In 2026, agents no longer finish in one request. The length of tasks AI agents can complete on their own has been doubling at a steady pace for years, so agents now code, research and test for hours, and they spend much of that time waiting on a model.
That shift changed what a sandbox has to do. Solari and Sail both build machines for long-running agents. Solari is the faster and broader choice, with browsers, sandboxes and desktops from the same API. Sail fits teams that already run open-weight models on Sail's inference and want sandboxes that sleep during those calls. This guide compares the two on speed, price and fit.
Why Does the Sandbox Choice Matter Now?
A sandbox used to run a snippet of agent code for a few seconds and disappear. Now the same agent keeps a machine for a working day: it installs packages, edits a repo, runs tests and waits for the next model response.
That pattern puts two questions in front of every team. How fast does a machine start and wake, given that a long agent pauses and resumes many times? And what do you pay while the agent is thinking rather than computing? Sail built its product around the second question when it made Sailboxes generally available as a cloud environment for long-horizon agents.
Solari vs Sail at a Glance
Solari gives you browsers, sandboxes and desktops from one account. Sail pairs persistent sandboxes, called Sailboxes, with its own model inference.
Which Is Faster, Solari or Sail?
Solari is faster to start and wake. A Solari machine is ready usually in about a second, and a same-host resume after a pause takes about 1.3 seconds on staging, five trials. Sail says a Sailbox starts or resumes in under 3 seconds.
A long agent wakes far more often than it starts. Pause a Solari machine and its memory and processes are saved. For short waits inside a session, park the machine instead: its CPUs freeze while everything stays in memory, so the next action wakes it with nothing to restore.
Sail makes a different trade. Sail says it moves running Sailboxes between hosts a few times a day to raise utilization, so an occasional command waits several seconds longer in exchange for lower cost.
If your agent also works on the web, Solari browsers are ready in 8 ms, so research steps start as fast as code steps.
Which Is Cheaper, Solari or Sail?
It depends on how your agent uses the machine. Sail says it bills $0.015 per used vCPU-hour, charges memory and disk by what a Sailbox actually uses, and does not bill sleeping, paused or cold-starting time.
Solari bills per second while an environment runs, at $0.0175 per vCPU-hour and $0.0055 per GB-hour for teams running at scale. Sail's used-capacity billing favours an agent that reserves a large machine but barely touches it; Solari's flat rate favours an agent that keeps its machine busy, since you know the hourly cost before the run starts.
At volume, Solari's rates drop further: for fleets of up to 100,000 concurrent sandboxes or desktops, Solari charges $0.00175 per vCPU-hour, a tenth of that rate.
For smaller teams, Solari's top self-serve plan is $200 a month with $200 of usage included. Sail says its paid self-serve plan is $250 a month with $100 in included credits every month. On Solari you can pause a sandbox for free, and a paused machine stops counting against your plan's limit on running machines.
What Can You Run on Each?
Both run long agents. The difference shows in the jobs around them:
Long-running coding agents. Both give the agent a full Linux machine that survives idle time. Sail says Sailboxes have no runtime limits; a Solari sandbox resumes exactly as it was after a pause. Either keeps a repo, its packages and its shell state across a day of work.
Rollouts and evals. Solari lets you snapshot a prepared sandbox and fork copies from the same state, so hundreds of graded episodes start from one setup. Sail markets Sailboxes for reinforcement learning rollouts paired with its own inference.
Web research behind a login. An agent that reads sites needs a browser next to its shell. With a Solari profile your agent signs in once and every later session reopens the same cookies, and it connects with Playwright or any Chrome DevTools Protocol client.
Computer-use agents. Models with computer use operate software through a screen. Solari desktops give them a full Linux computer with a live view you can watch and take over.
Agents on open-weight models. If your model runs on Sail's inference API, Sailboxes sleep automatically during those calls, so you stop paying for compute while the model works. Solari runs any model you bring, including models served by Sail.
What Role Does Solari Play?
Solari is the infrastructure layer for the whole agent: one API key, one bill, three kinds of machine. Solari launched with all three surfaces under one API.
Fleets on demand. Solari lets you run one agent or a fleet of thousands in parallel. An eval sweep or a batch of coding tasks starts at once instead of queueing.
Hardware isolation by default. A default Solari sandbox is a microVM of its own with a dedicated kernel, separated by hardware virtualization. Choose the hardened tier and all outbound network is blocked, so untrusted agent code has nowhere to send data.
Browsers that stay signed in. Solari browsers carry profiles, stealth and proxies for the sites your agent reads. Your agent gets the web and a shell from the same account.
Desktops for screen work. Solari desktops share the sandbox lifecycle, so pause, resume and park work the same way for a computer-use agent as for a coding agent.
Where Are Long-Running Agents Going Next?
Agents that run for days. As task lengths grow, a machine that keeps its state through a week of work becomes the default, not the exception.
Idle time priced near zero. Billing moves toward active compute only, so an agent that waits on a model or a person costs little while it waits.
Teams of agents on shared state. A coder, a researcher and a reviewer will share files across a sandbox, a browser and a desktop.
Screens instead of APIs. Computer-use agents will operate tools that never shipped an API, which puts desktops next to sandboxes in every stack.
What stays human is direction: choosing what the agent works on, and approving what it ships.
Get Started With Solari
Choose Solari when your agents need fast starts and wakes, predictable hourly rates, and browsers or desktops next to the sandbox. Stay with Sail when your model already runs on Sail's inference and sleeping during those calls is the saving you want. Sail is the specialist with its own model inference; Solari moves faster on speed and price and gives you every surface from one API.
Start with one job: move a single coding agent's sandbox to Solari and time its starts and resumes against your current setup. Solari is the fastest and cheapest way to run cloud browsers and VMs for long-running agents. Get started with a free account and start running your agents today.
