# ibl.ai vs OpenAI's Forward-Deployed Engineers

> Source: https://ibl.ai/resources/comparisons/ibl-ai-vs-openai-forward-deployed-engineers
> Last updated: 2026-08-19


*We deploy OpenAI's models and agents too — the difference is that you keep LLM agnosticism and AI sovereignty, owning all the code and the data*

**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 ibl.ai and OpenAI Forward-Deployed Engineers?

OpenAI has industrialized the forward-deployed engineer. It capitalized a deployment company at more than $4 billion — led by TPG with Advent, Bain Capital and Brookfield as co-lead founding partners — and acquired Edinburgh-based Tomoro for roughly 150 deployment engineers.

The model is borrowed from Palantir, and it works. GPT is excellent, and enterprises are right to want it in production.

This page is not an argument against OpenAI's models. We deploy them. ibl.ai runs GPT alongside every other model, and our forward-deployed engineers build production agents on it exactly as an OpenAI engagement would.

The difference is what you keep afterwards. With us you retain LLM agnosticism and AI sovereignty: you own all the code and the data, GPT is a component you can swap rather than the foundation you are standing on, and the whole system runs on infrastructure you control.

## Feature Comparison

### What Arrives on Day One

| Criteria | ibl.ai | OpenAI Forward-Deployed Engineers |
|----------|--------------------|--------------------|
| A Working Platform | In production with 1.6M+ users from 400+ organizations, deployed on your infrastructure. | People and method. The platform is built during the engagement. |
| Domain and Industry Depth | Production deployments across higher education, K-12, government, legal, financial services and healthcare, with sector agents already built and running. | Unmatched depth on their own frontier models — the people deploying them work alongside the people who trained them, with early access and direct escalation paths no third party can match. |
| Time to First Production Workload | Weeks — there is no construction phase, because the platform already runs. | Quarters, most of them spent building infrastructure common to every client. |
| Capacity to Deliver Your Programme | A dedicated forward-deployed team plus a finished platform delivers the programme end to end — no bench to wait on and no second firm required. | Very large global staffing, though your programme is resourced from it and priced accordingly. |

### What You Own Afterwards

| Criteria | ibl.ai | OpenAI Forward-Deployed Engineers |
|----------|--------------------|--------------------|
| Source Code Ownership | Full source under a perpetual licence, running on your infrastructure. | A contract question rather than a product property, and frequently under-negotiated. |
| Model Freedom | Model-agnostic by construction — any LLM, switchable without rewriting the platform. | Oriented around one vendor's models, which is what the deployment arm exists to support. |
| Independent Operability | Documented, supported, and maintained upstream so your own team can run it. | Depends on knowledge transfer and on continued access to whoever built it. |
| Can It Run Air-Gapped | Yes — open-weight models on a self-hosted platform run with zero external calls. | No. The models are API-only and the weights are not distributed, so an isolated network cannot use them. |

### Commercial Shape

| Criteria | ibl.ai | OpenAI Forward-Deployed Engineers |
|----------|--------------------|--------------------|
| How It Is Priced | A flat platform licence plus a bounded integration engagement. | Typically enterprise engagements attached to model consumption. |
| Is There a Ceiling | Yes — the licence plus the compute you run. Extending to more users does not multiply it. | Bounded by the contract if fixed-price, otherwise by the estimate's accuracy. |
| Cost of Undifferentiated Infrastructure | Zero — retrieval, guardrails, access control and audit already exist and are amortised across every customer. | Funded by you, and rebuilt for the next client afterwards. |
| Ongoing Maintenance | Upstream releases carry model support, protocol updates and security fixes. | A separate contract, or an internal team, for a system built only for you. |

## Detailed Analysis

### Can you have GPT agents and still own the stack?

**ibl.ai:** Yes — that is precisely what we deliver. ibl.ai runs OpenAI's models, our engineers build the agents on them, and you keep LLM agnosticism and AI sovereignty because you own all the code and the data.

**OpenAI Forward-Deployed Engineers:** An OpenAI engagement delivers excellent GPT agents. What it does not deliver is a platform you own or the ability to route a workload to a different model when that becomes the better answer.

**Verdict:** You do not have to choose between GPT and sovereignty. Hire the partner that gives you the agents AND the ownership, rather than trading one for the other.

### Can the deployment run where your data must stay?

**ibl.ai:** ibl.ai runs on your infrastructure, on-premise or fully air-gapped with zero external calls, because the platform and the open-weight models it can serve are both yours to host.

**OpenAI Forward-Deployed Engineers:** OpenAI's frontier models are not distributed as weights, so they cannot be brought inside a perimeter that forbids external calls, however skilled the engineers are.

**Verdict:** For classified, air-gapped, or strictly residency-bound workloads this decides the question before expertise is considered.

### What do you hold when the engagement ends?

**ibl.ai:** The full source under a perpetual licence, running on your own infrastructure — an asset that keeps working regardless of any commercial relationship.

**OpenAI Forward-Deployed Engineers:** A well-integrated deployment that depends on continued access to the vendor's API, pricing, and model availability.

**Verdict:** Ask what happens to the system if the commercial relationship changes. That answer is the real measure of who owns the outcome.

## FAQ

**Q: What is an OpenAI forward-deployed engineer?**

An engineer embedded with an enterprise customer to take frontier-model deployments into production. OpenAI has scaled this into a separately capitalized deployment company backed by TPG, Advent, Bain Capital and Brookfield, and acquired Tomoro for roughly 150 deployment engineers.

**Q: Should we hire OpenAI's engineers or ibl.ai?**

We deliver on OpenAI's agents too — the same models, production-grade, built by forward-deployed engineers. The difference is that with us you keep LLM agnosticism and AI sovereignty: you own all the code and the data, and GPT is a component you can change rather than a foundation you cannot.

**Q: Can we still use OpenAI models with ibl.ai?**

Yes. The platform is model-agnostic and routes to OpenAI's models alongside open weights running inside your own network. You keep access to them without being limited to them.

**Q: What about air-gapped deployments?**

Frontier models are not distributed as weights, so they cannot run inside a network with no outbound connectivity. An owned platform serving open-weight models can, which is why classified environments resolve this question on architecture rather than expertise.

**Q: Why are the model labs building services arms at all?**

Because deployment is the bottleneck on enterprise adoption, and the Palantir forward-deployed model demonstrably converts capability into production usage. Anthropic has done the same through a joint venture reported above $1.5B.

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

ibl.ai arrives with the base already built. The platform is in production with users from 400+ organizations and ships with the full source code, so customized engineering starts from something that works rather than from an empty repository — which is what makes it comparatively fast and cost-effective. 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 ibl.ai and OpenAI Forward-Deployed Engineers?

**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 delivers on OpenAI's agents while you keep LLM agnosticism and AI sovereignty.

Our forward-deployed engineers build production agents on GPT exactly as an OpenAI engagement would — and they do it on a platform already run by users from 400+ organizations, integrated with your systems over APIs and MCP. Because it is model-agnostic, the same agents can route to Claude, Gemini, or open weights running inside your own network when a workload is too sensitive or too high-volume to send out.

You own all the code and the data, with no per-seat pricing, and can deploy anywhere including fully air-gapped. That is the whole proposition: the frontier agents you want, without surrendering the stack they run on.

- **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.