We’re trying to build an actual financial model for migrating to open-source BPM, and the cost piece keeps getting messy because we have so many separate AI subscriptions. Currently we’re paying for:
GPT-4 access ($20/month minimum)
Claude API subscription ($50/month minimum)
Specialized model subscriptions (Cohere, Gemini) ($30-40/month each)
Plus usage overages that fluctuate
Add it all up, and we’re probably burning $200+ per month just on AI model access, before we even account for infrastructure, maintenance, and engineering time.
The question I keep asking is: what does the actual cost look like if you have one unified subscription that covers 400+ models? I understand execution-based pricing is different from per-model pricing, but I’m trying to figure out if it’s actually cheaper, or if the savings are just a marketing narrative.
Let me be specific about what I need to know: if we migrate to a single subscription model that charges based on workflow executions, what does that actually cost for our volume? We run roughly 5,000 workflow executions per month, with an average of 2-3 AI model calls per execution.
Does that math actually work out cheaper, or are we just trading one cost structure for another that looks better on a spreadsheet?
Has anyone actually done this calculation and compared apples-to-apples with their previous setup?
We did this exact calculation because the vendor claims were making us nervous. Let me break it down.
Old setup: We had three main AI subscriptions running in parallel—GPT-4, Claude, and a specialized NLP service. Cost per month was unpredictable because we’d hit overages when usage spiked, especially around month-end processing.
New setup with execution-based pricing: We switched to a consolidated model and tracked the first three months carefully. For 5,000 monthly executions with an average of 2 AI calls per execution, we’re looking at roughly 10,000 discrete AI model interactions.
With execution-based pricing starting at $19/month for basic volume, the per-execution cost works out to something like $0.002 per execution at our volume level. That’s significantly cheaper than managing three separate subscriptions with their respective minimums and overage costs.
Where we actually saved money: the operational overhead disappeared. We consolidated API key management, unified monitoring, single billing cycle. That operational simplicity was worth more than we expected. Plus, no more overage charges because the execution model gives you predictable pricing.
But here’s the thing: the cost savings were about 30-35% reduction in total AI spend. Not 50% or 70%. It’s meaningful but not earth-shattering. The bigger win was predictability and reduced operational burden.
For your 5,000 monthly executions, I’d estimate you’d see similar savings—probably around $140-160 per month if you’re currently at $200. That’s real money but not transformative.
The real number depends on your current usage patterns. We were paying for three separate subscriptions but only using about 40% of the capacity we were paying for. That’s the hidden cost nobody accounts for.
When we switched to execution-based pricing, we suddenly only paid for what we actually used. That was the actual savings driver. For your 5,000 monthly executions with 2-3 AI calls each, you’re probably looking at 30-40% cost reduction if you compare fairly—meaning accounting for the unused capacity you’re currently subsidizing.
The execution model is cheaper than separate subscriptions for typical usage patterns, but the gap isn’t huge. Maybe 25-35% savings realistically.
Don’t just look at base prices. Look at what you actually consume vs. what you’re paying for. That’s where you’ll find real savings.
Execution-based pricing works better than per-model pricing when your usage is predictable and moderate. At 5,000 monthly executions with 10,000 AI interactions, you’re in a sweet spot where consolidation makes financial sense.
Old model (three subscriptions): $200+ minimum plus unpredictable overages
New model (execution-based): $30-50 per month for your volume level
That’s roughly 70-80% savings for your specific usage, not the 30-35% we see with average implementations. Your volume and pattern happen to align well with execution-based pricing advantages.
Calculate based on your actual usage. If you can extract three months of data from your current subscriptions, you’ll see exact cost impact.
Your math is on the right track, but you’re missing the operational savings piece. We see clients consolidating from multiple AI subscriptions achieve 40-50% cost reduction on the AI side alone, but the bigger financial impact comes from reduced engineering overhead.
With 5,000 monthly executions at 2-3 AI calls each, you’re actually in a strong position. At Latenode’s execution-based pricing ($19/month starting), your 10,000-15,000 monthly AI interactions would cost significantly less than three separate subscriptions plus the person-hours you’re spending managing keys, monitoring limits, and dealing with overage charges.
What we recommend: run a one-month parallel analysis. Keep your current setup, but log what you’d consume using execution-based pricing. After one month, you’ll have exact numbers instead of estimates.
The real financial win comes when you stop paying for minimum subscriptions on services you only use 40% of. That’s not marketing narrative—that’s predictable waste elimination.