I’m trying to build honest numbers on what we’re actually spending on automation right now, and it’s more complex than I initially thought. Obviously there’s the Camunda licensing bill—that part is clear. But when I include everything, the picture gets messy.
Then there are the AI model subscriptions we’re bolting on separately. Then there are the custom integrations we’ve had to build because nothing connects natively. Then there’s the internal engineering time to build workflows, maintain them, and manage the whole system.
What I’m realizing is that the licensing cost that shows up on the bill is maybe 40% of the actual total spend. The other 60% is hidden across engineering time, integration costs, and vendor management overhead.
I’m trying to figure out if this is just how automation always works, or if there’s a way to structure platforms and licensing to collapse some of this hidden cost. And I’m curious what other people are actually seeing in their numbers.
When you run a full accounting of automation spend—not just the platform license but everything that touches it—what does the breakdown actually look like? And for people who’ve migrated to different platforms, did you actually see that breakdown shift, or is it just different costs with the same rough ratio?
This is one of those questions that is way more important than most people realize. We went through a full audit two years ago, and it was eye-opening.
Breakdown looked like: 35% platform licensing, 25% AI model subscriptions, 20% internal engineering time (full-time equivalent), 15% API integration costs and custom development, 5% vendor management overhead.
Total annual spend was more than 3x what the platform licensing showed alone. When we switched to a different platform with unified AI model pricing and better native integrations, that percentage shifted dramatically.
New setup: 45% platform subscription (one unified fee covering everything), 25% internal engineering time, 20% custom integration work, 10% infrastructure. Net effect was we reduced total spend, but more importantly we reduced variance. The subscription is predictable, so finance can actually budget properly.
The internal engineering time didn’t go down—people just got more productive per hour. With better tooling and fewer integration headaches, the same team handled more workflows.
Most important thing we learned: don’t just look at platform licensing. If you can collapse vendor costs and reduce integration work, internal time usually handles itself through productivity gains.
Your numbers are roughly right. Most organizations run about 30-50% as visible licensing cost with the rest distributed across integrations and labor. The deeper issue is that these hidden costs scale differently.
Licensing usually scales linearly. Support and integration work scales exponentially if you’re not careful. Each new workflow compounds your integration debt.
What changes the math is how much of that integration work is baked into the platform versus left for you to solve. A platform that ships with solid integrations and AI models already included moves a lot of that work from your engineering team to the vendor’s engineers. That’s worth real money.
The ideal breakdown for a mature automation setup is roughly: 40-50% platform cost (covers licensing and core features), 30-35% integration and development (mostly one-time, amortized), 20-25% ongoing support and maintenance. Internal team overhead should be minimal once the platform is stabilized.
If your internal time is higher than 20%, you’re either running an immature platform or you chose tools that force too much custom work. That’s a sign the platform itself might not be optimized for your use cases.
we audit every quarter. platform 30%, integrations 25%, internal time 35%, infrastructure 10%. switched platforms, moved to 50% subscription, rest stayed same. tooling matters.
Full audit including labor. Calculate total spend, then compare platform features against internal time required. High internal time means weak platform fit.
When I did this breakdown for our team, it was humbling. We were spending about 60% of total automation budget on hidden costs outside the platform license.
Here’s what I found: 40% platform licensing, 20% separate AI subscriptions (we were paying for Claude, GPT, and Deepseek independently), 25% internal engineering time, 15% custom integrations.
Moving to one platform with 400+ AI models included changed everything. The unified subscription consolidated the licensing plus AI cost into 45% of total spend. Native integrations cut custom development from 15% to 8%. And here’s the surprising part—internal time went from 25% to 18% because people weren’t fighting with API credentials, model selection logic, and integration debt.
The no-code builder was huge here. Non-technical people could build simple workflows, which freed up engineering time for actual complex work. That productivity multiplier cut effective labor costs.
Total automation spend actually went down, but the real win was that it became predictable. Finance can actually forecast now.