All systems operational

Give your AI agent
a computer.

Browsers to navigate the internet. Terminals to write and run code. Desktops to interact with applications. Isolated, metered, instant.

<1s
Container start
$0.02
Per hour starting
L0-L4
Isolation levels
100%
API-driven
Agent Browser
competitor-analysis.com/results
Q1 2026 Market Report
marketresearch.io/reports/q1-2026
Competitor Pricing Analysis
data.industry.com/pricing
Technology Trends Overview
techtrends.co/2026
Agent Terminal
$ git clone repo.git
Cloning into 'project'...
$ python run_tests.py
Running 47 tests...
PASSED (47/47) 12.3s
$
Agent Desktop
14:32

Browsers. Terminals. Desktops.
Everything an agent needs to act.

Your agent can think. We give it hands, eyes, and a workspace.

Web Browsers

Real Chrome and Firefox instances your agent controls via CDP. Navigate pages, fill forms, extract data, click buttons. The full internet, accessible by API.

POST /api/browsers → CDP URL in <1s

SSH Terminals

Full Linux machines with root access. Write code, install packages, run processes, manage files. Persistent state that survives across sessions.

POST /api/sandboxes → SSH access

GUI Desktops

Linux desktops with XFCE or KDE accessible via VNC. Your agent can see the screen, click buttons, type text, and interact with any GUI application.

POST /api/desktops → VNC stream

Instant Start

Warm pool keeps containers pre-provisioned. Your agent gets a browser or sandbox in under one second. No cold boots, no image pulls, no waiting.

Warm pool → sub-second delivery

Full Isolation

Five isolation levels from L0 (full host) to L4 (read-only rootfs). Each agent gets its own network namespace. No cross-tenant visibility. Ever.

L0–L4 isolation + org boundaries

Hard Cost Limits

Set a monthly budget. The platform enforces it. If your agent hits the limit, it stops. No surprise bills. No runaway loops costing you $500 overnight.

HTTP 402 at budget → agent stops

Three steps. One API call.
Your agent has a computer.

No infrastructure to manage. No images to build. No networking to configure.

1

Create an environment

One API call provisions a browser, terminal, or desktop for your agent. Specify the type, isolation level, and idle timeout. The container starts instantly from the warm pool.

# Give your agent a browser response = requests.post( "/api/browsers", json={"browser_type": "chrome"}, headers={"X-API-Key": key} ) cdp_url = response.json()["proxy_url"]
2

Agent does its work

Your agent connects to the browser via CDP, the sandbox via SSH, or the desktop via VNC. It browses websites, executes code, interacts with applications. Full autonomy, full isolation.

# Agent browses autonomously browser = playwright.connect(cdp_url) page = browser.new_page() page.goto("https://example.com") data = page.query_selector_all(".result")
3

Container cleans up

When the agent finishes, the container is released back to the pool or destroyed. You pay only for the minutes used. Auto-stop prevents orphaned containers from running forever.

# Release when done requests.post( f"/api/containers/{container_id}/release", headers={"X-API-Key": key} ) # Cost: ~$0.005 for 3 minutes

Personal assistants. Research agents.
AI employees. Automation at scale.

Autonomous Web Research

Give your AI a browser and a question. It navigates real websites, reads content, follows links, extracts data, and compiles findings into structured reports. No scraping APIs. No proxy services. A real browser, doing real research.

  • Personal research assistant that reads 50 articles overnight
  • Competitive intelligence agent monitoring rival websites daily
  • Academic research agent downloading papers and extracting methods
  • Market analysis agent tracking pricing across 100 e-commerce sites

Illustration: Agent researching across multiple browser windows,
data flowing to compiled output

Browser Automation at Scale

Run 50 browser instances simultaneously. Each agent fills forms, navigates workflows, enters data, and processes documents. Parallel execution across isolated containers. No shared state, no race conditions.

  • Invoice processing agents downloading from 20 vendor portals
  • CRM update agents entering data into Salesforce and HubSpot
  • Compliance agents checking regulatory websites for policy changes
  • Data entry agents populating enterprise systems from spreadsheets

Illustration: Grid of browser windows operating in parallel,
progress indicators, central orchestration

Autonomous Code Execution

Your agent clones repos, writes code, runs tests, and pushes changes. Full Linux environment with package management, git, and every language runtime. The agent works like a developer at a terminal.

  • AI coding assistants that fix bugs and submit pull requests
  • Data processing agents running ETL pipelines on datasets
  • Infrastructure automation agents patching servers via SSH
  • ML training agents setting up environments and running experiments

Illustration: Code editor, terminal, git flow,
full development workflow

Enterprise AI Operations

Deploy hundreds of AI employees across departments. Each department gets isolated compute with its own cost tracking, permissions, and admin visibility. Auth Mesh handles org hierarchy, SSO, and audit trails.

