We’re in the middle of a platform evaluation and the financial math keeps getting weird because nobody agrees on what to include in the TCO calculation.
Our platform costs are straightforward—Make or Zapier, pick a plan, you know the monthly bill. But the labor piece is where everything falls apart. If we go with Make or Zapier, we’re probably outsourcing most automation work to consultants or building a small internal automation team. If we go with something more accessible, maybe our business teams can handle more of it themselves.
So the questions become: How do you value the labor savings if non-technical people can build basic automations? Do you include full salary costs, or just marginal time allocation? What’s the ROI timeline—do you amortize over 12 months or longer?
We started with a simple model: platform cost plus estimated labor hours per automation, annualized. But we kept getting different numbers depending on whose estimates we used. Our lead developer said automations take 12 hours to build and maintain. Our business development manager said she could build one in 2 hours herself. Obviously those don’t compare directly, but they also aren’t just a complexity difference—one is professional maintenance, one is ad-hoc.
When we factor in the unified AI pricing we might get from moving to a platform like Latenode, the math shifts because we’re consolidating subscriptions. But I’m honestly not sure if we’re calculating the ROI correctly or just adding variables until the spreadsheet matches what we want the answer to be.
How are other teams actually doing this math? Are you including labor costs at all, or just treating the platform as a platform cost and labor as a separate budget line?
You’re hitting the real problem with these comparisons. The TCO isn’t just about platform cost—it’s about the total drag on your business from building and maintaining automations, plus what automation actually enables you to do.
Here’s how I think about it: Break it into build time and maintenance time. Build time is a one-time cost per automation—make an assumption about average complexity and stick with it. Maintenance time is recurring—assume each automation needs maybe 5-10% of its build time per year for updates and fixes. Then layer in the labor cost based on whoever’s actually doing the work.
If you’re comparing to using consultants, your actual cost per automation is pretty high because you’re buying external expertise. If you’re building internal capability, the cost per automation trends down over time as your team gets faster. That means TCO improvements scale with maturity.
For the non-technical person building in 2 hours versus your developer building in 12, the 2-hour version is probably less robust and will need more maintenance. Account for that in your ongoing cost model and the math usually makes more sense.
The unified AI pricing piece is a legitimate cost reduction that often gets overlooked. Most teams have API spend scattered across multiple vendors. Consolidating that definitely improves the ROI timeline, but you need to actually calculate it instead of just assuming it’s better.
Run a 90-day pilot where you measure actual labor hours spent on automation work and actual platform usage. Real numbers will align your team much better than estimates. Then you can build your ROI model on actual data instead of guesses. That’s what finally got our stakeholders to agree on an evaluation approach.
Most TCO models I see miss the hidden costs. License management overhead, onboarding time, error handling and exceptions. If a platform is complex to use, support costs go up. If it’s accessible, they go down. Quantify those differences.
Also, be explicit about your assumptions. Write down whether you’re valuing your developer’s time at their full burden cost or just marginal allocation. Write down what you assume about automation complexity distribution. Share those assumptions with stakeholders so there isn’t disagreement later about why the numbers came out a certain way.
break it down: platform cost + labor hours + maintenance. use real numbers not estimates. compare apples to apples.
Include labor costs explicitly. Build time + maintenance time + platform fee = true TCO. Use average complexity, not outliers.
The reason your numbers keep shifting is that you’re not anchoring on what actually matters—total effort per automation across the full lifecycle, not just platform cost.
Here’s the frame that works: Calculate your true cost per automation by including build time, maintenance, AI model costs, and platform licensing. Then model how that cost changes if different people are building automations. Your 2-hour business user automation is cheaper to build but might have higher maintenance cost. Your 12-hour professional automation is more expensive upfront but more reliable.
Now here’s where Latenode changes the math. Because the platform is more accessible, your business teams can actually own more automation work. That doesn’t mean they build everything—it means they can handle higher percentages of routine automations, which frees your professional people for the complex stuff. Your average cost per automation goes down because you’re distributing the work more efficiently.
Plus, the unified subscription for 400+ AI models means you’re not fragmenting costs across multiple vendors. That consolidation often cuts 25-35% off total AI-related expenses compared to managing separate subscriptions.
Build your model around total team capacity and what people can actually own, not just individual automation complexity. That’s how TCO actually connects to ROI.
Head to https://latenode.com to see how teams are modeling their automation economics.
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