I keep seeing the pitch for unified AI platforms: access to 400+ models through a single subscription. The appeal is obvious—no more juggling APIs, one simple pricing model, whatever.
But I’m trying to understand whether that actually matters for a BPM migration specifically. When you’re moving workflows from Camunda to an open-source platform, does having optionality across hundreds of models change your technical approach or timeline?
My instinct is that most workflows probably don’t need 400 model options. They need maybe two or three that you pick based on cost and performance characteristics, and then you stay with those. For standard business processes, you’re not going to redesign your workflow architecture around model variety just because it’s available.
But maybe I’m missing something. Maybe workflows that could only work with expensive GPT-4 suddenly become viable with cheaper alternatives if you have options. Or maybe having access to specialized models opens up functionality that wasn’t feasible before.
I’m also wondering whether the optionality creates decision paralysis or implementation complexity. If your teams are used to working with one or two AI services, giving them 400 options might actually slow down decision-making instead of speeding it up.
For a migration context specifically, does optionality across models actually influence your migration strategy, or would you end up using the same two or three models regardless?
We had access to multiple models during migration, and honestly, optionality mattered less than we expected for the migration itself. We picked models based on cost and latency characteristics and stuck with them. The real value wasn’t in having 400 options available.
But what did change our strategy was being able to experiment. When we were deciding how to automate a data validation step, having access to different models cheaply meant we could test a few approaches without worrying about API costs spiraling. That experimentation reduced risk in the migration because we validated approaches before committing to them.
So for migration specifically, optionality doesn’t change your strategy, but it changes your confidence in the decisions you make. You’re not locked into your first architectural choice because exploring alternatives is cheap enough to justify.
Long-term, after migration, optionality matters more. You can optimize workflows over time by testing different models and keeping what works best. But during migration, you’re in survival mode—you’re not optimizing, you’re moving functionality from point A to point B and validating it works.
One workflow-specific case where optionality mattered: we had a validation process that needed both language understanding and structured data processing. With limited models, we’d probably built a complex prompt or delegated part of the logic to custom code. With access to multiple models, we used one model for language understanding and another specialized model for structured processing. That was cleaner architecturally and cheaper to run than the alternative approaches would have been.