  • Customer support agents browsing knowledge bases and CRM tools
  • Finance agents processing invoices and reconciling accounts
  • HR agents scanning resumes and scheduling interviews
  • Legal agents researching case law and drafting contract summaries

Illustration: Org hierarchy tree, department isolation,
cost badges, security shield

From one person with an AI assistant
to an enterprise with 500 AI employees.

Individual AI Power User

Developers, researchers, knowledge workers
"I want my AI assistant to have its own computer. A browser it can use, a terminal it can type in. And I want it to cost less than my coffee."
  • Personal research assistant browsing the web overnight
  • Code automation agent that clones, tests, and deploys
  • Data collection agent extracting prices from 50 stores

Enterprise AI Operations

VP Engineering, AI Ops leads, IT Directors
"I need to give 200 AI agents their own browsers and computers, track costs per department, and make sure marketing's agent can't see engineering's data."
  • 50+ AI employees across 10 departments with isolated compute
  • Per-department cost tracking and monthly budget enforcement
  • SAML SSO, org hierarchy, and compliance audit trails

Agent Platform Builder

AI startups, agent framework teams
"We're building the AI agent. We don't want to also build the cloud. Give us an API that creates isolated containers and tracks costs per customer."
  • Runtime compute for their agent product via API
  • Multi-tenant isolation: child orgs per end-customer
  • Wholesale compute metering for resale billing

Solo Developer

Indie hackers, freelancers, experimenters
"I just need a box. Don't make me think about networking, IAM, or security groups. Let me SSH in and start working."
  • Remote dev environment at $0.08/hr
  • ML training on GPU instances without AWS complexity
  • The on-ramp: start with a sandbox, discover agent capabilities
<1s
Container Start Time
5
Isolation Levels
$0.02
Starting Per Hour
80%
Uptime Commitment

Your agent's work is invisible
to everyone else.

Every container is isolated at the network, filesystem, and process level. Multi-tenant boundaries are enforced by Auth Mesh's Zanzibar-based access control.

Container Isolation

Each agent runs in its own Linux namespace with dedicated networking, filesystem, and process space. L4 isolation adds read-only rootfs, restricted syscalls, and capability dropping.

Org Boundaries

Auth Mesh enforces org-level isolation via Zanzibar relation-based access control. Department A's agents cannot see Department B's containers, data, or costs. Verified at the database layer.

Audit Trail

Every container creation, command execution, and resource access is logged with user_id, org_id, and timestamp. Generate compliance reports without custom code.

Dual Authentication

Every endpoint accepts both JWT bearer tokens (for human users via OAuth) and API keys (for agents and CI/CD). SAML SSO supported per-org for enterprise deployments.

One API call.
Your agent has a browser.

REST API with OpenAPI spec. Authenticate with API key or JWT. Every container type follows the same create / use / release lifecycle.

Read the Docs
agent_example.py
import requests from playwright.sync_api import sync_playwright # 1. Create a browser for our agent api = "https://sandbox.service.ab0t.com" resp = requests.post( f"{api}/api/browsers", json={"browser_type": "chrome"}, headers={"X-API-Key": API_KEY} ) browser_url = resp.json()["proxy_url"] # 2. Agent browses the web with sync_playwright() as p: browser = p.chromium.connect_over_cdp(browser_url) page = browser.new_page() page.goto("https://example.com/data") results = page.query_selector_all(".item") data = [r.text_content() for r in results] # 3. Release container when done requests.post( f"{api}/api/containers/{cid}/release", headers={"X-API-Key": API_KEY} )

Pay for compute, not seats.
Your agents aren't employees.

Usage-based: per-minute billing on every container, no flat seat fees. Start with $10 of free credit — no card required.

Free
Experiment and evaluate
$0/mo
$10 of compute credit included
  • 1 concurrent sandbox
  • 2 browser sessions
  • $10/mo spend cap
  • Community support
Start Free
Pro
Power users at scale
Usage
Up to $1,000/mo spend cap
  • 25 concurrent sandboxes
  • 50 browsers, 25 desktops
  • 5 GPU instances
  • Audit logs
  • Priority support
Get Started
Enterprise
AI operations at scale
Custom
Unlimited compute
  • Unlimited containers
  • 50+ GPU instances
  • SAML SSO & custom roles
  • SLA & dedicated support
  • Audit logs & compliance
Contact Sales
See full pricing & per-instance rates →

Give your AI agent
its own computer.

Start free. No credit card required. Your first browser container starts in under a second.