Selecting the Optimal DigitalOcean Droplet vCPU for n8n-based AI Agents

Below is some guidance to help you plan your scaling strategy based on n8n’s recommended practices:

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CPU and Memory Considerations

• CPU: n8n isn’t very CPU intensive. Even small instances (like many DigitalOcean droplets) can support the typical workload, as long as the workflows aren’t doing heavy computation. The emphasis in n8n is more on memory — many workflows only pass data along, unless you’re using nodes (such as the Code node) that do extra processing.

• Memory: The recommended memory for a standard setup can be as low as 320 MB, but more complex or data-intensive workflows (especially involving binary data or custom processing) might require 2 GB or more. For your use case (100–400 daily interactions per agent), ensure that the droplet has sufficient RAM to cover temporary processing and the chosen database’s needs.

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Concurrency and Request Handling

• Concurrent Executions:
  While n8n Cloud sets explicit concurrency limits (e.g., Starter accounts are capped around 5 concurrent executions), self-hosted instances like yours typically run without such fixed limits. From performance benchmarking, an n8n instance can handle a very high number of executions per second on a robust setup. However, the real-life numbers depend on factors such as workflow complexity and available resources.

• vCPU Setups:
  There isn’t a one-size-fits-all answer regarding how many concurrent requests a certain vCPU configuration can handle since:

  • It depends on whether your workflows are lightweight or involve heavy processing.
  • Real-world concurrent interactions may be spread over the day rather than spiking heavily.

  For lightweight workflows, even a droplet with a single vCPU (or burstable CPU on lower-tier plans) may be enough, especially at the scale of 100–400 interactions daily. However, if you anticipate peak traffic where many customers interact simultaneously, you might want to consider scaling up to droplets with more vCPUs and memory.

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Recommendations for Scaling Your Setup

1. Monitor Real Usage:
   Start with your current setup and monitor metrics such as execution times, CPU load, and memory consumption. This will help you understand resource requirements under your specific workloads.

2. Dedicated Database:
   Use a dedicated database for each instance or leverage Postgres with schema isolation to avoid potential performance degradation related to shared resources.

3. Incremental Approach:

   • If you start seeing delays or increased response times, consider moving to a slightly larger plan (adding more memory or vacating additional vCPUs).
   • Scale out by deploying additional droplets for separate n8n accounts as planned. This ensures that one instance’s overload won’t affect the others.

4. Leverage Containerization:
   If you’re running n8n in containers, ensure that your data volumes are persistent and correctly mounted. This is especially important for stateful data like workflow executions and credentials.

5. Benchmark Testing:
   Consider running your own benchmarks using n8n’s benchmarking framework (as outlined in the performance benchmarking guidelines) to simulate your expected load. This can provide insights on how your current setup behaves under simulated peak conditions.

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Summary

• For 100–400 daily interactions per AI agent, the DigitalOcean $6 plan might suffice if your workflows are lightweight.
• Since n8n is not CPU intensive, memory allocation and proper database management become more important.
• If your interactions or workflow complexity increases, you might need to scale vertically (more vCPUs/memory in each droplet) or horizontally (distributing workloads across multiple instances).

By monitoring and incrementally scaling based on real usage patterns, you can ensure a stable environment for your AI agents without running into crashes.