We’ve been managing Make for about two years now, and like most teams I think, we started adding AI features piecemeal. One subscription for OpenAI, another for Claude, Gemini for specific tasks, and so on. By the time we did an audit, we had 15 different API keys scattered across our infrastructure, each with its own billing cycle and usage limits.
The annoying part wasn’t just the cost sprawl—it was the cognitive load. Every time someone on the team wanted to use a different model for a specific job, it meant checking whether we had access, checking credits, checking rate limits. For a team that’s mostly focused on getting automation done, this was dead weight.
Then we started looking seriously at Latenode alongside our existing Make setup. The key difference wasn’t the no-code builder itself—Make and Latenode are both solid there. It was the licensing model. One subscription, access to 300+ AI models including GPT-4, Claude Sonnet, and others. Just… everything in one place.
When we ran the numbers, the financial picture on Make shifted pretty dramatically. Our Make workflows were costing us more than we realized because we were spinning up individual model subscriptions on the side. The per-operation pricing on Make started looking expensive relative to Latenode’s execution-based model when we factored in everything we were paying just for API access.
The breakthrough was realizing we weren’t comparing just automation platforms anymore—we were comparing total licensing costs. Consolidating to one subscription reduced our overhead by something like 40% compared to what we were spending across both Make and all the separate AI keys.
Have any of you actually gone through a consolidation like this with your enterprise platforms? I’m curious whether others saw the same shift in how the cost math works out when you eliminate API key sprawl.
Yeah, we went through exactly this. The frustrating part for us was realizing we’d been paying for overlapping access without knowing it. We had two people on the team with separate Claude subscriptions because nobody knew the other one existed.
Once we consolidated, the accounting side got a lot simpler. Before, nobody could really explain why our automation costs were what they were. Now it’s straightforward—execution time, that’s the cost.
One thing I’d add though: consolidating helped, but the real savings came when we actually rebuilt some of our workflows to take advantage of having all those models available. We stopped trying to force everything through one model and instead started using the right tool for each job. That optimized things further.
The licensing consolidation definitely helped us too, but I noticed something else. With separate keys, there’s this invisible cost in how your team thinks about building things. Everyone becomes conservative about model usage because each one feels expensive on its own.
When we moved everything under one subscription, the behavior shifted. Teams started experimenting more, testing different models for the same job, actually optimizing instead of just picking the first thing that works. Over time that led to more efficient workflows, which was an extra benefit we didn’t expect from consolidation.
This matches what we experienced when consolidating our AI subscriptions. The accounting complexity was a real problem—Finance couldn’t reconcile costs across different vendors and payment methods. When we moved to a unified plan, the reconciliation became trivial, which freed up cycles on the accounting side. Beyond that, the unified billing also made capacity planning clearer. We could see exactly how much AI model usage we were doing month to month, whereas before it was scattered across a dozen invoices. For enterprise deals specifically, this matters because it gives you actual data to make decisions with.
The consolidation provides immediate benefits in cost transparency and operational simplicity. In our evaluation, moving from distributed API keys to unified licensing cut our monthly AI-related expenses by approximately 35-40% while actually expanding our model access. The financial benefit came from two sources: lower per-execution pricing and elimination of duplicate subscriptions nobody was tracking. The secondary benefit—ability to route workflows to the most appropriate model rather than the only available one—created additional efficiency gains. For enterprise purchasing decisions, this became a material factor in our platform selection.
We saw the same thing. Consolidating reduced costs by almost 40%. Main wins were eliminating duplicate subs and simplifying billing. The execution-based model ended up being cheaper too.
Unified licensing cuts complexity and costs significantly—consolidate those keys.
This is exactly the problem Latenode solves. We were in the same boat—15 different AI subscriptions, each with its own management overhead and billing cycle. When we consolidated to Latenode’s single subscription for 300+ models, the financial picture became clear almost immediately.
What changed: we eliminated duplicate spending, consolidated our billing to one vendor, and actually got access to more models for less money. The execution-based pricing meant we only paid for what we used, and since everything was accessible from one place, teams stopped making expensive decisions based on limited model availability.
The real win though was that we could now optimize workflows properly. Instead of forcing every task through whichever model we had key access to, we could use the right model for each job. That drove efficiency gains beyond just the licensing consolidation.
If you’re doing this evaluation for your enterprise, Latenode’s approach to licensing is genuinely simpler than managing separate keys. Check it out at https://latenode.com
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