Run computer-use agents across Linux, Windows, macOS, and Android
Who should use this?
Computer-use agents spanning native and web applications and Cross-OS agent training and evaluation.
Who should avoid it?
Simple workflows with dependable APIs, Production desktops containing broad user credentials
What problem does it solve?
Cua is open-core infrastructure for giving AI agents isolated computers, GUI control, cross-OS fleets, and evaluation environments through SDK, CLI, and MCP interfaces.
Would I recommend it?
Cua earns a pilot for engineering teams whose agents genuinely need native-computer access or cross-OS evaluation. Start with disposable, non-production machines and read-only tasks. Require an explicit permission policy, isolate identities and credentials, and promote write access only after failure-path results are acceptable.
Advisor score
Premium review framework
Cua is open-core infrastructure for giving AI agents isolated computers, GUI control, cross-OS fleets, and evaluation environments through SDK, CLI, and MCP interfaces.
Cua earns a pilot for engineering teams whose agents genuinely need native-computer access or cross-OS evaluation. Start with disposable, non-production machines and read-only tasks. Require an explicit permission policy, isolate identities and credentials, and promote write access only after failure-path results are acceptable.
Run 250 tasks across two operating systems and five applications, including stale windows, ambiguous controls, hidden dialogs, denied permissions, prompt injection, network failure, duplicate submissions, and destructive actions. Measure verified completion, silent error, intervention, duplicate effects, policy bypass, recovery time, retained artifacts, and fully loaded cost per accepted task.
Personal Recommendation
Cua earns a pilot for engineering teams whose agents genuinely need native-computer access or cross-OS evaluation. Start with disposable, non-production machines and read-only tasks. Require an explicit permission policy, isolate identities and credentials, and promote write access only after failure-path results are acceptable.
Try the recommendation
Open-core cross-OS computer runtime
Overall Score
Editorial Review Framework
Who should use this?
Computer-use agents spanning native and web applications, Cross-OS agent training and evaluation, Teams needing local, cloud, BYOC, or on-premises options.
Who should avoid it?
Simple workflows with dependable APIs, Production desktops containing broad user credentials
What problem does it solve?
Cua is open-core infrastructure for giving AI agents isolated computers, GUI control, cross-OS fleets, and evaluation environments through SDK, CLI, and MCP interfaces.
Would I recommend it?
Cua earns a pilot for engineering teams whose agents genuinely need native-computer access or cross-OS evaluation. Start with disposable, non-production machines and read-only tasks. Require an explicit permission policy, isolate identities and credentials, and promote write access only after failure-path results are acceptable.
Overall Score
8.2
Ease of Use
7.8
AI Quality
8.0
Features
8.6
Speed
8.0
Integrations
8.2
Value for Money
8.0
Customer Support
7.6
Learning Curve
7.4
Recommended Because…
Open-core cross-OS computer runtime
Scores use a 0-10 editorial scale. The source data is maintained as 5-point review dimensions, then normalized for reader-friendly comparison.
Reusable trial worksheet
Turn this review’s buyer test into evidence. Your entries autosave only in this browser and are never added to shared shortlist links.
Confirm the tool meets every must-have workflow and stakeholder requirement.
Review starting point: Computer-use agents spanning native and web applications; Cross-OS agent training and evaluation; Teams needing local, cloud, BYOC, or on-premises options
Run the same representative work you would use in production; do not score a polished demo.
Review starting point: Complete three to five representative tasks with known acceptable outcomes and compare them with your current process.
Calculate the effective cost per accepted result, including usage, review, corrections, and required add-ons.
Review starting point: Cua's open-source Sandbox, Driver, Bench, and Lume components can run locally without a software subscription. Cua Fleet lists $0.044625 per vCPU-hour and $0.0223125 per GB-hour; model inference, storage, operating-system licensing, network traffic, and engineering remain separate. BYOC and on-premises arrangements are available. Reviewed September 12, 2026.
Define an acceptance threshold, test known answers and edge cases, and record every correction.
Review starting point: Editorial quality signals: features 4.3/5; AI quality 4.0/5. Validate these signals in your own work.
Verify what data enters the product, who can access it, how long it is retained, and whether it trains models.
Review starting point: Use approved low-risk data first. Check roles, consent, deletion, subprocessors, model-training settings, and the contract—not only the marketing page.
Test the real handoffs, permissions, failure states, and export path your team depends on.
Review starting point: OpenAI Codex, Claude Code, Gemini, MCP, Python, Terraform
Record training, governance, reliability, accessibility, ownership, and change-management risks before rollout.
Review starting point: GUI automation remains nondeterministic; Safe defaults depend on configuration; Total cost extends beyond compute rates
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Evaluation
Research-based
Price posture
From $0/month
Reviewed
2026-09-12
No authentic product screenshot is published for this review. DiscoverAI does not use generated interface images as product evidence.
Cua's open-source Sandbox, Driver, Bench, and Lume components can run locally without a software subscription. Cua Fleet lists $0.044625 per vCPU-hour and $0.0223125 per GB-hour; model inference, storage, operating-system licensing, network traffic, and engineering remain separate. BYOC and on-premises arrangements are available. Reviewed September 12, 2026.
Free plan: Yes. Core local components are MIT licensed; cloud fleet consumption is usage-priced.
Editorial freshness
Pricing and material product claims were checked September 12, 2026.
Community evidence
Structured, editor-moderated experience—not star ratings. This complements our independent review and never changes its score.
No approved community evidence yet. Be the first verified user to contribute.
Cua is infrastructure that gives AI agents isolated computers and a driver for operating code, tools, APIs, and graphical interfaces.
Its core local components are open source; managed Cua Fleet usage is billed by CPU and memory, with other workload costs separate.
Cua advertises Linux, Windows, macOS, and Android environments, with exact features varying by runtime and deployment.
No. It supplies isolation and policy controls, but buyers must configure permissions, constrain credentials, verify actions, and require approval for consequential steps.
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