I’ve been tasked with figuring out if we can consolidate our AI licensing, and it feels like a puzzle with a thousand pieces. Right now we have:
OpenAI (ChatGPT and GPT-4)
Anthropic (Claude)
Deepseek
Cohere
A couple of smaller model providers we use for specific tasks
Plus all our n8n self-hosted licensing and infrastructure
Each one has its own API key, its own documentation, its own failure modes. What worries me is that these models aren’t always interchangeable. Some workflows require Claude for specific reasoning tasks. Others need GPT-4’s vision capabilities. I can’t just swap them around without breaking things.
The appeal of a centralized approach is obvious—one contract, one billing cycle, one interface. But I’m skeptical about whether that’s actually possible without ending up in a situation where we have to rewrite dozens of workflows to fit into a new system.
Has anyone actually done this migration? What broke? What didn’t? How long did it actually take to get everything stable on the new system? I’m trying to figure out if this is a legitimate cost-saving move or if we’re just trading one set of headaches for another.
We went through this migration last year, and the honest answer is it depends on how tightly coupled your workflows are to specific models.
Here’s what I’d recommend: audit your workflows by model dependency. We found that about 60% of our use cases could actually work with multiple models. The remaining 40% had specific reasons they needed Claude or GPT-4 or whatever.
That breakdown changed everything about how we thought about consolidation. Instead of trying to migrate everything at once, we focused on standardizing the 60% first. That gave us quick wins and built confidence for tackling the harder stuff.
The migration itself took us about six weeks from start to finish, but that included testing and validation. Three weeks of actual work, honestly. The biggest pain point wasn’t the technical side—it was coordinating across teams to make sure we weren’t breaking someone’s workflow without knowing it.
The key insight most teams miss is that consolidation doesn’t mean uniformity. You can still use different models for different tasks within a unified platform. The consolidation benefit comes from having a single interface and contract rather than swapping models randomly.
What actually matters is whether your primary workflows can handle model flexibility. Vision tasks, yes, usually need specific models. But a lot of text-based automation is more flexible than people think. The question isn’t “can we use only one model?” It’s “can we reduce from fifteen contracts to one contract while staying performant?”
Most companies find the answer is yes, though they often keep a couple of specialized subscriptions for edge cases. The bulk of their spend consolidates.
Migration risk is real, but it’s manageable if you approach it systematically. Create a mapping of every workflow to its current model dependency and performance requirements. Then test alternative models against those same workflows in a sandbox environment. Document the performance differences.
That data drives your consolidation strategy. You’ll discover that some workflows are genuinely model-specific while others are just configured that way out of habit. Once you separate those, the migration becomes much lower risk because you’re only touching workflows where you’ve already validated the alternative approach.
Test model swaps in sandbox first. Most workflows tolerate alternatives. Migrate 60% standardized cases, keep specialized subscriptions for remainder. Reduces complexity 80%.
The anxiety you’re describing is because you’re trying to think about this as a single migration event. What actually works is treating it as a gradual transition where your platform handles model selection for you.
With unified access to 400+ models, you don’t have to choose ahead of time which model handles which task. You can test different models against the same workflow and see which performs best for your specific use case. That changes the whole math because you’re not locked into a decision—you can optimize as you go.
The other piece that changes is that you’re not touching your workflows to consolidate licensing. You’re just switching which back-end your workflows connect to. The workflows themselves stay stable. You’d be surprised how many failures in migrations happen because teams try to refactor everything at once. This approach lets you keep things running while you optimize.
The real win is that you eliminate the API key sprawl across your infrastructure. One connection, one token management, one set of credentials to maintain. That alone reduces operational risk significantly.