OpenClaw Deployment Paths

Deployment checklist

OpenClaw is a harness. To actually run it, you need to wire up a brain, hands, and a place for it to live.

1. Pick a model (the brain). Choose your AI model provider based on cost, security, and latency. Haiku from Anthropic is a reasonable starting point.
   • Use thinking/reasoning models for orchestration and evaluation.
   • Use cheaper models for tasks that need less brainpower.
   • Local vs. cloud — local if you need privacy, specialized integration, or machine learning. Cloud if you don't want to configure infrastructure.
2. Sign up for a developer account and get an API key from your chosen provider.
3. Sign up for tool API keys (the hands). OpenClaw needs access to whatever it's supposed to act on — calendar, email, search, etc. Each integration is its own API key.
4. Set your budget. Cloud model calls + cloud VM compute add up. Start small, watch the meter.
5. Pick a host. Options discussed in the room:
   • Digital Ocean (OpenClaw marketplace app) — Rodrigo's preferred path. Note: the previous DO credit promo has ended, but Tiffanie mentioned you can still message DO support to ask for a $200 new-user credit.
   • Hostinger — "just a click" option, lighter touch than DO.
   • BareClaw — Yong's cloud-based OpenClaw instances. True one-click deployment if you want to skip the VM setup entirely.

   Rodrigo's advice: there's no consensus on the "best" way. Start with minimal requirements, see what happens, scale from there.

How members are deploying

The room shared their actual setups — useful as a sanity check that there's no single right answer:

• Jay is running three agents in parallel: one on a Windows laptop, one on an Ubuntu Linux VM via UTM on macOS, and one on a Debian VM on Google Cloud (in progress). His criterion for picking a host: "how quickly can I pull the kill switch?" — and he applies the same security hardening + internal agent policies across all three.
• Manasa is exploring Hostinger.
• Ligaya is running one on a Digital Ocean droplet with the web interface — no skills or tools wired up yet. ("You are a super powerful agent and you don't have permission to do anything," she joked.)
• Marc brought up a hardware constraint: an M4 MacBook Pro with 24 GB RAM still can't run a local model that organizes his Downloads folder reliably. Rodrigo: depends on the size of the model.

Discussion themes

Personal-assistant use cases

Manasa wants a personal assistant to do literature search for her — she's a scientist. Danita pointed out this is very close to what she did with newsletter digests; both are "find recent relevant things, summarize, surface what matters." A recurring pattern across the cohort.

Anthropic Claude Platform

Danita flagged that Anthropic released the Claude Platform last week — aimed at running and orchestrating agents more securely. Arathi asked whether it's similar to OpenClaw; the room's read is yes, with overlap. Google is reportedly working on something similar.

The Karpathy AI-OS concept

Danita asked if anyone's tried a Karpathy loop on their agent. Marc connected it to his wiki-AI-OS work — same wavelength. Reference: karpathy/autoresearch.

A prompt worth stealing

Jay shared the prompt he used to bootstrap his OpenClaw setup. It's a good template for any "help me design a system for myself" conversation with an agent:

I want you to help me turn a goal or desire into an actionable, personalized system. First, ask me a few quick questions to understand what I want, why I want it, what's blocking me, and what kind of structure works best for me. Then, based on my answers, design a simple but effective system that I can follow consistently. The system should be specific and realistic, include daily/weekly actions or rituals, minimize friction and decision fatigue, include tracking or reflection if useful, and be adaptable over time. Let's start the interview now. Ask your questions one at a time.