We’ve been running Make for about two years now, and I finally sat down to actually calculate what we’re spending on AI integrations. It’s worse than I thought.
Right now we have:
OpenAI subscription ($20/month)
Anthropic direct access ($100/month)
Cohere through Make’s integration (unknown tier, hidden in our Make bill)
Hugging Face tokens (sporadic usage, adds up)
Then there’s the cost of managing all these separate API keys, the security overhead, and honestly the developer time spent just keeping credentials rotated and documented.
When I add it all up, we’re probably doing $200-300/month just on the model subscriptions alone. But that doesn’t include the cost of maintaining separate integrations in Make, handling authentication across multiple services, or the pain when one provider has an outage.
I keep hearing that consolidating to a single subscription for AI models could simplify this, but I want to understand what the actual financial impact would be before pitching it to leadership. Has anyone actually done the math on this? What does your TCO look like when you’re comparing platforms now?
Yeah, we went through this exact exercise last year. The per-model approach was bleeding us dry because we weren’t actually using most of what we paid for.
What changed for us was realizing we were paying monthly minimums on services we only used sporadically. With OpenAI, Anthropic, and a couple others, you’re basically committed to the base tier even if your usage varies wildly month to month.
The bigger thing though wasn’t just the subscription costs. It was the operational overhead. Every new model meant new API key management, new rate limit handling in our workflows, new authentication logic. That time adds up fast.
We actually did something similar and found that the math changes significantly once you account for the people cost. One engineer spent about 20% of their time just managing integrations and keeping documentation current. That’s easily $15-20k a year in salary time.
When you consolidate access through a single subscription that handles authentication centrally, you eliminate a lot of that friction. We went from three platforms to one and cut that overhead almost entirely.
The per-model subscription trap is real. We were in the same situation until we looked at unified platforms. What we found was that many alternatives actually give you access to multiple models under a single contract. This changes the financial picture significantly because you’re no longer managing separate vendor relationships, separate invoices, or separate authentication schemes.
From a TCO perspective, consider factoring in not just the direct subscription costs but also the developer time for integration management, the security overhead of managing multiple API keys, and the cost of context switching when dealing with different rate limit structures and authentication approaches.
When evaluating the real cost, you need to include several factors beyond just the subscription fees. First, there’s the direct cost of multiple subscriptions. Second is the operational complexity of managing different API limits, authentication schemes, and billing cycles separately. Third is the developer time spent on integration maintenance and debugging issues across multiple systems.
In our case, consolidating gave us better predictability on costs and freed up resources. The financial case became clear when we actually calculated the fully-loaded cost per workflow, including the infrastructure time.
I’ve been managing the same fragmented situation for a while, and it finally became clear that we needed a better approach. The real issue isn’t just the subscriptions, it’s the operational tax they create.
What changed for us was moving to a platform that gives access to 400+ AI models through a single subscription. Sounds like marketing speak, but the actual impact is significant. Instead of managing OpenAI, Anthropic, Cohere separately with their own authentication and rate limiting, we just pull from one unified pool.
The cost math becomes much simpler: one invoice, one authentication flow, one rate limit to manage across all models. We cut our AI subscription costs by about 35% and eliminated the integration maintenance overhead almost entirely.
The other benefit nobody talks about is flexibility. We can experiment with different models without provisioning new accounts and waiting for approval. A data analyst can literally switch between Claude and Deepseek in a workflow without engineering bottlenecks.