environment. The Spinup Agent is a durable, workspace-owned object, not just a process inside a machine. Swap the harness running inside without rebuilding the environment around it. Manage credentials and policies at the agent level instead of per-tool. Spinup is building here.

Most teams need the second or third type. They often start with the first because that is what they know.

E2B

E2B is the easiest starting point if you need a clean, isolated place to run code fast. Their API is well-designed. Sandboxes start in milliseconds. 500 million sandboxes have started through their infrastructure, and 88% of Fortune 100 companies use it. Those numbers tell you it works at scale.

Where it stops: sandboxes are short-lived by default. State does not carry over between runs unless you engineer around it. If your agent is doing a single task (run this code, return the output), E2B is hard to beat. If your agent is doing a job that spans hours or days, you are wiring persistence together yourself.

Best for: code execution, data analysis, one-shot agent tasks, LLM apps that need a safe place to run untrusted code.

Sprites

Sprites describes its product as "a persistent Linux computer" for your code. That is closer to what most agent workloads actually need. Files live on a real ext4 filesystem. Packages stay installed. You can checkpoint the environment and restore it later. The company's own framing is refreshingly direct: "the simplest answer for where to run a blob of code."

Hardware isolation uses Firecracker microVMs, billing stops when a Sprite is paused, and the product supports up to 100 GB of persistent storage. The product is opinionated about being simple, which is a genuine advantage over assembling infrastructure yourself.

Where it stops: Sprites is an environment, not a runtime layer above it. It gives your agent a machine. It does not manage what runs inside, how multiple harnesses share that environment, or how secrets and policies apply across a team of agents. Those remain your problem.

Best for: individual developers and small teams running persistent agent workloads, prototypes that need a real environment without building a full platform.

OpenComputer

OpenComputer's tagline is "long-running cloud infrastructure for AI agents," which is close enough to several other products in this list that the differences matter. The product is a full Linux VM with checkpoint, fork, and elastic compute semantics. Fork from a checkpoint to run five approaches in parallel. Adjust memory and CPU while the agent is running. The abstraction is the VM, which gives you a lot of power and a lot of responsibility.

OpenComputer gives you the machine. The agent runtime that sits on top of it, that manages which harness is running and how its configuration persists, is still yours to build.

Best for: teams that need VM-level control, want fork-and-test parallelism, or are building their own runtime layer on top of solid VM infrastructure.

Daytona

Daytona calls itself "more than a sandbox, the runtime AI agents actually need." The product covers isolated environments, snapshots, file operations, and customer-managed compute with enterprise compliance baked in (HIPAA, SOC 2, GDPR). Sub-90 ms sandbox creation and regional deployment are the headline technical claims.

The honest read: Daytona is environment infrastructure with good architecture concepts. It has the building blocks. The agent control plane (the part that manages agent identity, which harness is running, and how configuration moves between runs) is still up to you. Teams with engineering resources who want a solid foundation to build on will find it credible.

Best for: compliance-sensitive teams, engineering-heavy teams who want infrastructure they control, teams building agent platforms on top of a managed compute layer.

Factory / Droid Computers

Factory stakes one of the loudest positioning claims in this space: "persistence is the new paradigm for cloud agents." They are right about that. Their Droid Computers give Factory's own agents, called Droids, persistent machines that pause when idle and resume from snapshots. The filesystem, credentials, and process memory all survive between sessions.

The catch: Droid Computers are for Droids. Factory is a vertically integrated product. If you are using Claude Code, Codex CLI, OpenClaw, or any other harness outside the Factory ecosystem, this is not your product. The persistence model is solid. The ecosystem is closed.

Best for: teams already using Factory's Droids who want managed persistence without infrastructure work.

Spinup

Spinup is where we are honest about where we fit, because we built it.

The problem we kept running into: every time you switch harnesses, you rebuild the environment from scratch. New installs, new config, credentials wired up again, the whole setup repeated. The agent's work was portable. The environment it lived in was not.

Our answer was simple. One agent. One computer. Any harness. The agent is the durable object. The environment is a real cloud computer it works on. The harness inside it is swappable. One stable API, one isolated environment per agent. Swap the harness in the request payload, and the installed packages, files, secrets, and browser state stay with the agent.

Any harness can drop into the same runtime without the agent needing to know what changed underneath. OpenClaw and Hermes are the supported harnesses today; Claude Code, Codex CLI, and others are on the roadmap, plugging into the same shape. Early access is open if this is the problem you are solving.

Best for: teams building across multiple harnesses, product teams who want a stable API above the infrastructure layer, or anyone who has rebuilt the same agent environment more than once.

How to choose

• E2B if you need untrusted code execution, right now, for one-shot tasks
• Sprites if you want a persistent environment without building infrastructure
• OpenComputer if you need VM-level control and fork-from-checkpoint workflows
• Daytona if you want infrastructure you own with enterprise compliance
• Factory if you are already using their agents
• Spinup if you want a durable agent above a real cloud computer, with the harness swappable on top

The thing nobody says clearly enough

The category is splitting into layers. Sandboxes, environment platforms, and runtime layers are different products that solve different problems. Most teams start with a sandbox, hit a wall when their agent needs to remember something, and move up a layer. They move to an environment platform, hit another wall when they need to manage several agents with different harnesses and a shared policy model, and move up again.

Knowing which layer you need before you build saves a rebuild later.

The market in 2026 has enough choices that you can pick the right layer on day one. A year ago, you couldn't.

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Spinup is in early access. If you want one durable agent above a real cloud computer, with any harness inside it, join the waitlist.