# T&M vs Owned Platform: What the Engagement Actually Costs

> Source: https://ibl.ai/resources/calculators/t-and-m-vs-owned-platform-calculator
> Last updated: 2026-08-19


*Most of a from-scratch AI budget goes to infrastructure that is identical in every organization. This works out how much, using your own numbers.*

A time-and-materials AI engagement is priced as hours multiplied by a blended rate. What that price buys is rarely broken down, and the breakdown is the whole story.

Every AI deployment needs the same foundational layers: permissions-aware retrieval, an evaluation harness, guardrails and prompt-injection defense, role-based access control, audit logging, and model routing. None of it is specific to your organization. A competitor in your sector would need it built identically.

Only the remainder — your data model, your workflows, your integrations, your agents — is genuinely yours, and it is the smaller share.

This calculator separates the two. Enter your team size, blended rate, and timeline, then set what proportion of the effort goes to that undifferentiated foundation. The output is the amount you would be paying to build something that already exists, alongside what the same programme costs when the platform is licensed and only the integration is scoped.

**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.**

## How is this calculated?

The comparison isolates one variable: how much of the platform already exists when the engagement starts. Both paths are costed at the same blended rate, with the same engineers, so the result reflects scope rather than a pricing advantage.

The T&M path multiplies engineers by rate by 160 billable hours a month across the timeline, then applies the overrun you expect. The undifferentiated share of that total is calculated separately, because it is the portion funding work that is identical across organizations and available off the shelf.

The licensed path costs the platform licence, a year of compute, and integration effort billed at the same rate — scoped in weeks rather than months because the system being integrated is finished.

Two things the model deliberately excludes, both of which favour the licensed path: ongoing maintenance of a bespoke system (every model release, protocol change and security fix becomes a funded backlog item), and the value of holding source rights at the end. Excluding them keeps the arithmetic conservative and easy to audit.

The default 65% foundational share is a planning figure, not a measurement of your programme. Categorise submitted timesheets into platform infrastructure versus organization-specific work and substitute your own number — that single ratio explains most AI budget outcomes.

## Assumptions

- **Billable hours per engineer per month:** 160 hours (Standard full-time equivalent used across professional services rate cards)
- **Integration effort is billed at the same blended rate:** 40 hours per engineer per week (Deliberately conservative — integration on a licensed platform is charged at the same rate, so the comparison isolates scope rather than price)
- **Enterprise AI forecasts are missed by more than 25%:** 80–85% of enterprises (Enterprise AI infrastructure budgeting research, 2026)
- **Enterprises reporting AI cost overruns in the last 12 months:** 79% (Enterprise AI infrastructure budgeting research, 2026)
- **Average sunk cost per abandoned enterprise AI initiative:** $7.2M (Enterprise AI infrastructure budgeting research, 2026)
- **Financial institutions running AI agents at scale:** 10%, with 80% still in ideation or pilot (Capgemini Research Institute, World Cloud Report in Financial Services 2026 (1,100 leaders, 14 markets))
- **Licence figure is a modelling placeholder:** Not an ibl.ai price (Use the figure from your own quote; flat licensing means it does not scale with headcount)

## Industry Benchmarks

| Segment | Metric | Typical | With AI |
|---------|--------|---------|---------|
| Mid-size enterprise (5 engineers, 12 months, $185/hr) | First-year cost to production | $1.78M at the estimate, $2.22M at a 25% overrun | $594K licensed path — licence, compute and 8 weeks of integration |
| Large programme (12 engineers, 18 months, $250/hr) | Spent on foundational platform work | $5.6M of an $8.6M invoice at a 65% foundational share | $0 — those layers already exist and are amortised across every customer |
| Public sector T&M award | Time to first production workload | 12–18 months, with a ceiling conversation before it | Weeks, because the construction phase does not exist |
| Any organization, year three | Maintenance of the foundational layers | A funded backlog item for every model release and protocol change | Delivered upstream, with the source code in your possession |

## FAQ

**Q: What proportion of an AI build is actually undifferentiated?**

It varies, and it is measurable rather than a matter of opinion. Categorise delivered effort into foundational platform work — retrieval, evaluation, guardrails, access control, audit logging, model routing — versus your data model, workflows and integrations. If a competitor in your sector would need a capability built identically, it is undifferentiated. The default here is 65%.

**Q: Why does the calculator apply an overrun by default?**

Because the estimate is the number quoted and the invoice is the number paid. Research on enterprise AI budgeting reports 79% of enterprises hit cost overruns in the past 12 months, with 80–85% missing infrastructure forecasts by more than 25%. Set it to zero if your programme has a track record that justifies it.

**Q: Is the licence figure ibl.ai's price?**

No. It is a modelling placeholder so you can enter the figure from your own quote. What matters structurally is that a flat licence does not scale with headcount, so the number does not move when the platform serves more users.

**Q: Doesn't a licensed platform still need engineering?**

Yes, and the calculator charges it at the same blended rate. Integration with your systems of record, identity provider and data model is real work, and so are the agents specific to your organization. It is scoped in weeks rather than months because the platform being integrated is already finished.

**Q: What does the model leave out?**

Two things, both favouring the licensed path: ongoing maintenance of a bespoke system, where every model release and protocol change becomes a funded backlog item, and the value of holding source rights at the end. Excluding them keeps the arithmetic conservative.

**Q: When does the from-scratch build actually win?**

When the foundational share is genuinely low — that is, when almost everything being built is specific to your organization. Set the slider to 20% and the difference narrows sharply. That is the honest test: if your requirement really is unlike anything built before, building it is the right answer.

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

ibl.ai is the already-built base. The platform is in production with 1.6M+ users from 400+ organizations and ships with the full source code under a perpetual licence, so an engagement scopes integration rather than construction. 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.


## How does owning the stack change these numbers?

**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.**

- **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.