We’re at that point where our finance team is asking hard questions about switching from our current BPM stack. They want to know the real total cost of ownership, and honestly, I’m not sure how to frame it fairly.
Right now we’re paying for individual AI model subscriptions, licensing fees for our BPM platform, and then there’s the infrastructure cost of keeping everything running. When I look at alternatives, I see options ranging from self-hosted open-source solutions to platforms that bundle everything under one subscription.
The issue is that every option looks different on a spreadsheet. One approach charges per execution, another charges per workflow, and the open-source route makes you pay for your own DevOps overhead. I’ve been reading about how consolidating multiple AI models under a single subscription can reduce vendor management complexity, which should theoretically lower costs, but I can’t figure out how to measure that in a way that doesn’t sound like I’m making it up.
Has anyone actually built a TCO comparison that accounts for:
Licensing fees across different vendors
The hidden cost of switching between platforms
Time spent managing integrations and subscriptions
What happens when you consolidate 400+ AI models into one plan versus juggling separate contracts
What methodology do you actually use when you’re comparing these options with your own team?
The way I approach this now is thinking about it in three buckets: platform licensing, infrastructure, and overhead cost.
Platform licensing is straightforward - it’s the subscription fees. But infrastructure is where open-source gets you. You’re paying for servers, monitoring, backups, and someone’s time to manage it. A lot of companies underestimate this when they’re attracted to the idea of “free” open-source software.
Overhead cost is the thing nobody talks about until it’s too late. When you’re running multiple subscriptions for different AI models and integrations, your team spends time managing those relationships. Tracking which model is best for which task, updating credentials, handling billing disputes. If you can consolidate that to one dashboard, it’s not a huge savings per month, but annualized it adds up.
For our comparison, we found that the per-execution pricing model actually worked better for our workload because we have spiky usage. Some days we’re running hundreds of workflows, other days it’s minimal. When you’re paying a flat fee regardless of usage, that flexibility matters.
The honest answer is that your TCO is going to be unique to how you actually use automation. What worked for us might not work for you.
I think you’re overcomplicating this. Start by building a baseline of what you’re currently spending across all your tooling - that’s your benchmark. Then model what each alternative would cost for your actual workload, not theoretical usage.
The key insight is this: most platforms have different cost structures, so you can’t just compare monthly fees. You need to compare cost-per-workflow or cost-per-execution. For open-source BPM specifically, don’t forget that you’re buying infrastructure time. That’s usually the killer for small to mid teams because DevOps labor isn’t cheap.
One thing that helped us was realizing that consolidating AI models under one subscription meant we could actually experiment more without worrying about spinning up new API contracts. That experimentation value is harder to quantify but it’s real - faster iterations, fewer approval processes, less procurement overhead.
Take one month of realistic workflow data and run it through each platform’s pricing calculator. That gives you a grounded comparison instead of guessing.
The fundamental challenge with TCO comparison across these platforms is that they’re optimized for different usage patterns. A per-execution model rewards efficiency and lighter workflows. A flat subscription rewards high-volume, consistent work. Open-source rewards teams with strong infrastructure capabilities.
What you should measure is cost-per-business-outcome, not just cost-per-feature. If your workflows process customer orders, the TCO should include the cost of errors, latency, and downtime - not just the platform fees. A platform that costs more but has better reliability might actually be cheaper in total cost.
The consolidation of 400+ AI models under one subscription is worth examining carefully. The vendor lock-in risk is lower because you’re not dependent on any single AI provider - you have choices. But the switching cost to get there might be significant if you’re currently optimized around specific models or workflows.
I’d recommend building three scenarios: best case adoption, realistic adoption, and worst case. Run your actual workload patterns through each. The spread between these scenarios will tell you how much risk you’re carrying in your TCO estimate.
What you’re dealing with is the complexity tax - every extra vendor you manage adds documentation overhead, credential management, and integration points that can fail independently.
With Latenode, this actually simplifies because you get 400+ AI models under one subscription. No more juggling separate OpenAI accounts, Claude credits, Gemini quotas. Everything is in one place with unified billing and authentication.
From a TCO perspective, what matters is that your infrastructure cost goes down when you eliminate vendor management overhead. The platform itself uses execution-based pricing, which means you pay for what you actually use - no waste on unused monthly allocations. We’ve seen teams cut their automation costs by 40-60% compared to platforms that charge per task or per workflow.
The consolidation of AI models is huge for TCO because it removes the hidden cost of vendor switching. You’re not locked into one model - you can experiment with Claude for analysis tasks and GPT for creative work, all without changing subscriptions or renegotiating contracts.
If you want to actually compare apples to apples, model your workflows on a platform that consolidates your entire automation stack. You’ll see the TCO difference immediately.