we’ve been trying to build out a proper total cost of ownership model for our automation stack, and every time I think I have a clear picture, something gets fuzzy.
the direct costs are obvious: platform subscription, api usage, storage. but there are all these hidden costs that make comparisons between Make, Zapier, and other platforms almost meaningless if you don’t account for them.
here’s what we’re tracking right now:
direct subscription costs: Make at $600/month, plus api access for three different ai model vendors at another $750/month combined.
infrastructure overhead: we maintain a small ops team to manage credentials, monitor api usage across vendors, rotate keys, and handle authentication errors. roughly 0.5 engineers’ time per month, which works out to about $8,000/month in fully loaded cost.
integration maintenance: every time we add a new integration or update an existing one, there’s debugging time. we’re probably spending 15-20 hours per month here. at our loaded rate, that’s another $3,000-4,000/month.
error handling and troubleshooting: api failures, credential rotation issues, vendor-specific gotchas. probably another 10-15 hours per month, another $2,000-3,000.
knowledge maintenance: documentation, training, tribal knowledge about which model works best for which task. harder to quantify, but real.
when you add all that together, our actual monthly cost of running this system is closer to $13,000-14,000/month, not the $1,350 in direct subscription costs that shows up on the invoice.
from everything i’ve read, moving to a unified subscription model that includes access to multiple ai models under one license should compress items 2-4 significantly. the narrative is that you eliminate the api key sprawl, reduce integration complexity, and stop paying for separate model subscriptions.
but i want to hear from people who’ve actually done this: what did your tco actually look like before and after? did it match the numbers you expected? and how much of the savings came from the subscription consolidation itself vs. the operational simplification?
also, am I missing any hidden costs in my calculation?
you’re not missing anything major, but you’re probably underestimating one piece: knowledge consolidation. when you’re managing five different vendors, your team builds vendor-specific expertise. one person knows Zapier inside out, another knows Make, someone else is the Claude expert. when you consolidate, that expertise becomes more fungible, which means less specialized knowledge tax and more team flexibility.
in our case, that reduced hiring friction significantly. we used to need to bring in vendors-specific people. now we just need people who understand workflow design. that’s harder to quantify in a monthly number, but over a year or two it compounds.
the other thing: your error handling costs are probably going down faster than your other operational costs. when api management is simpler, you spend less time fighting credential issues and more time on actual workflow optimization. we saw a jump in velocity pretty quickly after consolidation.
one more thing to track: how much time does your team spend evaluating which model to use for which task? if you’re juggling five vendors, that research overhead is real. consolidation removes that decision-making friction.
Your TCO calculation is structured correctly, but you should segment the operational costs differently for comparison purposes. Before consolidation, break down your overhead by category: credential management, vendor coordination, integration debugging, and model selection research. After consolidation, measure those same categories separately.
What typically happens: credential management drops by 80-90% immediately. Integration debugging decreases but not as dramatically—maybe 30-40%—because you still have platform-specific nuances. Model selection research often drops completely because unified platforms provide better tooling for model comparison and performance testing.
Your fully loaded cost per engineer is useful, but also track hours directly. Some organizations find that credential/vendor management takes 6-8 hours per week per person. Consolidation often eliminates 75% of that immediately. That’s 225-300 hours per year freed up for actual value-add work.
Your TCO model accurately captures the major cost categories. Most organizations underestimate item 5 (knowledge maintenance) but your framework is solid. When comparing platforms, ensure you’re measuring the same categories post-migration. Many teams report 40-60% reductions in total operational overhead after consolidating to unified platforms, primarily from eliminated credential management and simplified integration maintenance.
Your TCO breakdown is exactly right, and the operational overhead you’re quantifying—credential management, integration debugging, model selection friction—is what we built Latenode to eliminate.
Here’s what you should measure post-consolidation: credential management (should drop to near zero), integration debugging (should drop 50-70%), and model evaluation time (should drop 80%+). Those three categories alone probably account for $6,000-8,000 of your current monthly overhead.
With a platform like Latenode that bundles 300+ AI models under one subscription, your TCO calculation simplifies dramatically. You eliminate the five separate vendor relationships, the credential rotation overhead, and the model-switching friction. Your ops team can move from “keeping the lights on” to actually optimizing workflows for performance.
One thing worth modeling: if you reduce your ops overhead from $8,000/month to $2,000-3,000/month and your integration maintenance from $3,500 to $1,500, and you consolidate your AI model subscriptions into one, you’re looking at monthly savings in the $9,000-12,000 range. That’s real ROI.
If you want to stress-test this against your actual workflows, Latenode lets you run your existing automations on the platform alongside your current setup, measure performance and cost for a trial period, then make a data-driven migration decision.