I’ve been wrestling with this for the past few quarters, and I need to get real about the numbers. Right now, we’re paying for:
- OpenAI subscriptions (multiple tiers across teams)
- Anthropic Claude API access
- Google’s Vertex AI
- Cohere integration
- A couple of niche model providers
Plus we’re maintaining n8n self-hosted, which means we’ve got infrastructure costs, ongoing maintenance, security patches, and—honestly—a DevOps person who’s half-dedicated just to keeping the lights on.
I did a rough calculation last month. The licensing alone is running us about $45K annually, and when you factor in the infrastructure and the engineering overhead, we’re looking at closer to $80K a year just to keep the lights on.
I keep hearing about platforms that offer access to 400+ AI models through a single subscription, which sounds like it could cut through this complexity. But I’m struggling to find anyone who’s actually quantified what the real savings would be. Is it just marketing, or are companies actually seeing meaningful ROI by consolidating?
The part that worries me is whether we’d lose flexibility by moving to one platform, or if the simpler licensing actually lets us run more efficiently.
Has anyone actually sat down and modeled this out for their organization? What did your actual TCO look like before and after consolidation?
I went through a similar exercise about a year ago at my company. We had 18 different API contracts scattered across teams, and the vendor management alone was eating up cycles.
The honest answer is that the savings aren’t automatic. They come from consolidation—if you actually migrate your workloads. We spent the first two months just mapping out which models we actually needed and which subscriptions were legacy cruft that nobody was using anymore.
Once we consolidated to a unified platform, the financial impact was real, but not in the way I initially expected. The direct licensing savings were about 35-40%, which sounds good until you realize that cost wasn’t the main problem. The real win was velocity. By having 300+ models accessible through one interface, teams stopped asking DevOps for custom integrations. That freed up engineering capacity worth way more than the subscription savings.
The catch is implementation friction. We spent four weeks migrating existing workflows, which cost us effort upfront. If you’re sitting on older n8n setups, migration isn’t free. Budget for that.
One thing nobody talks about: when you have everything in one place, you actually start using models more intelligently. Teams experiment more because there’s no procurement friction. That changes consumption patterns in ways that are hard to predict up front.
Ran the numbers on this for a mid-market ops team. Your $80K total cost is realistic if you’re counting infrastructure. Here’s what actually changed for us:
Before consolidation:
- 5 active AI contracts: $32K
- Infrastructure and maintenance: $48K
- Lost productivity from procurement delays: unmeasured but real
After consolidation:
- Single unified subscription: $19K base + variable execution costs (averaged $8K)
- Infrastructure overhead dropped to $12K (less vendor-specific config creep)
- Productivity gains: teams stopped waiting for new API provisioning, which was usually a 3-5 day cycle
Net savings were about $40K in year one, but the real value was in what teams could do without waiting. That’s harder to quantify in a spreadsheet.
One warning: your DevOps person might actually become more valuable, not less. They shift from infrastructure management to governance and optimization. That’s not a cost reduction; it’s a capability shift. Plan for that.
The consolidation math depends heavily on your actual usage patterns. If you’ve got sprawling teams using different models for different purposes, the unified platform is worth the migration effort. But you need to be honest about three things before you move.
First, calculate your true vendor cost including procurement overhead. That’s usually 20-30% of the subscription cost—people, approvals, onboarding. Second, measure your infrastructure burden realistically. n8n self-hosted isn’t cheap when you factor in skilled labor. Third, project what you’d actually use if model access wasn’t a blocker. That consumption lift is where real savings materialize.
Consolidation works best when you’re consolidating actual usage, not just contracts. If half your subscriptions are underutilized, you’re not consolidating—you’re just moving the waste to a different billing structure.
From a TCO perspective, you should model three scenarios: status quo with continued n8n self-hosting, migration to a managed platform with unified licensing, and a hybrid approach. Most companies find the hybrid approach doesn’t actually save money because you end up maintaining complexity across both systems.
The unified licensing model works because it removes procurement friction and enables better resource allocation. In our case, moving to a single subscription reduced our licensing surface area from 8 vendor relationships down to 1, which simplified security reviews, compliance sign-offs, and vendor management generally.
What surprised us was that model selection became a non-blocker. When access to multiple models wasn’t restricted by different API keys and vendor relationships, engineers actually optimized for the right tool rather than whatever was easiest to provision. That’s a subtle benefit that compounds over time.
Consolidate usage first, licensing second. Map actual consumption before moving.
I had almost the exact same situation—juggling multiple AI contracts with n8n infrastructure costs piling up. We actually just went through this migration, and the financial shift was substantial.
Here’s what changed for us: instead of managing 18 separate vendor relationships and constantly negotiating tier levels, we moved to a single subscription covering 400+ models. The licensing consolidation alone brought costs down about 35%, but that’s not where the real value emerged.
The bigger win was that our engineering team stopped being a bottleneck for model access. Previously, when someone needed a different model or wanted to experiment with a new approach, there was procurement friction. Now they just use what they need from one platform. That freedom to experiment actually improved model selection across workflows—teams started optimizing for the right tool instead of whatever was easiest to access.
The n8n infrastructure overhead disappeared too. No more managing servers, security patches, scaling concerns. Our DevOps person redirected from infrastructure maintenance to building governance policies and optimization, which is way more valuable.
Total year-one savings came to about $65K when you factor in licensing reduction plus infrastructure elimination. Year two is looking even better because we’re not hitting unexpected scaling or maintenance costs.
If you want to model this properly for your organization, I’d suggest focusing on three metrics: direct licensing costs, infrastructure overhead, and procurement cycle time. The first two are easy to quantify. The third—time to market when teams can access models without friction—is harder to measure but often the biggest financial driver.
Worth exploring in detail: https://latenode.com