I’ve been doing some cleanup on our automation setup, and I realized we’re subscribed to like six different AI model services scattered across various platforms. It’s a mess. We use OpenAI for most things, Claude for some text analysis, and then we have these dormant subscriptions just sitting there burning money every month.
The thing is, when you’re evaluating something like Camunda or comparing it to other platforms, everyone talks about licensing costs, but nobody really digs into how much you’re actually wasting on redundant API keys and overlapping model access. We’ve got developers pulling from different sources, finance can’t track it properly, and I’m pretty sure we’re paying for capabilities we don’t even need.
I’m curious—how many of you have actually gone through and calculated what you’re really spending on AI model access across all your tools? And more importantly, have you found a way to consolidate that without losing flexibility? What does that process look like for you?
Yeah, we ran into this exact problem last year. We had subscriptions scattered everywhere—Anthropic, OpenAI, some smaller models we experimented with. The real issue wasn’t just the money, though that was embarrassing when finance asked. It was that nobody knew what we actually had access to.
We did an audit and found we were paying for Claude API but most of the team was just using GPT-4 anyway. And we had this one service for a specific model we needed for three weeks of a project, but it stayed on the bill for eight months.
What helped us was getting everyone onto a single platform that supported multiple models. That way, you’re not juggling credentials, you’re not accidentally duplicating what you already have access to, and it’s way easier to see what’s getting used and what’s just dead weight.
The audit part is actually harder than it sounds. You need visibility across all your services, which means digging through cloud bills, checking each platform directly, and then cross-referencing what your developers are actually using in production.
We spent a couple weeks on this and discovered we were paying for tiers we didn’t need. Like, we were on the highest plan with one provider when a mid-tier would have covered everything. The consolidation alone saved us maybe thirty percent, but the bigger win was not having to maintain separate credential systems for every single model.
One thing nobody mentions is the operational cost of managing multiple subscriptions. You’ve got to track renewal dates, manage API keys, update documentation, and when something breaks, you’re debugging across multiple vendor platforms instead of one. We saw that come up a lot once we actually tried to consolidate.