# Rate Cards vs a Flat Platform Licence

> Source: https://ibl.ai/resources/comparisons/rate-card-vs-flat-platform-license
> Last updated: 2026-08-19


*Buy hours priced by seniority, or buy a platform priced once — and notice which one has a ceiling*

**On ibl.ai you own all the code and the data, run it model-agnostic across any LLM, and pay with no per-seat pricing — so you can deploy anywhere, from your own cloud to a fully air-gapped network.**

## What's the difference between Owned Platform + FDE and Rate Card?

A rate card is a price list: architect, senior engineer, data engineer, project manager, each with an hourly figure, often blended into a single number for simplicity.

It is a clean way to buy labour and a poor way to buy an outcome. Nothing in a rate card refers to what gets delivered — it prices the input, and the relationship between input and result is exactly what neither party can predict on AI work.

A flat platform licence prices the other side of the equation. The platform costs what it costs, the compute costs what it costs, and neither figure moves because a discovery in month three added six weeks.

The distinction that matters is not the hourly number. It is whether the arrangement has a ceiling.

## Feature Comparison

### Cost & Risk

| Criteria | Owned Platform + FDE | Rate Card |
|----------|--------------------|--------------------|
| Who Carries Scope Risk | The platform's cost is fixed and already incurred; only the integration is variable, and it is scoped against something that exists. | Carried by the buyer, entirely — the rate is fixed but the hours are not. |
| Is There a Ceiling | Yes — a flat licence plus the compute you choose to run. The ceiling is the price. | Billed as hourly rates per labor category, or a blended hourly rate, so the total depends on how wrong the estimate turns out to be. |
| Incentive Alignment | Paid the same whether delivery takes six weeks or twelve, so the incentive is to finish. | Bill more hours at higher-seniority rates. |
| Budget Defensibility | A known line item that survives a budget review and can be forecast years out. | Forecastable only as well as the underlying estimate, which is the thing in question. |

### Fit to AI Work

| Criteria | Owned Platform + FDE | Rate Card |
|----------|--------------------|--------------------|
| Reliability of the Estimate | High, because the platform is finished. What remains is integration with known systems. | Low on from-scratch builds — the reason this model is reached for at all. |
| Handling Changed Requirements | Absorbed as engineering, because you hold the source code and can change it directly. | Handled commercially, through re-estimation or change orders. |
| Time to First Production Workload | Weeks — the construction phase does not exist, because the platform already runs. | Quarters, most of them spent building infrastructure that is not specific to you. |
| Depth of Customization Available | Extensive, on top of a working base, with the source in your possession. | Unlimited in principle, which is exactly why it is unbounded in practice. |

### What You Hold Afterwards

| Criteria | Owned Platform + FDE | Rate Card |
|----------|--------------------|--------------------|
| Source Code Ownership | Full source under a perpetual licence, running on your infrastructure. | Determined by contract clauses rather than by the delivery model — commonly a delivered system and an invoice history, with ownership set separately. |
| Ability to Operate Independently | Your team runs it; the platform is documented, supported, and maintained beyond any engagement. | Depends on knowledge transfer, which is the step most engagements shorten first. |
| Model Freedom | Model-agnostic by construction — run any LLM and switch without rewriting the platform. | Whatever was built in, and whatever the team is willing to maintain. |
| Deployment Flexibility | Any cloud, on-premise, or fully air-gapped, from the same deployment. | Whatever was engineered; air-gapped operation is significant additional scope. |

## Detailed Analysis

### Does a rate card give you cost control?

**Owned Platform + FDE:** A flat licence plus compute has an actual ceiling: adding the ten-thousandth user or the third department changes utilisation, not the invoice.

**Rate Card:** A rate card fixes the price per hour and leaves the number of hours entirely open. Negotiating the rate down 10% while the hours grow 40% is a common and expensive outcome.

**Verdict:** Cost control comes from bounding the quantity, not the unit price. Rate-card negotiations optimise the wrong variable.

