I’ve been reading about autonomous AI teams—multiple AI agents working together on a single end-to-end process. The pitch is that you can handle way more complex business tasks by having agents specialize and coordinate. That sounds powerful, but I’m trying to understand the cost structure.
With single-agent workflows, you can predict licensing cost pretty straightforwardly. You’re running a workflow, it consumes some tokens or API calls, you get billed. But with multiple agents coordinating—an AI CEO directing other agents, various specialists handling different parts of a task—I’m not sure how the costs scale.
Does each agent require its own subscription? Do you get charged per agent per task? Is there overhead for the orchestration layer? And how does this compare to Camunda’s licensing model, where you’re paying per instance anyway?
I’m trying to figure out whether multi-agent orchestration is more expensive to run than traditional single-workflow automation, or whether the cost structure is different enough that it doesn’t matter as much.
Specifically:
How does licensing typically work when you have multiple AI agents in one workflow?
What’s the actual cost scaling difference between a single workflow and a multi-agent orchestration of the same business task?
Are there hidden coordination costs I should be aware of?
Does having a unified AI model subscription change how multi-agent costs work compared to per-model pricing?
We started experimenting with multi-agent setups about six months ago, and it’s been an interesting learning curve on the cost side.
What we found is that licensing cost doesn’t scale linearly with more agents. If you have a unified subscription model—one platform, one price—then adding more agents doesn’t automatically mean paying more. You’re just using the same subscription differently.
The thing that does cost more is token usage. When you have multiple agents in a workflow, each agent might process information, make decisions, or generate outputs. If the first agent needs 500 tokens and the second agent needs 500 tokens, you’re at 1000 tokens total. The coordination layer adds overhead. That’s where your actual costs go up—not from licensing, but from token consumption.
With Camunda’s model-by-model approach, I’d imagine this is more expensive because you might need different models for different agents, and each model has its own pricing structure. With a consolidated subscription, you’re just consuming more tokens from the same pool, which is cleaner to budget for.
The other factor we didn’t anticipate was that multi-agent setups can actually be more efficient than single workflows in some cases. Instead of one agent trying to do five different things sequentially, you can parallelize with multiple agents. That’s faster, which sometimes means lower overall token usage because you’re not re-processing context. It’s not always cheaper, but it’s not always more expensive either—it depends on how you design it.
The cost spiral happens when you’re not careful about agent communication overhead. If agents are constantly querying each other or passing context back and forth, that adds token consumption quickly. We designed our first multi-agent workflow poorly and watched the token usage double compared to what we expected. After we refactored to reduce inter-agent communication, costs came down. So the cost issue isn’t inherent to multi-agent systems; it’s about design efficiency.
With per-model licensing like Camunda, multi-agent setups are expensive because you might need multiple different models running simultaneously, and each has its own subscription cost. With a unified subscription for 400+ models, the cost structure shifts—you’re not paying for model access, you’re paying for usage. So multi-agent orchestration becomes more feasible economically. You’re trading off architectural complexity against token consumption, not licensing complexity. That’s a better trade-off.
multi-agent costs spike on token usage, not licensing, if ur on unified sub. per-model licensing makes it pricier. design matters—bad communication = higher costs. avoid redundant inter-agent calls.
Multi-agent cost scales with token usage and inter-agent communication. Watch for redundant context passing. Unified subscriptions make multi-agent feasible; per-model pricing makes it expensive.
We built out a multi-agent system for end-to-end customer onboarding, and this question was exactly what I was worried about upfront.
Turned out, the cost structure is actually simpler than I expected. Because we’re on a unified subscription covering all the AI models, adding agents doesn’t add licensing cost. What matters is token consumption. Each agent processes information and makes decisions—that costs tokens. But here’s the thing: when we parallelize tasks across agents instead of running them sequentially in one workflow, we often use fewer total tokens because we’re not re-processing the same context multiple times.
The real cost issue is poor design. If agents are constantly passing data back and forth or re-analyzing the same information, costs spike. But if you architect it right—agents work independently and hand off results cleanly—it’s actually efficient.
The licensing benefit versus Camunda is huge here. With per-model pricing, each agent might need a different model, and you’d be stacking subscriptions. With a unified model subscription, you’re just consuming tokens. Way more predictable.