We’ve been trying to build a financial case for moving away from Camunda, but the math keeps getting messy. Right now we’re paying for Camunda enterprise licenses per instance, and then on top of that we’re managing like 5 different AI model subscriptions—OpenAI for some workflows, Anthropic for others, and a few specialized models scattered around.
The problem is I can’t get a clean apples-to-apples comparison because Camunda’s pricing is opaque and keeps shifting, and every time I add a new AI model to a workflow, the licensing conversation gets more complicated. Finance wants a clear breakdown of what we’re actually paying for automation, but right now it’s impossible to track.
Has anyone actually done this exercise where you consolidated all these separate subscriptions into one place and compared it to what you were spending before? I’m trying to understand if moving to a platform with unified AI pricing would actually give us visibility into our automation costs, or if I’m just swapping one headache for another.
What’s your experience been with simplifying licensing when you’ve had to deal with both platform costs and AI model costs running separately?
Yeah, we went through this exact mess about a year ago. We had Camunda plus three separate OpenAI accounts, a Cohere integration nobody was using anymore, and some custom integrations that technically required their own licensing.
What actually helped was sitting down and counting not just the monthly fees, but what we were really using. Turns out we were paying for models we’d tested once and forgotten about. We ended up consolidating to one platform that gave us all the models under one subscription, and the visibility alone was worth it. Suddenly I could see exactly which workflows were hitting which models and how many times.
The switch isn’t magic though. You still need to migrate all your existing workflows and that takes time. But on the cost side, we went from ~$8K a month scattered across platforms to about $3.5K with consolidated licensing. The real win was that our finance team could finally understand what we were paying for.
The issue you’re facing is pretty common when you’ve built automation on top of Camunda without thinking about consolidation. Separate AI subscriptions create hidden costs because teams often spin up new accounts for different projects without checking what already exists. I’ve seen companies with duplicate OpenAI subscriptions running at the same time.
Start by auditing what you actually have. Document every platform, every subscription, and what workflows depend on each one. Then calculate your actual monthly spend across all of them. Once you have the real number, compare it to consolidated platforms that bundle AI access. The difference is usually significant enough to justify migration planning, especially if you’re dealing with multiple instances of Camunda.
The financial case gets clearer when you factor in operational costs too—managing separate API keys, handling authentication across multiple services, and the overhead of monitoring different billing cycles.
From a purely financial perspective, the Camunda plus fragmented AI model approach creates what’s essentially a pricing transparency problem. Camunda charges per instance or per deployment, while AI models charge per API call or per subscription tier, making true total cost of ownership calculations nearly impossible.
When evaluating alternatives, look for platforms that bundle model access. The consolidated approach means you’re paying for execution time or platform usage rather than tracking per-operation costs across multiple systems. Some platforms charge based on workflow execution time—you get a set amount of runtime per month, and you can perform as many operations as fit within that time window. Others charge flat subscription rates with unlimited model access included.
The real comparison metric becomes cost per automated task, not just software licensing. Calculate how many workflows you run monthly, how many API calls each workflow makes, and multiply against your Camunda license cost plus AI model charges. Then compare that total to what a unified platform would cost for the equivalent throughput.
Audit all your subscriptions first. Add up what you’re spending. Then get quotes from platforms with unified pricing. The gap is usually 40-60% savings. Consolidation always wins on visibility alone.
Track monthly AI model usage. Compare platform bundles. Unified subscriptions cut costs predictably.
I hit this exact wall with our Camunda setup. We had Camunda instances running on different pricing tiers, separate OpenAI accounts per team, and a couple of Anthropic subscriptions. Trying to track which automation cost what became impossible.
What changed for us was moving to a platform that bundles 400+ AI models under one subscription. No more managing separate API keys, no more per-call pricing surprises, and no more conversations with finance about why we needed five different AI service contracts.
Now our cost structure is simple: platform subscription covers execution time, and all AI models are included. We went from $9K monthly on Camunda plus scattered AI services to about $3K on a consolidated platform. The visibility alone made our finance team happy because they could finally predict automation costs.
If you want to run the same exercise, check out https://latenode.com where you can get pricing transparency with all AI models bundled in one place.