I’ve been tasked with figuring out our automation spend, and it’s been a mess. We’re currently running Camunda for our core workflows, but we’ve also got separate subscriptions to OpenAI, Anthropic, Deepseek, and a few others just to handle different tasks across different teams. Each one has its own billing cycle, its own API key management, its own documentation rabbit hole.
The finance team looked at our invoices last quarter and basically said “this has to stop.” So I started digging into what consolidating to a single subscription model would actually look like.
Right now, we’re spending roughly $2,400 a month across all our AI model API subscriptions alone. Then on top of that, we’re paying for Camunda licensing, which scales with the number of process instances we run. We’ve got developer time spent just maintaining integrations between our workflow engine and all these different AI providers. And there’s the hidden cost of context switching—teams waiting for different approval cycles because different tools are integrated at different times.
I’ve been looking at some numbers that suggest you can actually do quite a bit with a single unified subscription covering 400+ AI models. The execution-based pricing model they use means you’re not paying per operation—you’re paying for the time your workflows actually run. That’s a different beast entirely.
Has anyone actually made this transition? Not the sales pitch version, but the real version—where you’ve actually consolidated your AI spending and could speak honestly about whether it actually moved the needle on your total cost of ownership?
Specifically: how did you handle the transition period where you couldn’t just flip a switch? What actually broke or required rework? And did the consolidation savings actually materialize, or did you end up spending more because you kept both setups running in parallel for months?
We did this about eight months ago. The transition was painful, not gonna lie.
The real issue wasn’t the switch itself—it was the handoff period. We couldn’t just turn off our old subscriptions overnight because we had workflows in production that depended on specific API endpoint behaviors. Some of those AI models had quirks we’d built around.
What actually worked: we picked our lowest-risk workflows first and migrated those. We kept both systems running for about six weeks while we validated that the new setup was handling the load the same way. That overlap cost us money, yeah, but it meant we weren’t gambling with critical processes.
The math came out in our favor pretty quickly though. Once we were fully transitioned, we were running about 65% lower on the AI model side of things. Camunda licensing was a separate conversation with our vendor, but the savings from consolidating the models was enough to get finance to sign off on a three-year contract.
What helped most: having a clear picture of which workflows were using which models before we started. We actually graphed out the dependencies. Took us two weeks, but it saved us from making assumptions that would’ve bitten us later.
The consolidation worked for us because we approached it differently than I think most teams do. Instead of trying to migrate everything at once, we created a gateway layer that abstracted which AI provider each workflow was actually talking to. Sounds fancy, but it was just a simple mapping system.
That meant we could point old workflows at the new consolidated platform gradually, test the outputs against what we’d been getting before, and only move forward when we were confident the results were the same.
On the cost side, we were at about $1,800 a month across OpenAI and Anthropic alone. The comprehensive model subscription brought that to about $600 a month. The savings weren’t immediate though—we spent three months in that parallel period, so we actually spent more in the short term. But our payback was less than four months once we cut over completely.
The thing nobody talks about is the operational simplification. One billing contact, one support channel, one API key rotation schedule instead of five. That’s worth something too, even if it’s hard to quantify.
We calculated TCO by breaking it into three buckets: direct costs, operational overhead, and opportunity cost of developer time spent managing multiple integrations.
Direct costs were straightforward—all the subscriptions added up. Operational overhead included the time spent managing API keys, handling rate limits across different providers, and maintaining documentation for teams on which tool to use when. We tracked that for a month and it was shocking. One engineer was spending about eight hours a week just managing API authentication and failover logic across our different tools.
Opportunity cost: when a developer needs to integrate a new AI capability into a workflow, how long does it actually take if they have to figure out which service to use, find the right API docs, and handle the integration? We tracked that too. Average was about four hours per new capability.
Once we looked at it holistically, the math was clear. The unified subscription cut our direct costs nearly in half, but the real savings came from freeing up that eight hours a week of engineering time. Spread that across our salary costs and it was almost half our annual spend again.
We did the same consolidation with Camunda licensing—negotiated a volume contract based on guaranteed process instances. Combined savings were close to 50% on the infrastructure side.
migrated 6 workflows last year. costs went from $2100/month to $750. took 3 months of parallel running, but worth it. biggest win was actually less api management overhead, not just the $ saved.
This exact scenario is where the unified model subscription approach really shines. The execution-based pricing means you’re not getting charged per API call or per model instance—you’re charged for the time your workflow actually runs, which completely changes the economics.
What we’ve seen with teams making this switch is that the direct cost savings from consolidating eight different subscriptions into one is obvious. But the operational win is huge too. One platform, 400+ models, no juggling API keys. Your developers spend their time building workflows instead of managing authentication layers.
The part that doesn’t get talked about enough though: templates and reusability. Once you’re in one platform, building from templates means your next automation is faster and cheaper. The leverage compounds.
If you’re evaluating this consolidation, the math tends to work out in months, not quarters.