You’ve got a proof-of-concept OpenClaw assistant humming locally, but now it’s time to share it, or perhaps you just want it running 24/7 without tying up your workstation. The natural next step for many is a low-cost VPS. While cloud behemoths offer a dizzying array of options, for OpenClaw users on a budget, DigitalOcean and Vultr often emerge as front-runners. The core problem isn’t just provisioning a server, but getting consistent, reliable performance for your AI without breaking the bank, particularly when dealing with the intermittent but intense bursts of CPU usage OpenClaw can demand.
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I’ve personally deployed numerous OpenClaw instances on both platforms, typically starting with their cheapest “basic” tier – a 1GB RAM, 1 CPU shared core machine. On DigitalOcean, this usually means a Droplet, and on Vultr, a Cloud Compute instance. The initial setup is straightforward on both: spin up an Ubuntu 22.04 LTS instance, SSH in, and follow the standard OpenClaw installation guide. The first snag often appears when you try to run your assistant with anything beyond a trivial prompt. You might see your assistant hang, or take an inordinately long time to respond, sometimes even leading to a SIGKILL from the kernel if memory is exhausted during a particularly large model load. This is where the shared CPU architecture starts to show its limitations.
The non-obvious insight here is not just about raw CPU speed, but about CPU credits and burst performance. DigitalOcean, especially on their older Basic plans, can sometimes feel like you’re sharing a single core with half a dozen other busy tenants. Vultr, on the other hand, often provides a slightly more generous allocation of burstable CPU, even on their entry-level plans. I’ve found that a Vultr “Cloud Compute” instance with 1 CPU and 1GB RAM often outperforms a comparably priced DigitalOcean “Basic” Droplet for OpenClaw’s typical workload, which involves periods of idle waiting followed by intense, short-duration computation. When you run top or htop during an OpenClaw model inference on a Vultr instance, you’re more likely to see sustained 100% CPU usage for the duration of the task, whereas on DigitalOcean, it can sometimes feel throttled, even if the OS reports 100% usage.
If you’re deploying a standard OpenClaw assistant that uses an on-device model like a small Llama derivative, you absolutely need to monitor your swap usage. While 1GB RAM is often enough for the OpenClaw core processes, loading a 7B parameter model can easily push you over the edge. Both providers allow you to add swap space, but Vultr’s underlying disk I/O often feels snappier when swap is actively being used. A good starting point for your /etc/fstab might be /swapfile none swap sw 0 0 after you’ve created a 2GB swap file. The key is to be proactive; don’t wait for your assistant to crash. Vultr often edges out DigitalOcean here due to what feels like a more consistently provisioned I/O subsystem on their lower tiers.
For your next step, provision a Vultr Cloud Compute instance (1 CPU, 1GB RAM), ensure you create and enable a 2GB swap file, and then deploy your OpenClaw assistant following the official setup guide, paying close attention to the openclaw-server-start.sh script’s memory footprint for your chosen model.
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