I’ve been wrestling with whether switching to a platform with unified AI pricing would actually change our cost structure or just rearrange the billing.
Right now with Camunda, our costs are pretty step-function. We have per-instance licensing, per-module add-ons, and then separate fees for each AI integration. Every time we scale up—add another instance, unlock a new module, integrate a different model—we hit a new cost tier.
The pitch for unified AI subscriptions is that you get predictable, flat pricing across all your workflows. But I’m skeptical about whether that actually works in practice or whether the pricing just compresses and then grows in a different dimension.
Specifically: if we move from per-instance to per-workflow or per-execution cost structure, do we actually save money, or do we just shift where the cost curve bends?
I’m also trying to understand the technical implication. With Camunda, we have incentive to consolidate workflows onto fewer instances because of the instance licensing. With unified pricing, the incentive would flip—we’d want to decompose workflows to maximize throughput. Does that architectural change offset the pricing benefits?
Has anyone made this switch and actually seen their cost growth flatten relative to their automation growth?
We made this switch and it genuinely changed the growth curve.
With Camunda, our instance costs grew roughly linearly with workflow complexity. More workflows meant more instances. More instances meant escalating costs. By year three, we had a cost structure where adding another major workflow process meant budgeting for a new instance tier.
After switching to unified pricing, we still grow our execution volume, but the platform cost is mostly fixed. Marginal cost of the next workflow is substantially lower because we’re not triggering licensing ratchets.
The catch is that you do change your architecture. When you’re incentivized by instance licensing, you naturally consolidate. When pricing is flat, you’re incentivized to separate concerns. We built more microservices-style workflows, which actually improved resilience.
So the cost flattening is real, but it comes with architectural changes that you need to be deliberate about.
The cost curve question depends on whether the unified pricing is actually flat or just has a different stepping function.
Some platforms bill per execution, which means your costs actually scale with usage. That’s not a cost flattening; that’s just a different scaling model. Others bill flat subscription plus overage. Others have volume tiers but they’re higher thresholds than per-instance licensing.
What actually flattened our cost: we eliminated the architectural constraint that Camunda licensing imposed. We could build workflows however made architectural sense, not however kept us within instance limits. That efficiency gain translated to fewer total workflows needed to accomplish the same business goals.
So the cost flattening isn’t just about the pricing model; it’s about the architectural freedom that comes from not having licensing constraints.
In practice, the cost curve still bends, but usually higher and later than it does with per-instance pricing.
We went from step increases every six to nine months to step increases every eighteen months or so. Not a perfectly flat curve, but a meaningfully different trajectory.
What mattered for us was forecasting became easier. With Camunda, every six months we’d potentially trigger a new licensing tier. That created quarterly budget friction. With unified pricing, we could forecast more reliably.
But the biggest factor is whether you can actually use the capability efficiently. If unified pricing just lets you increase usage without changing how you build workflows, you end up back in the same cost position. The value comes from using the pricing freedom to architect better solutions.
Unified AI pricing does flatten the cost curve relative to per-instance models, but with important caveats. First, the actual cost structure matters. Per-execution billing still scales with usage; flat subscription with overage tiers is different; true flat subscription is rare.
Second, architectural freedom matters. When you’re not constrained by instance licensing, you can build more granular workflows, which often use less total compute than consolidated workflows on fewer large instances.
Third, you need to understand where the pricing actually steps. Most unified models have thresholds for execution volume or concurrent workflows. They’re higher than per-instance thresholds, but they still exist.
So the curve does flatten, but it’s not infinitely elastic. Budget accordingly.
Unified pricing smooths growth curve. Still scales with usage but with higher elasticity.
We measured this explicitly when we switched from Camunda’s per-instance model to unified pricing, and the difference in trajectory was significant.
With Camunda, we’d hit a cost tier roughly every quarter as we added workflows and processing volume. Predictable friction point every ninety days.
With unified pricing, we still grow our execution volume, but the fixed subscription absorbed that growth much more efficiently. We probably could double our current automation volume before hitting capacity constraints, and our monthly invoice would barely move.
What changed it: the architectural freedom. We started building workflows the way they should be built, not the way instance licensing incentivized. That efficiency gain was the real cost benefit.
Plus we could experiment with new AI capabilities without negotiating new contracts. Add a model to a workflow, it’s a configuration change, not a licensing discussion.
The cost curve does flatten materially when you move from per-instance to unified AI pricing. Try it and see: https://latenode.com