I’m at the point where we’re paying for OpenAI here, Anthropic there, maybe Deepseek somewhere else, and honestly it’s a nightmare to track. Finance is asking me to justify why we need all these separate contracts when supposedly there are platforms that bundle everything.
The thing is, I can see the appeal of consolidating to one subscription with access to 400+ models. But how do you actually measure the ROI on that move? Is it just about cutting the number of invoices, or is there something deeper I’m missing?
We run about 8-10 different workflows across departments, and each one uses different models depending on the task. Some teams swear by OpenAI for their use case, others prefer Claude. If we move everything to one platform, do we lose flexibility? And more importantly, how would you actually calculate whether we’re saving money or just shuffling costs around?
I’m not looking for marketing speak here—just wondering if anyone’s actually done this transition and can share what the real numbers looked like. What metrics did you track before and after?
I dealt with this exact problem last year. We had subscriptions scattered across four different platforms and the licensing headache was ridiculous.
Here’s what actually worked for us: we ran a 30-day audit where we logged which model each workflow actually used and how many API calls it made. Sounds tedious, but it gave us real numbers instead of guesses.
Then we mapped those workflows to what a single platform could do. The surprise wasn’t just the cost savings—it was the operational overhead that disappeared. No more juggling API keys, managing separate quotas, or dealing with rate limits differently on each service.
The actual ROI came from three places: reduced licensing costs (yeah, we saved about 35%), but also the time my team saved not managing these separate accounts (probably worth another 20% in overhead), and honestly, we could move workflows around between models without rebuilding everything.
One thing though—don’t just look at monthly costs. We saved money on the subscription side but discovered consolidating meant we could actually optimize which model we used for each task instead of sticking with one vendor because switching was expensive. That flexibility added value we didn’t expect.
The transition is messier than the pitch makes it sound, but it’s doable if you approach it right.
What we did was create a simple spreadsheet tracking our spend across platforms for three months before the switch. Cost per API call, frequency of use, peak usage patterns—all of it. This became our baseline.
Then we modeled our actual workflow usage against the single subscription pricing. Not theoretical usage. Actual. This is where most people get tripped up—they underestimate how their usage patterns would change once you remove friction.
The ROI was real for us, but it wasn’t massive. We were looking at maybe 25-30% cost reduction after accounting for potential overage scenarios. The bigger win was that our developers stopped asking permission to try different models for different tasks. They just used what made sense. That led to better workflows, faster response times in some cases.
Key thing: make sure the platform you’re switching to actually handles all your model preferences. If you’re consolidating subscriptions but losing specific model capabilities you relied on, that kills the ROI calculation.
Before you calculate ROI, you need to separate the costs that actually go away from the ones that just get relabeled. Most people confuse the two.
When we consolidated, here’s what we actually measured: direct subscription costs (easy), time spent managing integrations and API governance (surprisingly substantial), and development time for recreating workflows when vendors changed pricing or added limitations (this was our biggest hidden cost).
The consolidation saved us money on subscriptions, sure. But the bigger ROI came from standardization. Once everything lived in one place, we could build once and deploy the same workflow template across teams. That reduced duplicate work significantly.
One warning though: make sure you’re not just moving from several complex systems to one complex system. Some platforms that claim to bundle everything still require different setup for different models. If that’s the case, you’re not really consolidating anything meaningful.
The ROI calculation depends heavily on your current workflow architecture. If you’re running simple, single-model workflows, consolidation saves you maybe 20-30% on licensing. If you’re orchestrating complex multi-model processes, the savings can hit 40% or more because you eliminate redundancy.
What matters is mapping your actual usage patterns first. Track which models you call for each workflow, how frequently, and what the cost-per-call looks like today. Then model the consolidated platform’s pricing against those exact patterns.
Here’s the non-obvious part: consolidation forces you to standardize how you invoke models. That standardization often leads to better error handling and retry logic. We saw about 15% improvement in workflow reliability after consolidating, which had its own ROI in reduced incident response time.
The real value comes when you can share standardized workflows across teams using a single model access layer. That’s where the leverage kicks in.
Track spend for 3 months before switching. Chart which models each workflow uses. Then calculate unified pricing against actual usage, not theoretical. We saved about 30% on subscriptions plus admin overhead. But only if the new platform supports all your model needs.
This is exactly where a lot of teams struggle, and honestly, it’s a problem that doesn’t need to be this complicated.
What we found works best is actually automating the ROI calculation itself. Instead of manually tracking spend across platforms, you set up a workflow that pulls cost data from your current vendors, normalizes it, and then models it against consolidated pricing. Sounds meta, but it saves weeks of spreadsheet hell.
With Latenode, we built a workflow that connects our usage logs, cost data, and model performance metrics into one dashboard. The automation runs daily, so we always have current numbers. When we actually made the switch, we already knew exactly what the ROI would be because the data was continuously updated.
The key insight: consolidating AI subscriptions is only part of the win. The real ROI comes from automating how you measure, track, and optimize model usage once everything’s in one place. You can’t do that with spreadsheets and manual processes.
This is exactly what Latenode was built for—taking scattered data sources and automating the workflows that matter to your business. If you’re serious about measuring ROI on consolidation, build the measurement system itself as an automation.