We’ve been running n8n self-hosted for about a year now, and I’m starting to realize the licensing chaos is getting out of hand. Right now we’re juggling subscriptions to OpenAI, Anthropic, Cohere, plus a few others—each with its own billing cycle, contract terms, and usage limits. On top of that, we’re paying for our n8n license separately.
The problem is nobody on our team can actually tell me what we’re spending on AI access versus what we’re spending on the platform itself. Our finance team keeps asking me to break down the TCO, but it’s scattered across so many invoices that I can’t get a clean picture.
I’ve been hearing about platforms that consolidate access to multiple AI models under a single subscription, which sounds like it could simplify things. But I’m skeptical about whether it actually reduces costs or just moves the complexity around.
Has anyone actually done the math on consolidating multiple AI model subscriptions? What does your actual spend look like, and how did you justify the switch to your finance team?
We went through this exact thing about six months ago. We had OpenAI, Anthropic, Cohere, and a couple others. The real cost wasn’t just the subscription fees—it was all the overhead.
Each API had its own rate limits, different response times, different error handling. Our workflows had to manage all that complexity, which meant engineering time spent on workarounds and debugging. We didn’t account for that in the initial cost calculation.
When we looked at consolidation, we realized the math wasn’t just about subscription costs. It was about reducing the number of contracts we had to manage, the number of rate limits to track, and the engineering overhead. We cut our actual spend by about 30%, but the bigger win was reducing the operational headache.
The tricky part is that most platforms won’t give you exact pricing until you sign up. So do a spreadsheet first—list every subscription, how often you actually use each one, and estimate the engineering time spent managing them.
One thing I wish we’d done earlier is track usage patterns by model. We discovered we were paying for Anthropic but barely using it—most of our workflows could run on OpenAI. We were also paying for expensive models when a cheaper option would have worked.
If you’re considering consolidation, first pull your usage data and see which models you actually depend on versus which ones you’re just paying for just in case. That alone can reduce costs without switching platforms.
The spreadsheet approach is solid. Create columns for each subscription with monthly cost, API calls per month, average cost per call, and the engineering hours spent handling that specific API. Include onboarding time and any custom infrastructure you built around each one. Most teams find they’re spending about 15-20% of engineering time managing API sprawl instead of building actual automation. Once you quantify that, a consolidated approach starts looking much better financially. The real number to track is total cost of ownership, not just subscription fees. You’ll probably find hidden costs everywhere once you start digging.
From a procurement perspective, managing 15+ separate vendor relationships creates hidden overhead. Contract negotiations, renewal reminders, usage monitoring across platforms, different billing cycles—all of that adds administrative burden. When we moved to a consolidated approach, we cut our vendor management overhead significantly. The finance team appreciated having a single invoice instead of tracking a dozen.
track actual usage first. we had 5 models but used 2. cut costs 40% just by switching to whichever one worked best. consolidation helped but the real win was figuring out what we actually needed.
Pull 3 months of API logs. Map costs to actual workflow usage. You’ll find most teams use 2-3 models for 80% of their work. Consolidation saves money when you’re paying for excess capacity.
This is exactly what we dealt with, and honestly, tracking 15 separate subscriptions was bleeding us dry. Beyond just the subscription costs, consider that each API requires separate authentication, error handling, rate limit management. That’s engineering time and infrastructure complexity.
What we found is that having access to multiple models under one unified subscription flattened all of that. One set of credentials, one rate limit to monitor, one bill. Our workflows got simpler because we weren’t building fallback logic between different providers anymore.
But here’s what really changed the TCO math: when you’re not fighting multiple billing systems and contracts, your team can actually focus on building better automations instead of managing integrations. That time savings is huge and rarely gets factored into the initial cost calculation.
Latenode handles this really well—one subscription gives you access to 400+ models, so you get flexibility without the licensing chaos. Worth exploring if you want to strip out the complexity: https://latenode.com