This is exactly what I push every operator I coach to do. Measure everything. Know your costs per call, per model, per workflow. Most teams skip this and then wonder why their AI budget exploded in month three.
What I love about your n8n implementation that you're using proper software architecture fundamentals. I've been building my own automation stack for tracking this and I might steal your approach for my n8n workflows.
The teams that win at AI aren't the ones with the biggest models. They're the ones who know exactly what every inference costs and build accordingly.
@Travis Sparks , thank you and so glad that you found it resonating.. in this whole Demo to Dependable series, I am going to share what rarely anyone talks about.. the real BTS stuff of running AI Automation as business and what pitfalls to avoid.. Thanks for pushing me kick start this long pending initiative.. :)
Wow, Dheeraj! Thanks for this super comprehensive piece on AI costs in executions, scaling, inference. n8n tracking executions over API calls seems to highlight the crux. The AI Cost scanner and cost tracking framework is very useful and something that I will try myself!
Thank you Raghav for giving it a read and foudning it helpful.. I always wanted to build this AI Cost scanner that is more hands-off.. Most of the solutions out there require each workflow to have this data emitted out, so one more thing tio remember, this approach helps hands-off cost tracking of llm calls in your entire n8n instance.. so worth having this in place.
Thanks for this detailed breakdown Dheeraj, I love the step-by-step approach and the screenshots to showcase your build journey. I don’t currently use n8n, but I think this framework should work across other AI automations tools like Make.com and Zapier.
This is a great piece for anyone wanting to track AI automation costs.
This is exactly what I push every operator I coach to do. Measure everything. Know your costs per call, per model, per workflow. Most teams skip this and then wonder why their AI budget exploded in month three.
What I love about your n8n implementation that you're using proper software architecture fundamentals. I've been building my own automation stack for tracking this and I might steal your approach for my n8n workflows.
The teams that win at AI aren't the ones with the biggest models. They're the ones who know exactly what every inference costs and build accordingly.
@Travis Sparks , thank you and so glad that you found it resonating.. in this whole Demo to Dependable series, I am going to share what rarely anyone talks about.. the real BTS stuff of running AI Automation as business and what pitfalls to avoid.. Thanks for pushing me kick start this long pending initiative.. :)
Wow, Dheeraj! Thanks for this super comprehensive piece on AI costs in executions, scaling, inference. n8n tracking executions over API calls seems to highlight the crux. The AI Cost scanner and cost tracking framework is very useful and something that I will try myself!
Thank you Raghav for giving it a read and foudning it helpful.. I always wanted to build this AI Cost scanner that is more hands-off.. Most of the solutions out there require each workflow to have this data emitted out, so one more thing tio remember, this approach helps hands-off cost tracking of llm calls in your entire n8n instance.. so worth having this in place.
Thanks for this detailed breakdown Dheeraj, I love the step-by-step approach and the screenshots to showcase your build journey. I don’t currently use n8n, but I think this framework should work across other AI automations tools like Make.com and Zapier.
This is a great piece for anyone wanting to track AI automation costs.