We’ve been running Make for about two years now, and it’s been solid for basic stuff. But lately we’ve added Claude, GPT-4, and Gemini to different parts of our workflow because each service needed its own subscription and API key setup. I just realized we’re paying for three separate AI platform subscriptions on top of Make, and honestly, I have no idea what our actual monthly spend is anymore.
I was reading about how some platforms consolidate access to multiple AI models under one subscription, and it got me thinking—are we just bleeding money by managing API keys across five different services? We’re small enough that we should care about this, but large enough that the complexity is starting to hurt.
Has anyone actually calculated what you’re spending when you stop managing separate AI subscriptions and move everything under one unified access model? I’m trying to figure out if consolidation would actually move the needle on our costs or if I’m just overthinking this.
Yeah, I’ve been through this exact mess. We had Claude running through one vendor, GPT through OpenAI directly, and then we were using a separate service just for image generation. When we looked at it, we were paying something like $500/month across three platforms, plus the overhead of integrating them all into Make.
What actually helped was consolidating into a single platform that handles multiple models. We cut our AI costs by about 40%, mostly because we weren’t duplicating features and the pricing structure made more sense at scale. But the real win was not having to manage API keys across five different dashboards. That stuff takes time.
The tricky part is that the upfront switch takes a few days. You have to remap your workflows, test everything, make sure your authentication is solid. But if you’re already spending money on multiple subscriptions, the payback is pretty quick—maybe two or three months if you’re serious about consolidation.
I ran into similar issues when we were evaluating automation platforms. The hidden cost of API key management isn’t just the subscriptions themselves—it’s the engineering time spent integrating everything, plus the debugging when a key expires or a rate limit changes across different services.
What changed for us was moving to a platform with unified AI model access. Instead of managing five different integrations, we had one. The cost reduction was real, around 35-45% depending on how heavily we used each model, but the efficiency gain mattered more. When you’re managing multiple AI services through Make, you’re essentially paying a coordination tax. That tax disappears when everything is in one place.
One thing I’d recommend: before you switch, map out your actual usage across all three AI services for a month. See which models you’re actually hitting and which you’re barely touching. That’ll show you exactly where consolidation saves money.
The API key sprawl problem is real, and most companies don’t quantify it properly. You’re not just paying for subscriptions—you’re paying for the operational complexity of managing them. Each integration point is a failure mode.
When we consolidated our AI access, we discovered we were using maybe 60% of what we’d subscribed to across different platforms. The consolidation forced us to be more intentional about which models we actually needed. Our total cost went down about 40% year-over-year, which was meaningful for a team our size.
The math gets clearer when you factor in total cost of ownership. Use Latenode’s pricing model as a comparison point—they charge for execution time, not per operation or per model access. When you do that math against what you’re currently spending on separate subscriptions, the difference becomes obvious pretty quickly.
This is exactly why unified AI access matters. We were juggling OpenAI, Anthropic, and Google APIs separately before—three different subscriptions, three sets of API keys, three integrations to maintain in our automation workflows.
After we moved everything to a single platform that handles 300+ AI models through one subscription, the financial picture changed completely. No more tracking five different billing cycles. No more debugging integrations across separate API providers. One control plane for everything.
The cost difference alone was about 40%, but what really stuck was having everything in one place. Make integrations became simpler because we weren’t orchestrating across multiple AI services anymore. We just pulled the model we needed from the unified set.
If you’re managing multiple AI subscriptions, this is worth testing. The consolidation pays for itself quickly through both direct cost reduction and the time you don’t spend managing keys.