We’ve been doing some internal analysis trying to understand whether consolidating our AI subscriptions would actually save money, or if moving everything to a unified platform would just trade one set of costs for a different set of costs.
Our current actual spend looks roughly like this:
OpenAI API and ChatGPT Plus subscriptions across different team instances: about $8K/month
Anthropic for specific teams that prefer Claude: about $3.5K/month
Gemini for image and video work: about $2K/month
Cohere for a few special cases: about $1K/month
Miscellaneous other models our teams are experimenting with: about $2.5K/month
Plus API key management overhead, procurement cycles for each vendor, compliance review for each integration
Total direct spend is roughly $17K/month. But we also have what I’d estimate as about 10-15% overhead in terms of procurement time, integration testing, and developer time managing credentials. So actual cost is probably closer to $19.5K/month.
The pitch for consolidation is that we could move to a unified platform with one subscription that includes all those models. The claimed savings are usually around 40-60%. But I’m skeptical of those numbers because I don’t know how they’re calculated. Are they accounting for the same volume of API calls? Are they assuming different pricing tiers? Are they real deployments or marketing math?
I’m also wondering what the switching costs actually are. Migration isn’t free. There’s time to understand the new platform, time to adapt existing workflows, potential downtime during transition. How long before the financial benefit actually accrues?
And then there’s the classic vendor lock-in concern. If we consolidate to one platform and they raise prices next year, what are our options? We’d have everything built on their infrastructure.
Has anyone actually done this calculation and then executed the migration? What did the real numbers end up being? Did you actually achieve the claimed savings, or was the reality more complicated?
I did this exact math exercise about 18 months ago. Let me walk you through what actually happened versus the pitch.
Our direct spend was similar to yours—roughly $18K/month across multiple vendors. The unified platform quoted about 40% savings, so $7.2K/month reduction to around $10.8K.
Here’s what actually happened: we achieved roughly 35-40% savings, but not uniformly. Some workloads got way cheaper. Others were actually slightly more expensive. Some use cases shifted to be more efficient because the pricing model aligned incentives better.
The real financial win wasn’t just the subscription consolidation though. It was the operational cost reduction. We had one person who was essentially full-time on vendor integration and API key management. That went away. We had recurring procurement cycles—each new model meant a new contract review. That went away. Compliance reviews happened once per platform instead of once per vendor. That was probably worth $3-4K/month in labor cost that doesn’t show up on the subscription line.
Switching costs are real. We budgeted about 40 hours of engineering time to migrate workflows, validate they still worked on the new platform, set up monitoring. That’s maybe $8-10K in labor. But it’s one-time, and it paid back in about 4-6 months of operational savings.
On vendor lock-in, yes, it’s a concern. But ask yourself: were you really not locked in to 15 separate vendors before? Being locked to one vendor is actually more straightforward than being locked to 15. And most unified platforms have better data export and portability than individual vendors, honestly.
Our actual savings ended up being about $7-8K/month direct, plus $3-4K/month in operational overhead reduction. Six month payback on switching costs, then steady savings.
What surprised me most: our teams actually built more efficiently because they weren’t rationing model usage. When you’re paying per-operation and each operation had microeconomic choices, teams optimized for cost at the expense of quality. With time-based execution pricing, they optimized for actual value.
The gotcha in the savings calculation is usually hidden math. When vendors claim 40% savings, they’re sometimes comparing their best-case pricing against your worst-case current spend. That’s not dishonest exactly, but it’s not apples-to-apples either.
Do an honest comparison: take your actual usage patterns—how many calls per day to each model, average call complexity, error rate patterns—and model that against both your current spend and the unified platform pricing. That’s where you see real numbers.
From that exercise, we found that our most-used models (GPT-4 and Claude 3) were priced competitively by the unified platform. Our rarely-used models (Cohere, Mistral, specialist models) were actually cheaper because you’re getting access without having to maintain separate subscriptions. The average worked out to about 35-38% savings, so the 40% pitch wasn’t wrong, just optimistic.
