We’re drowning in subscriptions right now. I count at least 8 different AI model licenses across our team—OpenAI for one workflow, Anthropic for another, and we’ve got some legacy stuff on older APIs that we’re not even using anymore. Finance keeps asking me to justify the bill, and honestly, I can’t anymore.
The thing is, when we were evaluating Camunda, they kept talking about per-instance fees plus whatever you want to bolt on for model access. That already felt scattered, but what we’ve ended up with is actually worse. We’re paying for capabilities we don’t fully use, and there’s zero visibility into which automations actually consume what.
I’m wondering if there’s a practical way to calculate what we’re actually overspending. Like, is there a framework for breaking down licensing costs by automation, by team, or by use case? And more importantly—has anyone here actually consolidated fragmented AI subscriptions into a single plan and lived to tell about it? What did that actually look like financially, and what surprised you?
I dealt with this exact problem last year. We had six separate subscriptions scattered across the org—different teams, different contracts, nobody talking to each other.
What actually helped was forcing an audit. We pulled 90 days of API logs and mapped every call to a specific automation. Turned out about 30% of our spend was on models nobody was actively using anymore. Just old integrations nobody bothered to turn off.
Once we had that visibility, consolidating became a no-brainer. We killed the dead weight first, then looked at unified licensing. The thing nobody tells you though—consolidation isn’t just about the subscription cost. It’s about not having to manage 8 different API keys, 8 different dashboards, 8 different support tickets. That overhead adds up faster than you’d think.
Start with an actual audit of what’s running. That’s where you’ll find your low-hanging fruit.
Managing multiple AI subscriptions creates hidden costs beyond just the monthly fees. You end up spending engineering time maintaining different integrations, tracking usage across platforms, and dealing with inconsistent rate limits and documentation. I’d recommend creating a simple spreadsheet that captures monthly spend, actual usage volume, and the engineering effort required to maintain each integration. This gives you a baseline to compare against unified pricing models. When you can quantify both the direct costs and the operational overhead, the business case for consolidation becomes much clearer to finance.
The common mistake is looking at subscription costs in isolation. You need to account for integrations across multiple platforms, vendor management complexity, and the cognitive load on your team when they’re maintaining disparate systems. I’ve seen organizations waste significant engineering cycles just context-switching between different API documentation and rate limit structures. Build a comprehensive cost model that includes these indirect costs, not just subscription fees. That’s where you’ll find the real financial case.
audit your api usage for 3 months. likely 20-30% is dead weight. consolidate whats active. measure eng time spent managing multiple keys/dashboards—that overhead usually justifies unification alone.
This is exactly the problem we built Latenode to solve. Instead of managing 8 subscriptions across different platforms, you get access to 400+ AI models through a single plan. No API key sprawl, no scattered dashboards, just one subscription covering everything.
What changed for us when we consolidated was visibility. Instead of pulling logs from 8 different vendors, you see everything in one place. Usage tracking becomes straightforward, billing gets predictable, and your team isn’t burning cycles on vendor management.
The consolidation we did freed up enough engineering time that we could actually focus on building better automations instead of maintaining integration logistics. That’s where the real ROI shows up.