### What is the blended rate hiding?

**Owned Platform + FDE:** When the platform already exists, the mix question largely disappears — there is far less work, and the work that remains is integration by people who know the system.

**Rate Card:** A blended rate averages an architect and a junior engineer into one number, which makes the invoice simple and the staffing mix invisible. The mix is what determines whether the hours were well spent.

**Verdict:** Ask what the hours were spent on, not what they cost. On from-scratch builds, most of them go to infrastructure that already exists elsewhere.

### Which arrangement survives a budget cycle?

**Owned Platform + FDE:** A licence is a known line item that can be defended in a budget review, and the asset persists after the engagement.

**Rate Card:** An open hour count is difficult to forecast and harder to defend when finance asks what next year looks like.

**Verdict:** Predictability is not a luxury in public and regulated budgeting — it frequently determines whether a programme continues at all.

## FAQ

**Q: What is a blended rate in AI consulting?**

A single hourly figure averaging several labor categories — architect, engineer, project manager — so the invoice is simple. It also hides the staffing mix, which is what actually determines whether the hours produced value.

**Q: Is a lower rate card a better deal?**

Not reliably. The total is rate multiplied by hours, and hours are the volatile term on AI work. A 10% rate reduction is erased by a 12% overrun, which is well within the normal range.

**Q: How does a flat platform licence compare?**

It bounds the cost. The platform is priced once and the compute is yours, so extending to more users or departments does not multiply the bill the way additional consultant hours do.

**Q: Do you still need engineers with a licensed platform?**

Yes — integration and customization are real work. The difference is that the work starts from a platform that already runs, so it is a fraction of the hours a from-scratch build requires.

**Q: How should we compare vendor quotes?**

Normalise to total cost to a working outcome over three years, including what you own at the end. A rate card alone is not comparable to a licence, because one prices inputs and the other prices the asset.

**Q: How does ibl.ai fit in?**

ibl.ai is the already-built base. The platform is in production with users from 400+ organizations and ships with the full source code, so customization starts from something that works rather than from zero. You own all the code and the data, run it model-agnostic across any LLM, with no per-seat pricing, and can deploy anywhere including fully air-gapped.


## Where does ibl.ai fit alongside Owned Platform + FDE and Rate Card?

**ibl.ai is the agentic AI platform where you own all the code and the data. You self-host the entire stack inside your own perimeter, run it model-agnostic across any LLM and switch anytime, and pay by usage with no per-seat pricing — so you can deploy anywhere: your cloud, on-premise, GovCloud, or fully air-gapped.**

ibl.ai is licensed flat rather than billed by the hour, and the licence includes the full source code running on your infrastructure. Cost is bounded by the compute you choose to run, not by how long an engagement takes.

Engineering is still real — integration with your systems, agents specific to your workflows — but it starts from a platform already used by 1.6M+ users from 400+ organizations, so it is a fraction of the hours a from-scratch build consumes. You own all the code and the data, run it model-agnostic across any LLM, with no per-seat pricing, and can deploy anywhere including fully air-gapped.

- **You own all the code and the data.** Full source code under a perpetual license, running on your infrastructure. Not API access to someone else's platform — the stack itself is yours.
- **Model-agnostic.** Run any LLM — Claude, GPT, Gemini, Llama, Command, or your own fine-tune — and switch providers without rewriting the platform.
- **No per-seat pricing.** Usage-based billing against a budget cap you set. Cost tracks what your organization actually uses, not how many people you employ.
- **Deploy anywhere.** Your cloud, your VPC, on-premise, GovCloud, or a fully air-gapped network with no outbound connectivity.

1.6M+ users across 400+ organizations run the platform this way, including NVIDIA, MIT, and Syracuse University.

ibl.ai is family-owned and operated from New York, NY — a U.S.-headquartered, domestically-owned long-term partner, not a vendor that sells licenses and moves on.