Migration friction is usually overstated. Most modern platforms have import tools or standardized ways to express workflows. Yeah, you need testing, but it’s usually straightforward.
The vendor lock-in thing: consider that the alternative is vendor fragmentation, which is its own lock-in. Each vendor has their own integration points, their own API structure, their own authentication. That’s actually harder to escape than one platform’s standardized approach.
To properly evaluate financial impact, you need to separate direct costs from operational costs. Direct costs are subscription fees. Operational costs are infrastructure, integration, compliance, and personnel overhead.
For direct costs, the math is: current total API spend versus unified platform cost for equivalent usage. You won’t get exact apples-to-apples because pricing structures differ, but you can model it.
For operational costs, quantify: How much time does your team spend on API management, key rotation, vendor compliance reviews, integration testing? That’s real money that shows up in your budget implicitly.
The switching cost question: one-time migration labor plus any downtime during transition. Model this as payback period—switching cost divided by monthly savings.
Vendor lock-in is overblown when the alternative is vendor fragmentation. One comprehensive platform is actually less lock-in than 15 separate vendors in many cases.
The real ROI usually appears in month 6-12 after consolidation because that’s when operational efficiencies compound. You’re not just spending less on subscriptions—you’re operating more efficiently.
Financial consolidation math has several components:
Direct Cost Component:
Model your actual usage volume against both current pricing and unified platform pricing. Actual savings typically range 25-45% depending on your current vendor mix and volume.
Operational Overhead Component:
Quantify: API key management time, vendor compliance review cycles, integration testing overhead, procurement cycles. This usually ranges 5-15% of direct spend but is often invisible.
Switching Cost Component:
Migration labor plus testing, potential transient inefficiency during transition. Typical payback: 3-8 months depending on operational overhead savings rate.
Usage Pattern Changes:
Unified platforms often trigger efficiency changes because pricing incentives align differently. Per-operation pricing creates usage rationing. Time-based execution pricing creates efficiency optimization. Net effect usually reduces actual usage by 10-20% while improving outcomes.
Total Impact Calculation:
Total annual savings roughly equals (direct cost savings + operational overhead reduction) - (switching costs ÷ months to break even).
For a $17K/month direct spend operation: typically expect $6-8K/month direct savings after switching costs amortize. With operational overhead, total savings closer to $9-12K/month by month 12.
Vendor switching risk: real but lower than staying with 15 fragmented vendors. One platform can be negotiated better. Fifteen separate relationships actually reduce your negotiating power.
Direct savings usually 35-40%. Operational overhead reduction another $3-4K/month. Switching costs recover in 4-6 months. One vendor less risky than 15.
I ran this exact analysis. Our spend was roughly $20K/month across vendors. We projected 40% savings, so about $8K/month.
Actual results: we achieved $7-8K direct savings plus another $3.5K in operational overhead elimination. The mathematics worked out because our usage patterns actually benefited from execution-time pricing versus per-operation.
Here’s what made the difference: we stopped rationing model usage. When you’re paying per-API-call, your teams optimize for call count rather than outcome quality. With execution-time pricing, they optimize for actual value. We ended up using models more extensively but more efficiently. Fewer operations overall, better outcomes.
Switching costs were about 35 hours of engineering time. That’s negligible compared to ongoing savings. Migration from separate vendors to Latenode was actually cleaner because Latenode’s workflow interface is more standardized. Our workflows translated pretty directly.
Vendor lock-in is honestly less of a concern than staying locked into 15 separate relationships. We can actually negotiate better pricing now because we’re a more meaningful customer. If we needed to leave, exporting our automation logic is straightforward—workflows are portable.
Real financial impact: month 1-3 involved migration overhead, so actual savings negative while we did the work. Month 4-6 we recovered switching costs. By month 12 we’re running about $11-12K/month all-in versus the previous $20K. That’s 40-45% total reduction including switching costs amortized.
Biggest surprise: we built new automations faster because access to 400+ models meant less decision paralysis about which vendor to use for each use case. Speed to implement new ideas increased, which is hard to quantify but definitely meaningful.