i’ve been trying to build a legitimate apples-to-apples cost comparison between Make, Zapier, and alternatives for our enterprise automation needs, and the math keeps breaking down because of how each platform handles ai model access.
with Make: we pay for the platform, then we manage ai model subscriptions separately. Claude subscription here, OpenAI api there, maybe Gemini for specific tasks. the platform fee is one budget line, the ai costs are another. easy to track separately, hard to compare holistically.
with Zapier: similar situation—you pay for zaps and then manage your ai integrations separately through their ecosystem.
but then someone mentioned a unified subscription model where you get access to 300+ ai models under one license, and the comparison completely breaks.
here’s where i’m stuck:
the per-operation vs per-execution pricing gap: Make charges per operation (each module is an operation). Zapier charges per task/zap. but if you’re on a platform with unified ai pricing, you’re not paying per operation or per zap—you’re paying based on execution time. that changes the comparison completely because high-volume operations suddenly become cheaper.
ai model bundling: if one platform bundles 300+ models under one subscription, how do you compare that against a system where you’re cobbling together multiple vendors? the direct subscription cost for the ai models is gone, but how much is that worth?
hidden vendor coordination costs: make and Zapier force you to manage multiple vendor relationships for ai services. that’s overhead that doesn’t show up in the subscription cost but compounds quickly in an enterprise. how do you price that into the comparison?
execution efficiency: does running 2000 operations on Make cost more or less than running the same operations on a different platform? the pricing models are so different that traditional cost comparisons break down.
i’ve been collecting actual numbers from our operations:
we process roughly 15,000 lead records per month
we do ai classification on maybe 8,000 of those
we run enrichment on all 15,000
we generate ai-powered summaries on about 3,000
that’s a lot of operations if you’re paying per-operation, but on a time-based execution pricing model, it might be dramatically cheaper.
what framework should i be using to actually compare these platforms fairly? and has anyone built out an roi calculator that accounts for the unified ai pricing model without completely bending the numbers?
the framework you need has to separate three things: platform cost, integration overhead, and ai model cost. don’t try to mash them together.
for your 15,000 lead records with 8,000 ai classifications, here’s what matters:
Make: 15,000 enrichment operations × (minimum cost per op) + 8,000 classification operations + platform fee + your separate Claude/OpenAI subscriptions = total. that gets expensive fast at scale.
Zapier: similar math, their per-task pricing compounds even worse with volume.
unified ai pricing: 15,000 operations + 8,000 classifications on time-based execution. the same work might consume maybe 2-3 minutes of execution time total across all 15,000 records. that’s dramatically cheaper than Make’s per-operation model.
the hidden vendor coordination cost is real but you only account for it if you’re comparing total cost of ownership, not just subscription cost. add 0.5-1 engineer hour per week for vendor management. that’s your coordination tax.
for an roi calculator: baseline your current spend across all platforms and vendors. then run a pilot on the unified model for a month using actual data. measure actual execution time, error rates, and cost. that’s your real comparison. don’t try to model it theoretically—the pricing models are too different.
Your comparison framework needs to normalize across pricing models first. Convert all pricing to cost-per-task or cost-per-execution so you’re comparing apples to apples.
For your specific scenario: Make’s per-operation pricing applied to 15,000 lead records with 8,000 AI classifications would cost roughly $200-400 depending on which operations qualify as chargeable. Add your separate AI model subscriptions (probably $500-700/month) and platform fees.
With unified time-based pricing: the same 15,000 records with 8,000 classifications might execute in 5-10 minutes total system time, which translates to roughly $1-5 in execution cost plus the flat subscription.
The gap appears massive, but validate it with actual trial data before committing to the comparison. Run both platforms against identical datasets for one billing cycle and measure actual cost and performance.
Hidden coordination costs (credential management, vendor maintenance) are typically 0.3-0.5 FTE annually. Include that as a TCO line item.
The pricing model comparison requires normalizing to a common unit. Time-based execution pricing fundamentally differs from operation-based pricing, making direct comparison difficult without empirical data. Build your ROI calculator using actual platform trial results rather than theoretical pricing models, especially when comparing across different pricing paradigms.
You’ve identified the core problem: traditional pricing comparison frameworks collapse when you’re comparing across fundamentally different models. Operation-based, task-based, and execution-time-based pricing are incompatible without normalization.
Here’s how to build a legitimate comparison for your 15,000 monthly records with 8,000 AI classifications:
Step 1: Baseline your current spend. Make platform + Claude subscription + OpenAI API usage for your actual volume. That’s your starting point.
Step 2: Model the same workflows on a platform with unified AI pricing and time-based execution. The key: run your actual data through it. Not theoretical estimates—real execution.
Step 3: Measure three metrics across all platforms: total monthly cost, average execution time per workflow, and error rate. Those are your comparison points.
For your specific scenario (15,000 enrichments + 8,000 classifications): on Make, you’re probably looking at $400-600/month in platform costs plus $500-700 in separate AI subscriptions. On a unified platform with time-based pricing, the same workload might run for $200-300 total, including platform and all AI access.
The cost delta is real, but validate it with actual trial data. Latenode lets you run this exact comparison—you can import your lead records, run them through both your current Make workflows and equivalent Latenode workflows, and measure actual cost and performance side-by-side.
The ROI calculator you need isn’t a spreadsheet model—it’s trial data from your actual workflows.