In 2026, AI agents do more than call a model. They write code, run it, install packages and operate whole applications through computer use, so every agent needs a machine of its own. Where an agent runs now decides how fast, how safely and how cheaply it works.
Modal grew up on serverless GPUs, and its sandboxes share that foundation. The best Modal alternatives for AI agents are Solari, E2B, Daytona, Vercel Sandbox and Blaxel, and Solari leads the list because it pairs fast, isolated sandboxes with cloud browsers and desktops on one API. This guide covers why teams switch, what each alternative costs and which agents each one suits.
Why Do Teams Look for a Modal Alternative?
Modal is a strong platform for inference, training and batch jobs. Teams start comparing alternatives when the agent, not the model, becomes the product:
The sandbox invoice. Modal bills sandboxes at non-preemptible rates, above its standard rates for functions. For an agent fleet that runs all day, the hourly sandbox rate sets the budget.
Agents that wait. Modal says a sandbox runs for up to 24 hours, with filesystem snapshots to carry state further. Agents that stop for a person's approval need to pause mid-task and resume with memory and processes intact.
The agent needs the web. Coding and research agents read documentation, sign in to dashboards and check their work in a real browser. Modal's products center on compute, so a browser agent usually means a second vendor.
A screen to operate. Computer-use models click and type through a graphical desktop. Teams building them want desktops with a live view on the same account as their sandboxes.
Modal Alternatives at a Glance
1. Solari
Solari runs cloud browsers, AI sandboxes and computer desktops for agents from one API, and 100+ companies and 2,000+ engineers run agents on it.
Speed on every task. Solari finishes a sandbox's full lifecycle, from create through running the code to tear down, about 1.3x faster than E2B. An agent that starts a fresh machine for every task waits less on each one.
Isolation you choose. Every Solari sandbox is a dedicated microVM of your own by default, and you never share a machine with another organization. A hardened tier blocks all outbound network for the code you trust least.
State that survives a pause. Pause a sandbox and its memory and processes are saved; resume it and your agent picks up exactly where it stopped. Snapshot a running sandbox and fork copies from the same state, so you can run many attempts at one task in parallel.
Browsers and desktops on the same account. Solari browsers are ready in 8 ms, and profiles keep a login between runs in the storage state format Playwright uses. Solari desktops give computer-use models a full Linux screen that starts in about a second, with a live view you can take over.
Pricing. Solari starts free and bills every environment per second while it runs. A 4 vCPU, 8 GB Solari sandbox costs as little as $0.114 per hour, almost 3x cheaper than E2B and Daytona per sandbox hour at list price, and you can run up to 100,000 sandboxes or desktops at once.
Where it fits. Solari fits teams whose agents run code, browse and operate desktops without a second vendor. Modal is the more established platform, with a deep GPU and inference ecosystem; Solari is the younger company and moves faster on agent speed and price.
2. E2B
Pricing and plans. E2B says its Hobby plan is free with a $100 one-time usage credit and 20 concurrent sandboxes, Pro is $150 a month plus usage with 100 concurrent sandboxes and sessions up to 24 hours, and 600 concurrent sandboxes cost an extra $500 a month. A team that outgrows 100 agents at once should price the add-on next to its usage.
Where it shines. E2B is sandbox-first and built around code execution for agents, billed per second. It ships often, with E2B Secrets and E2B Embed launched this autumn.
Trade-off. Concurrency is tied to plan add-ons, so scaling past 100 sandboxes is a pricing decision as much as a technical one. Model the add-on cost before a launch.
3. Daytona
Pricing and plans. Daytona says it bills per second with no fixed plan fee, at $0.0504 per vCPU-hour and $0.0162 per GiB-hour of memory, with $200 in free compute to start and up to $50k in credits for startups. Usage-only pricing suits teams that do not yet know their load.
Where it shines. Daytona offers container, Linux VM, Windows and GPU sandboxes under one account. Agents that need Windows or a GPU find both without a second vendor.
Trade-off. Daytona says "Sandboxes run as Linux containers by default", so a full virtual machine is a class you choose. Teams running untrusted agent code should set the class explicitly.
4. Vercel Sandbox
Pricing and plans. Vercel bills sandboxes by active CPU, so time spent waiting on a model call is not billed as CPU, while provisioned memory is billed for the whole run. That model favors agents that spend most of their time waiting on the model.
Where it shines. Each sandbox is a Firecracker microVM with its own filesystem and network, OIDC authentication and an Ubuntu image with Node and Python. Teams already on Vercel get sandboxes in the dashboard they use today.
Trade-off. Vercel Sandbox lives inside Vercel's platform, so a team that hosts elsewhere takes on a new account for one capability. Its strengths center on code that ships to Vercel.
5. Blaxel
Pricing and plans. Blaxel is pay as you go with free credits to start, billing sandbox compute by allocated memory and never for suspended time. Agents that sit idle between bursts of work cost little while they wait.
Where it shines. Blaxel sandboxes scale to zero when idle and resume from standby with memory and filesystem intact. That suits agents that wake on an event, do a short job and sleep again.
Trade-off. Blaxel sells dedicated support and HIPAA coverage as paid add-ons, so a regulated team should add both to its estimate. Concurrency grows by tier as an account tops up credits.
Where Are Agent Sandboxes Going Next?
Four shifts will shape agent environments over the next year:
From containers to full computers. Agents already run Docker stacks and local databases inside their environment. Containers will keep light jobs, while a machine per agent becomes the default for real work.
Pause as the normal state. Long-horizon agents wait on people and tools for hours. Platforms will save memory and processes on every idle and charge nothing for compute while the agent waits.
One account for code, web and screen. A single task will run code, sign in to a website and operate a desktop app. Teams will pick platforms that cover all three.
Forks for evals and training. Teams will fork hundreds of copies of one prepared sandbox to test agents and generate training data. Fast snapshot and fork become standard requirements.
What stays human is the boundary. A sandbox contains an agent's mistakes, but people still decide which code it may run, which systems it may touch and which results ship.
How to Choose a Modal Alternative
Choose Solari over Modal when your agents run code, browse and operate desktops, and you want sandboxes that finish a full lifecycle faster at a lower hourly rate, with browsers and desktops on the same bill. Choose E2B for agents that only execute code, Daytona for Windows or GPU sandboxes, Vercel Sandbox for teams already on Vercel and Blaxel for agents that idle most of the day.
Stay with Modal when GPU inference and training are the core of your product and sandboxes are a side workload. For agent teams, Solari gives you browsers, sandboxes and desktops from one API, built for the way agents work, as set out in Introducing Solari.
Get Started With Solari
Start with one job, such as moving your agent's code execution into a Solari sandbox, then add a browser when it needs the web. Solari is the fastest and cheapest way to run cloud browsers and VMs for AI agents that write and run code. Get started with a free account and start running your agents today.
