We’ve been managing separate subscriptions for OpenAI, Anthropic, and a few other models across our self-hosted setup, and it’s becoming a nightmare. Every team requests access to a different model, and we’re tracking API keys across spreadsheets, deployment configs, and scattered documentation. The procurement side is asking why we’re paying for overlapping capabilities, and honestly, I don’t have a great answer.
I keep hearing about platforms that offer unified access to 400+ models under one subscription, but I’m skeptical about whether consolidating actually solves the real problem—which isn’t just cost, it’s governance. When you centralize everything, how do you maintain control over which teams can access what? And more importantly, how do you actually transition from 15 fragmented subscriptions to something consolidated without having to rebuild every workflow?
Has anyone actually done this migration? What did the transition look like, and did the cost savings justify the effort?
I went through this exact headache last year. We had about 12 different subscriptions scattered across departments, and the real problem wasn’t just the bill—it was that nobody knew what we were actually paying for. Some teams had keys they never used, others were blocked waiting for procurement.
When we consolidated, the biggest shock was realizing how much time we spent on key management alone. We had workflows with hardcoded API keys, environment variables spread across three different deployment systems, and nobody could tell me which subscriptions were actually active.
The transition itself wasn’t as painful as I expected. We set up a new unified access layer first, then migrated workflows one at a time. The key was keeping both systems running in parallel for a few weeks so we could verify each migration worked. We caught some workflows that were doing weird fallback logic that would’ve broken otherwise.
Cost-wise, we cut our AI model spend by about 40%. But the real win was governance—we finally had visibility into who was using what, and we could set actual policies instead of just hoping teams were responsible.
One thing I’d warn you about: consolidation sounds simple until you hit the edge cases. We had a few workflows that were using multiple models in sequence and relying on specific behavior from one provider that we assumed was standard. Turned out it wasn’t.
Before you consolidate, audit your actual usage patterns. We found that most of our workflows could’ve been using fewer models all along—we just had them that way because of historical decisions. Once we knew what we were actually doing, the consolidation planning became much clearer.
The governance piece matters a lot. You’ll want role-based access from day one, not added later as a patch. We built that in during our transition and it saved us from the typical “everyone gets a master key” scenario.
I’d suggest starting with an audit of what you’re actually using across those 15 subscriptions. Document which workflows depend on which models, what capabilities each team needs, and which subscriptions are genuinely duplicative. We did this and found we were paying for three different embeddings services that our teams could’ve consolidated into one.
Once you have that picture, unified platforms become much more practical. The consolidation itself should be gradual—don’t flip a switch on everything at once. Pick a less critical workflow, migrate it, run it in parallel with the old version for a week, then retire the old one. This approach caught issues early for us that would’ve been painful if we’d tried a big bang migration.
From a governance angle, the unified platform gave us better audit trails and access control than managing 15 separate API keys ever could. It’s not just about cost reduction; it’s about actually knowing what’s happening in your automation stack.
Consolidation requires careful planning around vendor dependencies and feature parity. Many teams assume all LLMs are interchangeable, but they’re not. Some models have better structured output, others excel at code generation, and specific workflows may depend on those nuances.
During our migration, we maintained a compatibility matrix—which workflows needed which capabilities, and whether alternative models could provide them. This prevented the false assumption that consolidation means everything works the same under a unified subscription.
From an infrastructure perspective, having a centralized access layer actually improves your security posture. Rather than API keys scattered across deployment configs, you have one authenticated interface. Role-based controls become enforceable, audit logging becomes consistent, and you can implement policies uniformly across teams.
Done this. Start with inventory of what ur actually using. Then pick low-risk workflows 2 migrate first. Run both systems in parallel 4 a bit b4 fully switching. Saves headaches & catches issues early. Governance layer matters—don’t just dump everyone into one key situation.
Audit usage, migrate gradually, enforce governance controls.
This is exactly what unified platforms are built to solve. Instead of managing 15 separate subscriptions and the operational overhead that comes with them, you could consolidate everything under one subscription that covers 400+ AI models.
What makes this work in practice is that you’re not just reducing your bill—you’re getting a single governance layer. You can control which teams access which models, audit everything from one place, and actually enforce policies instead of hoping teams follow them.
We used Latenode for a similar transition, and the workflow migration was straightforward because the platform handles the underlying access layer. You describe what you need to automate, and it manages the model access without you having to rewire everything. No more scattered API keys, no more spreadsheet tracking of what costs what.
Worth exploring for your situation. Check it out at https://latenode.com