So we spent the last 18 months managing separate subscriptions for OpenAI, Claude, Gemini, and a few others across different teams. It was messy. Procurement hated it. Finance couldn’t track spend. We were paying for overlapping access we didn’t even know about.
We started looking at consolidation options, and the math on Latenode’s one subscription for 400+ models actually made sense. Not in a theoretical way—in a real cash-flow way.
What we found: our actual cost per execution was genuinely cheaper than what we’d been spending on individual API keys. One of our automations that generates 2000 emails with GPT and pushes them into Sheets used to cost us about $450/month across platforms. Same workflow on Latenode execution pricing? Around $60/month. That’s not a growth-hacking claim. That’s real.
But here’s what surprised me more than the cost savings: the procurement simplification was almost worth it by itself. Instead of managing 12 vendor relationships, integration docs, billing cycles, and access controls, we now have one platform and one contract. Our IT team stopped needing to audit which teams had which API keys.
We’re not done optimizing yet. We’re still figuring out how to estimate what our execution footprint will look like as we build more complex workflows with our autonomous agents. And the ROI modeling is tricky when you’re comparing apples to oranges—legacy point solutions vs. a unified platform.
Has anyone else actually done this consolidation? I’d like to hear how you modeled the migration costs and what your recovery timeline looked like.
We did something similar but went about it differently. Started with templates instead of full migrations. Latenode’s ready-to-use templates let us prototype workflows in a day that would’ve taken us weeks to build from scratch on our old setup. That meant we could actually test the cost assumptions before committing to the full consolidation.
The execution-based pricing model is genuinely different from what we were used to. You’re not paying per operation anymore, so the incentives flip. I noticed our workflows got simpler because we stopped trying to hack around operation limits. That alone probably saved us more than the raw subscription difference.
What caught us off guard was the governance side. With autonomous agents handling parts of complex workflows, we needed to set up audit trails and approval gates. Latenode handled that, but we had to think through it upfront. If you’re consolidating, don’t skip that conversation with compliance.
The 60% savings number matches what we hit too, but our timeline was slower. First three months were about proving the platform worked, not proving cost savings. We used the free trial to build one workflow, then one of the templates, then started migrating the workflows that mattered.
One thing I’d push back on slightly: the execution pricing is cheaper, but it’s also less predictable if you’re not careful. We had to actually monitor our workflows and optimize them. A badly written loop could spike your costs. With Make or Zapier, at least your costs were bounded by operation count.
Biggest win for us was the built-in database. We were paying Zapier extra for data storage in external tools. Latenode just had it. That’s where a chunk of the savings came from for us.
The consolidation math is compelling, but I’d harden your ROI model around one thing: migration effort. We estimated two weeks to move 20 workflows. Took six weeks. Not because Latenode is harder, but because we had to validate every single workflow in production and our change control process was slower than we thought.
If you’re still in the planning phase, that’s where your real costs sit. Make sure you’re accounting for the engineering time to migrate, test, and validate. The platform cost savings are real, but they show up over months. The migration costs show up now.
What helped us most was starting with new workflows on Latenode before we migrated the legacy ones. That gave the team ramp-up time without touching critical systems.
Procurement simplification is underrated in these calculations. We saved about $40K in annual vendor management costs just by having one contract instead of five. That’s real money that doesn’t show up in the unit economics but shows up in headcount reality.
For the execution pricing model specifically, the key is understanding that time-based pricing rewards efficient workflows. We spent two weeks optimizing our four biggest automations for Latenode’s pricing model—removing unnecessary loops, batch-processing data more intelligently. That optimization alone brought our costs down an additional 15% on top of the platform switch.
One caution: if your workflows are very simple and mostly handled by point solutions today, you might not see the same 60% win. The bigger your workflows and the more complex your logic, the more Latenode’s approach saves you.
We saw similar savings. Start with templates, they prove the concept. The real ROI comes from streamlining your whole automation stack, not just cost per run. Took us about 3 months to fully migrate, not 2 weeks like we first thought.
60% is solid. Our migration focused on highest-volume workflows first. That’s where the math works best. Monitor early, optimize workflows for execution time.
Your consolidation story is exactly what we’re seeing across teams. The execution-based pricing genuinely rewards you for building efficient workflows. One thing to consider as you scale: Latenode’s autonomous AI agents can handle multi-step processes that would’ve required orchestrating across multiple platforms before. We’ve had customers reduce their total workflow count by 30% just by letting AI agents handle the decision logic and handoffs.
The built-in RAG and 300+ model access means you’re not stitching together fragile AI workflows across ChatGPT, Claude, Gemini separately. It’s all managed through one execution environment. That’s where the real consolidation lever is.
For your next phase, consider whether any of those 12 subscriptions you were managing were AI services. If so, running that through autonomous agents on one platform probably compounds your savings even more. The team is usually smaller too because the agents handle work that used to require people.