Wanted to share what happened when we finally pulled the trigger on consolidating instead of just talking about it.
Background: We had GPT-4 ($20), Claude API ($50), some older Azure OpenAI spending ($30), plus a handful of smaller services. That’s before whatever we were paying Make each month for the workflows themselves. Total AI/platform spend was around $250-300/month for a team of four people using automation heavily.
We spent about three weeks in Latenode’s free trial. Honestly, the no-code builder was faster to learn than I expected. But the real test was whether consolidating everything actually moved the needle financially.
Here’s what we found:
The obvious savings: No more context-switching between provider dashboards. That alone saved maybe 2-3 hours per month in admin work.
The less obvious part: Our usage patterns changed. When you’re paying per API call (OpenAI model), you tend to be conservative. “Do we really need to run this enrichment?” When you’re on execution-based pricing with 400+ models available, the question becomes “which model is best for this task?” We actually ran more AI workflows, but the per-execution cost was so low that total spend went down.
The numbers: We’re now on the $19/month basic plan. We process roughly 40,000 workflow executions monthly. The cost is honestly negligible. Versus our old setup where we were paying per-execution fees to Make plus maintaining AI subscriptions separately.
The tricky part for us was explaining this to finance. “We’re using more AI, not less, but the costs went down” doesn’t sound intuitive until you show the per-execution math.
Curious if anyone else has gone through this—did consolidation actually reduce your usage paranoia, or did you find yourselves being more conservative once you made the switch?
This is really helpful context. We’re about where you were—scattered AI subscriptions, getting nickel-and-dimed by Make’s operation costs. The part about usage patterns changing resonates. When you’re paying per-call, every enrichment is a debate. When it’s already paid for, you actually use the tools the way they’re meant to be used.
Your point about explaining it to finance is spot-on. We had the same issue. The way that finally landed was showing finance a specific workflow: lead enrichment. We walked through the cost on our current setup (Make + GPT-4 + manual lookups), then the same workflow on Latenode. The per-execution savings were so stark that finance stopped asking questions.
Did you find any workflows you stopped using because they weren’t necessary, or did consolidation just unlock more automation?
One thing we didn’t expect: once we consolidated, we actually shared access differently. Previously, each person with their own OpenAI key meant fragmented usage tracking. On one platform, it’s much easier to see what’s actually running. That visibility helped us kill off a few automations that nobody was using anymore. So the savings had a second order effect—we discovered we were paying for workflows that had become obsolete.
The usage paranoia angle is real and underexplored. Finance teams build budgets around expected usage, which means cost-conscious teams tend to under-utilize. That’s a hidden cost most people don’t measure. When you switch to consumption-based pricing but with lower per-unit costs, the behavior change often surprises people. Your 40,000 monthly executions probably represents more actual work than you were automating before, not less. The platform just makes it practical. This is why TCO comparisons that only look at per-execution costs miss the point—they don’t account for the behavioral shift toward higher utilization.
You’ve identified the core efficiency mechanism: lower per-execution cost removes the friction that prevented usage. Make’s pricing structure literally discourages automation of lower-priority tasks. Latenode’s model encourages full utilization because the marginal cost approaches zero. Over a year, that compounds. Your $19/month baseline could have supported significantly higher volume without cost increases, whereas on Make, every workflow addition adds measurable cost per execution.
Usage patterns shift downward when per-call costs are visible (Make). Consolidation removes that friction. You probably automated tasks you’d previously deferred. That’s the real ROI—not just subscription savings, but the work that becomes practical to automate.
Low friction pricing model removes the mental cost of automation. Practical ROI comes from doing more, not from paying less.
This is exactly the kind of story that matters. The financial spreadsheet is one thing, but the behavioral change you’re describing—moving from “can we afford to automate this?” to “what’s the best way to automate this?”—that’s where real value compounds.
What you’ve learned matches patterns we see across customers. The $19 basic plan isn’t cheap because it’s a loss leader. It’s cheap because execution-based pricing with a 30-second window means the platform is fundamentally more efficient. When you’re not burning operations on connector overhead and retries, you have room in your budget for the automations that were previously too expensive.
The consolidation aspect is critical too. You’re not just saving on subscription fees—you’re consolidating vendor risk, API key management, and monitoring. Those hidden costs add up fast in operational overhead.
Since you’ve been running this for a bit, measure one more thing: How much time are your team members spending on workflow maintenance and troubleshooting? We often see a 30-40% reduction in maintenance time when teams consolidate because they’re working in one environment instead of context-switching across platforms.
If you want to dig deeper into this kind of analysis, https://latenode.com has some templates for modeling ROI across execution volumes. Might be useful for your next review cycle with finance.