The cloud plans cover the hosted OpenClaw deployment footprint and the onboarding path for getting the system live. Credits and custom implementation work are handled separately so they scale with usage instead of being buried inside the base plan.
Choose the right OpenClaw rollout.
Start in cloud for speed. Move into ship-in or on-site only when the environment needs tighter control. Keep model usage separate so the base plan stays clean.
Keep implementation, hosting, and model usage separate so you can scale the right line item instead of paying for bundles you do not need.
Fastest path to a live operator.
For most teams, this is the right starting point. Pick the amount of headroom you need now, then move up only when the workload actually earns it.
Starter
Best for a first live assistant, early workflow volume, and proving a useful operator without overcommitting.
Start Starter- 2 vCPU / 2 GB RAM / 40 GB SSD
- 1 OpenClaw deployment
- Browser automation + integrations
- Telegram-first onboarding
Pro
For heavier messaging volume, multi-channel work, and teams that want a stable daily operator instead of a test environment.
Start Pro- 3 vCPU / 4 GB RAM / 80 GB SSD
- 1 OpenClaw deployment
- Priority support path
- Best fit for growth ops
Business
Built for more persistent autonomous work, higher concurrency, and teams that need more headroom before moving into custom environments.
Start Business- 4 vCPU / 8 GB RAM / 160 GB SSD
- 1 OpenClaw deployment
- Dedicated support path
- Better fit for heavier workflow spikes
Install for controlled environments.
Some deployments need hardware handling, tighter training, or a real on-site install. These options exist for those cases, not as default upsells.
Ship-In Setup
Ship your hardware to us. We configure, harden, test, and return it ready for deployment.
Book Ship-In Setup- Full configuration + testing
- Security hardening included
- 30 days post-launch support
- 5–10 business day turnaround
On-Site Setup
We come to your location, deploy the system directly, and train your team in the environment where it will actually run.
Book On-Site Setup- Professional on-site installation
- In-person training + walkthrough
- Security hardening included
- 30 days post-launch support
Support Hours
Remote troubleshooting, implementation help, review sessions, and system fixes. The more hours you commit, the lower the effective hourly rate.
- Remote troubleshooting and fixes
- AI model and workflow optimization
- Integration support
- Discounts up to 35% for larger hour blocks
Keep model usage flexible.
Credits are the usage layer. They let a deployment access model capacity without forcing provider setup on day one.
What credits cover.
Bring in help where it counts.
Use support hours for quick unblock work, a scoped engagement for a defined build, or a retainer when you need ongoing operating help.
Hourly sessions
Screen share, SSH, or video call support for fixes, reviews, and implementation help. Volume discounts apply as the hour block increases.
Project-based implementation
A fixed-scope engagement for automation builds, integrations, audits, migrations, or a defined operator rollout.
Send project inquiryMonthly retainer
Ongoing AI systems support for teams that need strategic guidance, implementation capacity, and faster escalation.
Start retainerWebsite design & build
A conversion-oriented site build with design, copy, SEO, and deployment handled as one scoped deliverable.
Buy website packageQuestions before launch.
These are the questions that usually come up before a team chooses a deployment path.
Not necessarily. You can use OpenClaw Credits and avoid managing provider accounts yourself, or bring your own keys later if your control requirements change.
Choose ship-in or on-site when the environment, security requirements, or rollout process requires more control than a managed cloud deployment gives you. For most buyers, cloud is still the fastest path to value.
Credits cover AI model usage inside the platform. That keeps access flexible across different providers and makes it easier to top up compute without changing the underlying deployment plan.
Pick the path first.
Start with the environment that fits the work. Add credits, services, or implementation help only when the rollout needs it.