In 2026, agents stopped finishing in minutes. The length of task a frontier model can complete on its own keeps growing on a steady exponential curve, so agents now run for hours or days, and each one needs a computer that stays alive for the whole job.
Sail Research builds for exactly that: it pairs low-cost open-model inference with Sailboxes, persistent Linux virtual machines for long-horizon agents. The best Sail alternatives are Solari, E2B, Daytona and Blaxel. Solari fits teams whose agents need browsers and desktops as well as a sandbox; E2B, Daytona and Blaxel are sandbox-first. This guide compares each on features, pricing and fit.
Why Do Teams Look for a Sail Alternative?
Teams look elsewhere when their agent needs more than a Linux shell, or when Sail's inference pairing does not apply to their stack. These are the three events that usually start the search:
The agent needs a browser or a screen. Sailboxes are full Linux machines built for code and long jobs. An agent that signs in to web apps, fills forms or drives a desktop app through computer use also needs a browser layer and a graphical screen, ideally on the same account.
The model comes from somewhere else. Sail says on its Sailboxes launch post that a Sailbox can sleep while it waits on a Sail inference call. Teams that run Claude or GPT models keep the sandbox but lose that pairing, which is a large part of the Sail pitch.
Some commands cannot wait. Sail says on the same post that live migrations happen a few times a day and can add a few seconds to an occasional command. Background agents tolerate that; an agent with a person watching, or one running in a tight loop, needs steadier starts.
Which Sail Alternative Should You Choose?
Each alternative has its own honest niche. Here is the short version:
Choose Solari when your agent needs browsers, sandboxes and desktops from one API. It also fits teams that care about start time and cost per sandbox hour across a large fleet.
Choose E2B when you want a code-execution sandbox with a free starting credit. It suits agents whose jobs finish within a day.
Choose Daytona when you need containers, Linux VMs, Windows and GPU machines from one provider. It suits teams that mix workloads across operating systems.
Choose Blaxel when agents sit idle most of the time and must wake almost instantly. Its pricing charges for active compute only.
Stay on Sail when your agents run for days on open models. Sail's inference pricing then carries most of the bill.
1. Solari
Solari is agent infrastructure: cloud browsers, AI sandboxes and computer desktops from one API and one account. Your agent brings the model and the logic; Solari gives it a place to run.
Core capabilities. Solari sandboxes are isolated Linux machines for agent code, with commands, files, git, snapshots and forks. Each one runs as a microVM of its own by default, and a hardened mode blocks all outbound network for code you do not trust. Solari desktops add a full Linux computer with a screen for computer-use models, and cloud browsers connect over Playwright, Puppeteer or the Chrome DevTools Protocol.
Built for long jobs. Pause a sandbox or desktop and its state is saved, so the next connect picks up exactly where the agent stopped. While your agent waits on a model response, parking freezes the CPUs and keeps the machine resident, so the next action wakes it with nothing to restore. Durable checkpoints on an interval mean a host problem costs at most one interval of progress.
Speed. Solari browsers are ready in 8 ms, so a new task starts as soon as your agent asks for it. A desktop usually starts in about a second, so a computer-use agent gets its screen without a wait.
Scale. With Solari you can launch up to 100,000 sandboxes at once. Your agent can fan a job out across a fleet, or run reinforcement learning rollouts in parallel instead of in a queue.
Pricing and plans. Solari is about 1.3x faster than E2B across a sandbox's full lifecycle and almost 3x cheaper than E2B and Daytona per sandbox hour, and you can start for free. A paused machine does not count against your limit on running machines, so a fleet of waiting agents does not crowd out new work.
Logins and audit. A Solari profile keeps a browser login between runs, so a long-running agent signs in once. Session recording lets you replay what the agent did on a page, which doubles as an audit trail.
Where it fits. Sail pairs its sandboxes with its own model inference; Solari is the younger company and pairs sandboxes with browsers and desktops, faster starts and a lower price per sandbox hour. Teams that bring their own model get the whole environment from one vendor.
2. E2B
E2B is a sandbox platform for running agent-generated code, with a Python and JavaScript SDK.
Pricing and plans. E2B's free tier comes with a $100 one-time usage credit, sessions up to 1 hour and 20 concurrent sandboxes, and its paid tier is $150 a month plus usage with sessions up to 24 hours and 100 concurrent sandboxes. Usage is billed per second of a running sandbox, by vCPU count. A prototype agent can start on the credit with no card.
Where it shines. E2B is focused on code execution and keeps adding features for it, such as secrets for credentials an agent needs inside the sandbox. A team that only needs to run model-written code gets a narrow, well-documented tool.
Trade-off. Session length is set by tier, so an agent that runs for days needs E2B's custom tier with multi-day sessions. A team that also needs browsers or desktops adds a second vendor.
3. Daytona
Daytona sells sandboxes as composable computers for agents, across several machine classes.
Pricing and plans. Daytona bills $0.0504 per vCPU-hour and $0.0162 per GiB-hour of memory, with $200 in free compute to start, calculated per second. It publishes no fixed monthly plan, and larger limits go through its sales team.
Where it shines. Daytona offers container, Linux VM, Windows, macOS and GPU sandboxes, with Linux containers as the default class. A team that needs a Windows app or a GPU next to its agent can stay with one provider.
Trade-off. Pause, resume and fork work on Daytona's VM classes only, not on the default containers. A long-horizon agent that needs its memory saved should be created as a VM sandbox from the start.
4. Blaxel
Blaxel builds persistent microVM sandboxes that suspend when idle.
Pricing and plans. Blaxel is pay-as-you-go with up to $200 in free credits, billed on active compute. Suspended time does not count toward the bill, so an idle agent costs only its snapshot storage.
Where it shines. Blaxel says its sandboxes scale to zero when idle and resume from standby in milliseconds with memory and filesystem intact. That suits coding agents and app builders that sit on standby until a person comes back.
Trade-off. Blaxel runs the root filesystem in memory, so data is wiped when a sandbox is destroyed. Long-term state needs a Blaxel Volume or its Agent Drive on top.
Where Are Long-Horizon Agents Going Next?
Today most long-running agents work alone on one machine. Over the next year that changes in four ways:
Agents that run for weeks. Research, migration and maintenance agents will hold context across days of work. The machine under them has to pause, resume and checkpoint without losing a step.
One agent, many machines. A planner will fork a sandbox into dozens of copies, try each path and keep the one that works. Start time and cost per machine decide how wide that search can go.
Code and web in one job. An agent will write code in a sandbox, check the result in a browser and file it through a desktop app. Teams will want all three under one account.
Audit by default. A company that lets an agent work for a week will want to see what it did. Recordings and replays become a normal part of review.
What stays human is the judgment: which jobs to hand over, what the agent may touch, and what counts as done. The teams that do best will set those rules and let the agents run.
Get Started With Solari
Choose Solari over Sail when your agent needs a browser or a screen next to its sandbox, when you bring your own model, or when start time and price per sandbox hour across a fleet matter most. Stay on Sail when your agents run for days on open models and Sail's inference carries most of the bill.
Start with one long job, such as a research agent that works through a list overnight in a sandbox with a browser beside it, then widen it to the full fleet once it runs cleanly. Solari is the fastest and cheapest way to run cloud browsers and VMs for long-horizon agents. Get started with a free account and start running your agents today. For the full picture of the platform, see the Solari introduction.
