Comparison

ibl.ai vs OpenAI's Forward-Deployed Engineers

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.

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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.

by ibl.ai

Owned platform + forward-deployed engineering

by OpenAI

Model vendor's deployment arm

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.

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'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.

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

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

A well-integrated deployment that depends on continued access to the vendor's API, pricing, and model availability.

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

You can commit to GPT and still own the stack. We deploy OpenAI's models and build agents on them, while you keep the code, the data, and the option to route elsewhere later.

Frontier models cannot be hosted inside a network with no external connectivity; open weights on an owned platform can.

A model-agnostic platform keeps the model layer replaceable, which is the one decision most likely to be revisited within three years.

Routing bulk work to open weights on owned GPUs and reserving frontier calls for the hard cases is materially cheaper than sending everything to one API.

Timeline: Four to ten weeks depending on how much has already been built

• Identify what the engagement built that is genuinely specific to you — that part survives as an extension on the platform.
• Map the undifferentiated layers onto the platform's existing retrieval, guardrails, RBAC and audit.
• Settle source-code and data rights for existing work before transition; delivery does not imply ownership.
• Re-point integrations at the platform's API and MCP layer instead of rebuilding them.
• Re-run your evaluation set before switching production traffic.

Timeline: Days to weeks to contract

• Sensible where you need unmatched depth on their own frontier models — the people deploying them work alongside the people who trained them.
• Note that the platform layer does not have to move with it — the licence and source remain yours.
• Confirm which party maintains what after go-live.
• Negotiate rights explicitly for anything newly built.

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 1.6M+ 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 dataFull source code under a perpetual license, running on your infrastructure. Not API access to someone else's platform — the stack itself is yours.
• Model-agnosticRun any LLM — Claude, GPT, Gemini, Llama, Command, or your own fine-tune — and switch providers without rewriting the platform.
• No per-seat pricingUsage-based billing against a budget cap you set. Cost tracks what your organization actually uses, not how many people you employ.
• Deploy anywhereYour 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.

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.

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.

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.

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.

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.

ibl.ai arrives with the base already built. The platform is in production with 1.6M+ 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.

See how ibl.ai deploys AI agents you own and control—on your infrastructure, integrated with your systems.