Two economic models. One structural question.
Should your ordering technology cost more as you grow — or less?
Your current vendor built a competent product. The problem isn't the product — it's the economic model the architecture forces. This page compares per-order economics against flat-fee economics, names where each model is stronger, and gives you the tools to run the math yourself.
What your invoices show vs. what they'd show under a different architecture.
Not a feature checklist. An economic model comparison with both sides named.
Per-unit cost trajectory at scale
Per-order model
Cost per order and cost per location increase as you grow. At 80 locations, tolerable. At 160, it accelerates — several fee layers compound independently. That's architecture, not greed.
Flat-fee model
Cost per order and cost per location decrease as you grow. Fixed annual fee divided by increasing volume. Arithmetic, not a projection — though specific savings should be modeled with your actual data.
Upfront cost and implementation timeline
Per-order model
Lower upfront. Faster implementation — weeks to months. The vendor provides templates and manages configuration. Legitimate tradeoff: speed for constraints.
Flat-fee model
Higher upfront. Longer implementation — months. You build the front end, your team handles integration, migration requires parallel-run. First six months are harder. Long-term trajectory is lower.
Operational control
Per-order model
Vendor manages the experience layer — faster setup, constrained customization. Reasonable when customization isn't a priority and engineering bandwidth is limited.
Flat-fee model
You own every pixel. Infrastructure provides logic through APIs; your team builds what the customer sees. Requires engineering capacity you maintain.
Portability
Per-order model
Front end, data model, and configurations typically live in the vendor's environment. Switching requires rebuilding — creating structural incentive to stay regardless of cost trajectory.
Flat-fee model
You own your code, data, and configurations. If you leave, you take all of it. Because you own more, you maintain more.
Higher upfront. Lower over time. Here's the math.
Below is the model — every assumption labeled and editable. The only projection that matters is the one your CFO builds with your actual numbers.
What's modeled: Five-year projections from published pricing data and standard growth assumptions. They are projections, not guarantees. Assumptions are editable because the model is most useful when you break it.
Where it's most sensitive: Growth-rate assumptions. The divergence accelerates with growth — which means the model looks best at aggressive rates and weakest at flat counts. Stress-test the growth assumptions first.
When the per-order model is the better decision.
The flat-fee model isn't universally superior. Below that threshold, the per-order model is the right architecture and the right economics.
Under ~30 locations
The per-order compounding is tolerable — the dollar difference doesn't justify migration complexity. A managed system with templates is faster and simpler. The growth tax becomes visible around 50–80 locations.
No internal engineering team
Headless architecture requires front-end development and integration maintenance. If that bandwidth doesn't exist, the managed model is the right choice. Not inferior — appropriate.
Speed to market is the priority
If you need to be operational in weeks, a templated system gets you there. Ordrin's implementation takes months. If the timeline matters more than long-term costs, the right decision is the faster one.
The compounding hasn't hit yet
If your costs are growing linearly and the acceleration is three years out, there's no urgency to migrate. Pull the invoices, chart the trajectory, run the calculator. If it says "not yet," we'll agree.
The real cost of switching — named before you ask.
Every comparison page shows you what you gain. This section shows what it costs.
Implementation timeline
Months. Data migration, integration reconfiguration, front-end development, parallel-run, cutover. We deliver the Implementation Reality briefing — including what's gone wrong before — before presenting a contract.
Engineering investment
Hours from your team over the migration period. Front-end development, integration config, testing. Not a one-time project — ongoing maintenance follows.
Operational disruption
The parallel-run period minimizes but does not eliminate disruption. Staff training, menu migration verification, integration validation — all while current system runs. Anyone who promises zero disruption during infrastructure migration either hasn't done one or isn't telling you.
Evaluation time
90–180 days of proper evaluation. That's time your team spends on assessment instead of operations. We require it because a $650K decision evaluated in 30 days hasn't been stress-tested. But the time cost is real.
The questions your evaluation team should be asking.
These are structured for the conversation between the operations leader who found Ordrin and the CFO or CTO they need to convince. If a question your team is asking isn't here, that's a gap we should close.
You're building infrastructure, not renting a template. Under per-order pricing, the vendor absorbs setup costs and recovers them through compounding fees. The question isn't which costs less on day one — it's which costs less over five years at your scale. The TCO calculator models both.
Months. Data migration, integration reconfig, front-end build, parallel-run, cutover. Engineering hours from your team. We cover disruption risks and what's gone wrong before — in the briefing, before the contract. Disruption minimized, not eliminated.
It might. Incumbent systems have years of feature development. The Implementation Reality briefing covers this: what Ordrin replicates, what it does differently, what it doesn't do. If there's a dealbreaker gap, we'd rather surface it in month two of evaluation than month six of implementation.
Different model. Most systems report what happened after the fact. Ordrin's intelligence layer interprets signals in real time and surfaces context before issues compound. The choice depends on whether your team acts on live signal or periodic reports.
Maybe. The math works clearly at 50–200+ locations with engineering staff and compounding cost pressure. It works against Ordrin under ~30 locations, without engineers, or when speed to market matters more. The TCO calculator shows where you fall. If it says per-order is right, we'll agree.
The comparison that matters is the one your CFO builds.
TCO calculator. Your actual numbers. Every assumption editable. If the math doesn't work at your scale, it'll show you that too.