n8n vs. Make vs. Custom Python Workers: Choosing Your Automation Core
Real cost, latency, and error-recovery benchmarks after running 4 million monthly webhook executions.
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1. Latency & Concurrency Limits
When orchestrating multi-step AI pipelines with external API calls, runtime latency and webhook timeout limits dictate architectural feasibility.
2. True Cost at 1M+ Executions
While visual workflow builders like Make offer rapid prototyping, operational costs scale exponentially compared to self-hosted n8n instances or lightweight Python Docker containers on Google Cloud Run.
3. Architectural Verdict
For most teams processing under 50,000 monthly events, managed platforms provide unbeatable speed-to-market. For enterprise volumes, dedicated microservices are essential.