I’m doing a workflow platform audit right now, and I’m noticing something that bugs me. We run n8n for some automations and Zapier for others, but we also have separate subscriptions to OpenAI, Claude, Azure OpenAI, and honestly I’ve lost track of what we’re actually using.
Some of those subscriptions are tied to specific n8n workflows, some are tied to Zapier integrations, and some are just there because someone needed them for a project six months ago and nobody ever canceled.
What I can’t figure out is whether we’re actually optimizing for cost or if we’re just sustaining complexity. Like, are we paying subscription costs on models we barely use? Is there a better way to consolidate this without losing the flexibility to use different models for different tasks?
And here’s the real question: if we’re using, say, OpenAI 60% of the time and Claude 20% of the time and the rest occasionally, are we better off keeping these separate or consolidating? I can’t find anyone talking about this scenario realistically.
Yeah, we were exactly where you are. Separate subscriptions to three different AI services, n8n in the middle, and honestly no clarity on what was actually getting used and what was just costing money.
First thing we did was audit actual usage. Turned out we were paying for Claude but using it maybe 15% of the time. OpenAI was our workhorse. The others were there for edge cases that came up maybe once a month.
The revelation was that subscriptions hide what you’re actually using. You pay the fee whether you use 10% of your quota or 100%. So we were paying for breadth we didn’t need.
We consolidated to OpenAI primary and Azure OpenAI backup for regulatory stuff. Cut costs by about 40% and honestly, workflows didn’t suffer because our actual usage patterns meant we rarely hit the edge cases where the other models were necessary.
Then we rationalized n8n to only run the workflows that were actually using the models we kept. Everything else we migrated to Zapier or just removed because it was legacy.
The lesson was: consolidation isn’t about having fewer options. It’s about matching your actual usage patterns to your subscription portfolio.
I’d also suggest looking at whether the problem is actually the subscriptions or the platform setup. Like, we kept n8n and Zapier because they do different things well. But we were using different AI models with each one, which meant managing subscriptions across both platforms.
Once we standardized on platforms and then picked the most-used AI models, the admin burden dropped way down. We’re paying for fewer things but getting better outcomes because there’s less overhead managing all the integrations.
Audit first, consolidate second. Log actual API usage for 30 days across all your AI subscriptions. Track which model is used where, how often, and what it’s doing. You’ll probably find that 80% of your actual usage comes from 20% of your subscriptions, and the rest is just sitting there.
Once you have that data, the decision gets easy. Keep what you’re using, consolidate the rest, and set a policy to cancel anything that doesn’t get used for 60 days.
The bigger picture question is whether separate AI subscriptions make sense alongside n8n or Zapier. Usually they don’t. A platform that bakes in AI model access means you’re not managing separate subscriptions at all. That’s where a lot of cost optimization actually happens.
Separate AI model subscriptions only make sense if each one is used for a specific, high-value purpose and subscription costs are justified by that usage. If you’re using them as options or backup, you’re paying for optionality you don’t actually value.
The business case for consolidation usually looks like this: audit usage for a month, identify which models are actually driving value, calculate the cost of keeping everything versus consolidating to what you actually use. Consolidation usually saves 30-50% when you cut out the “just in case” subscriptions.
For platforms like n8n, the model integration happens at the n8n level. You’re managing subscriptions through n8n’s configuration, so consolidating AI models doesn’t mean losing flexibility. It means paying for what you use instead of what you might use.
The real risk with consolidation is locking into a model that doesn’t fit your edge cases. That’s worth 20-30% overpayment to maintain optionality. Anything beyond that is waste.
We had the exact same situation. OpenAI, Claude, Azure, plus trying to manage all of this through n8n meant separate subscriptions and separate billing relationships. We were definitely overpaying.
What changed it was moving to a platform that included access to multiple AI models under one subscription. So instead of tracking five separate API quotas and renewal dates and billing contacts, we had one subscription and built workflow automation and orchestration on top of that.
The financial impact was immediate. Consolidated from five subscriptions down to one, cut overall costs by about 45%, and honestly had more model flexibility because we weren’t limited by what subscriptions we’d individually contracted for. We could use any model that made sense for the workflow without worrying about whether we’d paid for access.
The administrative burden just disappeared. No more managing separate API keys, no more juggling renewal dates, no more debate about whether to keep a subscription we used sporadically.
This is exactly the kind of thing that platforms with built-in access to multiple AI models solve. You get the breadth of model options without the complexity and cost of managing separate subscriptions.