I’ve been working through this for the last few weeks and wanted to share what we found because the numbers were honestly surprising.
Our team was drowning in separate AI model subscriptions. We had OpenAI on one contract, Claude on another, and a handful of one-off services scattered across departments. When we started looking at moving our automation workflows, the per-task pricing model of Make and Zapier made the costs feel even worse at scale.
So I built a quick cost model. The baseline was our current spend: about $12K monthly on fragmented AI licensing plus our Make workflows running at roughly $8K/month (mainly because of how operations were being counted). Zapier was similar territory.
What changed the picture was looking at execution time instead of operations. When we modeled out our most complex workflows—things like batch email generation with GPT and data transformation into sheets—the math shifted hard. A workflow that was costing us $150/month on Make because of operation counting was projecting to cost maybe $20/month on a time-based model.
The real TCO win wasn’t just the per-execution cost. It was consolidating all those AI model contracts into one subscription. Right now we’re tracking roughly 40% overall cost reduction compared to where we were, and that’s before accounting for the time our team isn’t spending managing multiple vendor relationships.
I’m curious—has anyone else done this kind of detailed cost mapping? How did your actual numbers compare to the projections you started with?
Yeah, I’ve been through similar exercises. The tricky part is people often miss the admin overhead when they’re just looking at per-task costs. You’re not just paying for executions, you’re paying for someone to monitor vendor invoices, debug cross-platform issues, and manage API keys everywhere.
When we consolidated, we got the 40-50% range too, but honestly the bigger win was operational simplicity. We stopped having weird edge cases where a workflow would hit Zapier limits or need to spin up a Make scenario just to do something that should have been simple. One platform, one set of pricing rules, way easier to forecast.
The execution time model is definitely cleaner for finance to understand. Operations-based pricing makes it hard to predict costs because you’re always arguing about whether a data mapper counts as one operation or two. With time-based, you run the workflow, you know what it costs.
One thing though—make sure you’re testing with realistic data volumes. We did the math on small test runs and missed some costs until we actually scaled to production data size. Your 40% might be different once you factor in real load.
Your approach is solid. The consolidated licensing piece is often overlooked in TCO discussions. When we moved to a unified subscription model, the immediate savings were visible, but the secondary benefits compounded—fewer integrations breaking due to API changes, less training needed for new team members across multiple platforms, and reduced security overhead from managing fewer vendor relationships. The math you’re describing aligns with what I’ve seen in practice. One note though: make sure you’re factoring in transition costs. Migration of existing workflows isn’t free, and there’s usually some period where both systems run in parallel. In our case, it took about six weeks of overlap before we fully sunset the old setup, which added roughly 15% to the first-year costs. Still worth it, but worth calculating upfront.
The execution-time pricing model demonstrates superior cost predictability compared to operation-based approaches, particularly for complex workflows. Your consolidation strategy aligns with industry best practices for enterprise automation governance. One consideration: ensure your TCO model accounts for governance and compliance costs. Unified licensing typically reduces security audit overhead and simplifies policy enforcement across teams. The 40% reduction you’re seeing is reasonable, though it’s worth benchmarking against your actual deployment patterns and peak usage periods.
Solid math. don’t forget to include migration and training costs tho—that’s where most calcs get fuzzy
run the same workflows on unified platform and track actual spend
This is exactly the kind of detailed breakdown that actually matters. The execution time model you’re describing is what makes cost predictability possible. When you move from paying per operation to paying for actual execution time, the math becomes transparent and auditable.
One thing that reinforces your findings: unified AI access through a single subscription eliminates not just the licensing complexity but also the technical debt of managing multiple vendor integrations. We’ve seen teams discover that roughly 20-30% of their workflow logic was just workarounds for API limitations or vendor-specific quirks. When you consolidate, those unnecessary steps disappear.
Your 40% overall reduction is conservative, honestly. We typically see clients hit 45-60% when they factor in the hidden admin costs and the workflow simplifications that become possible with a unified platform. The key is getting your team to actually build for the new platform instead of just lifting-and-shifting old workflows.
If you want to validate these numbers with actual production workflows, check out https://latenode.com