We’ve been running our automation stack on Make with a mix of Zapier integrations for about two years now. Over that time, we’ve ended up with subscriptions scattered everywhere—OpenAI for one workflow, Anthropic for another, some smaller models I honestly forgot we were paying for. Each one gets its own integration cost, its own API key management nightmare.
I started digging into what we’re actually spending per month, and the number shocked me. We’re paying roughly $2,400 in separate AI model subscriptions, plus the base platform fees on top of that. Then there’s the hidden cost: the engineering time spent managing all these integrations, documenting which API keys go where, handling credential rotation.
What really got me thinking is whether moving to a platform with unified AI pricing would actually change the financial picture for us. Like, if we could access 400+ models through a single subscription, would that flatten out the cost curve? Or are we just moving the problem rather than solving it?
I found some case studies showing that companies moving to unified AI licensing saw about 40% cost reduction for equivalent functionality, but I’m skeptical about how they’re calculating that. Are they including migration costs? Labor for rebuilding workflows?
Has anyone actually gone through this kind of consolidation and tracked the real numbers before and after? I’m wondering if the savings are real or if it’s just marketing math.
Yeah, we did this consolidation about six months ago. The math looked good on paper but the real win came from something different than what the vendors talk about.
We were managing five separate API keys for different AI models across our workflows. The platform fees were maybe 30% of the pain. The other 70% was the operational overhead—tracking which key went where, rotating them periodically, debugging integration failures that were actually just auth issues.
When we moved to a single subscription model, the per-model cost dropped about 35% for us, which was close to what they promised. But the real savings came from not needing a junior engineer spending maybe 8 hours a week on key management and integration troubleshooting.
One thing I’d warn you about though: the migration itself isn’t free. We spent maybe 40 hours rebuilding our existing workflows to work with the new platform’s architecture. That’s real cost that the ROI calculators sometimes gloss over. If you factor in engineering labor at $100/hour, that’s $4,000 just to migrate.
For us the payback was about four months. Now every new workflow we build costs less and takes less time to set up. Where we really see the benefit is when we’re prototyping new automations—we can spin them up way faster without worrying about licensing overhead for each component.
The consolidation question is worth answering with actual data, not marketing claims. I worked through this at a previous company where we had nine separate SaaS subscriptions for automation and AI integrations. We calculated the total cost of ownership including platform fees, API usage, and most importantly, the engineering labor spent managing credentials and integration issues.
What we found was that 60% of our automation budget wasn’t going to actual execution costs—it was going to licensing, overhead, and management. When we consolidated onto a unified platform, that percentage dropped to 25%. The remaining 75% was actual usage, which was roughly the same volume we’d been running.
However, there’s a critical factor most analyses miss: vendor lock-in. When you consolidate everything into one subscription, you gain efficiency but lose flexibility. If that vendor changes pricing or introduces features you don’t want, you’re now migrating again.
The real answer depends on your workflow complexity and team size. For teams under five people, consolidation often makes sense because the management overhead is already your biggest cost. For larger teams, sometimes it’s better to keep a few vendors and manage the overhead as a cost of maintaining flexibility.
The vendors often claim 40% savings by comparing apples to oranges. They’ll show the per-operation cost of their unified model versus the per-task pricing of traditional platforms without accounting for actual usage patterns.
Here’s what matters: measure your actual usage for one full billing cycle before making any decisions. Include every API call, every workflow execution, every credential rotation event. Then calculate two scenarios with that real data—staying with your current setup versus consolidating.
From what I’ve seen in practice, consolidation saves money primarily when you have high workflow volume and use multiple AI models within those workflows. If your usage pattern is sparse or concentrated in one or two models, the savings might not justify the migration effort. The break-even point typically comes between months three and six post-migration.
We consolidated 12 subscriptions last year. Real savings were about 38% after accounting for migration time. The math works if you’re above a certain usage treshold, but most companies overstimate their baseline costs.
We ran into this exact problem at my company. The real issue wasn’t just the subscription costs—it was the complexity of managing fifteen different API keys, each with its own rate limits, billing cycles, and support channels. Adding a new AI capability meant adding another vendor relationship.
What changed for us was switching to a platform with unified AI pricing. We went from paying separately for OpenAI, Anthropic, Google’s models, plus a bunch of smaller ones, to accessing all of them through one subscription. The actual cost dropped about 38%, but more importantly, the operational overhead disappeared.
Now when we need to try a different model for a specific workflow, we just swap it in. No new API keys, no new billing relationships, no credential rotation nightmares. We can focus on building automation instead of managing subscriptions.
If you want to see how this actually works in practice, check out Latenode. They’ve got the 400+ model access built in, and their pricing is execution-based rather than per-operation, so your costs actually scale with what you’re doing, not with how many tasks you’re triggering.