Best VPS Hosts for Running OpenClaw 24/7 (Hetzner vs DigitalOcean vs Vultr)

If you’re looking to run OpenClaw 24/7 and are evaluating VPS hosts like Hetzner, DigitalOcean, or Vultr, you’ve likely encountered the promise of cheap, always-on compute. The reality, especially with long-running AI inference tasks, can be a bit more nuanced than the marketing suggests. This guide cuts through the noise to give you practical advice based on real-world OpenClaw deployments.

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Hetzner Cloud: The Price-Performance King (with a Catch)

Hetzner Cloud often comes out on top for raw price-performance, especially with their “Falkenstein” (Germany) and “Helsinki” (Finland) data centers. For an OpenClaw instance, you’re primarily concerned with CPU stability, RAM, and consistent network throughput to your model API (e.g., Anthropic, OpenAI). A typical OpenClaw setup monitoring a few dozen RSS feeds and performing summarization, classification, and webhook notifications can comfortably run on a CPX11 instance (2 vCPU, 2GB RAM) costing around €4.30/month.

However, there’s a significant catch: Hetzner’s CPU fair-use policy. While not explicitly stated for individual vCPUs, their shared CPU instances can experience “noisy neighbor” issues, leading to unpredictable performance dips, especially during peak hours. More critically for OpenClaw, if your instance consistently hits high CPU usage (e.g., from frequent, complex model calls or parallel processing of many feeds), Hetzner’s automated systems might throttle your instance or, in rare cases, flag it for resource abuse. I’ve personally seen OpenClaw instances on CPX11 exhibit unexplained slowdowns and occasional outright crashes overnight, often recovering on their own, suggesting temporary resource contention. This is less of an issue if your OpenClaw instance is mostly idle, polling every few minutes. But if you’re running large language models locally (which is not recommended for these small VPS types), or processing hundreds of requests per minute, you will hit this limit.

To mitigate this on Hetzner, if you find your OpenClaw instance becoming unresponsive or showing high load average without obvious OpenClaw activity, you might need to upgrade to a dedicated core instance or, more practically for OpenClaw, ensure your interval settings in .openclaw/config.json are sufficiently high to prevent constant CPU spikes. For example, if you’re polling 50 feeds, setting "interval": "5m" (5 minutes) instead of "interval": "1m" can make a huge difference.

Here’s an example of a good starting .openclaw/config.json for Hetzner, emphasizing efficiency:

{
  "api_key": "YOUR_ANTHROPIC_API_KEY",
  "default_model": "claude-haiku-20240307",
  "base_url": "https://api.anthropic.com/v1",
  "interval": "5m",
  "max_concurrent_tasks": 5,
  "data_directory": "/var/lib/openclaw",
  "plugins": [
    {
      "name": "rss_ingestor",
      "config": {
        "feeds": [
          {"url": "https://example.com/feed1.xml", "tags": ["tech", "news"]},
          {"url": "https://example.org/feed2.xml", "tags": ["ai", "research"]}
        ]
      }
    },
    {
      "name": "webhook_notifier",
      "config": {
        "url": "https://your-webhook-endpoint.com/receive",
        "method": "POST"
      }
    }
  ]
}

The non-obvious insight here: while Hetzner’s documentation might imply that 2 vCPUs are equivalent to 2 full cores, in shared environments, they are not. For OpenClaw, prioritize stable, consistent CPU over burst performance if your budget forces you onto shared plans. Also, consider their dedicated core options like CCX12 if you need guarantees, but that pushes the price up significantly.

DigitalOcean Droplets: Predictable Performance, Higher Cost

DigitalOcean offers a more predictable experience, especially with their “Basic” droplets. A 1GB Memory / 1 vCPU droplet starts around $6/month, which is comparable to Hetzner’s CPX11 but often feels more stable under sustained load. Their “Premium AMD” droplets offer even better single-core performance, which is beneficial for OpenClaw’s largely single-threaded core processing of items before offloading to model APIs.

I’ve found DigitalOcean to be more forgiving with consistent CPU usage. If your OpenClaw instance is frequently polling and processing, a $6/month or $8/month droplet provides a smoother experience than a similarly priced Hetzner shared CPU instance. The main trade-off is the cost per resource unit; you generally pay more for the same raw specs compared to Hetzner.

DigitalOcean’s monitoring is also quite good, allowing you to easily track CPU utilization and I/O, which helps diagnose OpenClaw performance issues. If you see persistent 100% CPU usage, it’s a clear indicator that your max_concurrent_tasks or interval settings are too aggressive for your chosen droplet size, or you have a plugin misbehaving. The non-obvious insight: DigitalOcean’s network performance to major API endpoints (e.g., Anthropic, OpenAI) tends to be very consistent, which is crucial for reducing latency on LLM calls. This translates to faster overall processing per item.

A Raspberry Pi will absolutely struggle with OpenClaw. Even a Pi 4 with 8GB RAM will be bottlenecked by its slower CPU architecture and I/O compared to x86/x64 VPS options. This applies equally to DigitalOcean, Hetzner, and Vultr: stick to x86/x64 for OpenClaw.

Vultr Cloud Compute: A Balanced Middle Ground

Vultr positions itself somewhere between Hetzner and DigitalOcean in terms of pricing and features. Their “Cloud Compute” plans are competitive, with a 1 vCPU, 1GB RAM instance starting at $6/month. Vultr’s network quality is generally excellent, and I’ve experienced very stable CPU performance on their shared plans, often feeling more akin to DigitalOcean than Hetzner in terms of predictability under load.

One area where Vultr shines for OpenClaw is their global data center presence. If your primary API endpoints or webhook targets are geographically diverse, Vultr likely has a data center closer to them, potentially reducing latency. This can be critical if you’re chaining OpenClaw with other services that are sensitive to network delays.

The non-obvious insight: Vultr’s single-core performance on their standard plans tends to be very good for the price. This is beneficial for OpenClaw’s primary loop, which iterates through items and dispatches tasks. A strong single core reduces the time spent on the main thread, allowing the asynchronous tasks to complete more quickly. Always consider the CPU clock speed and single-core benchmark if you have a choice. Often, a “faster” 1 vCPU instance will outperform a “slower” 2 vCPU instance for typical OpenClaw workloads.

Choosing the Right Host for Your OpenClaw Deployment

For most OpenClaw users running a standard configuration (RSS ingest, LLM summarization/classification, webhook notification) with up to ~100 feeds and a few thousand items per day, a VPS with 2GB RAM and 1-2 vCPUs is sufficient. This only works if you’re on a VPS with at least 2GB RAM. Raspberry Pi will struggle due to its limited processing power and I/O. For the LLM models, I strongly recommend using external APIs like Anthropic’s Claude Haiku. It’s not just about cost; running a reasonable LLM locally on a small VPS is generally unfeasible due to RAM and CPU requirements.

Specifically, the docs might default to a model like claude-3-opus-20240229 for examples, but claude-haiku-20240307 (or its latest iteration) is usually 10x cheaper and good enough for 90% of tasks like summarization, sentiment analysis, and basic classification. Always configure your default_model accordingly to save costs.

If you’re budget-constrained and willing to tolerate occasional, minor performance fluctuations, Hetzner’s CPX11 or CPX21 are hard to beat on price. If predictability and consistent performance are paramount, and you don’t mind paying

Frequently Asked Questions