Deploying generative AI involves a chain of interconnected decisions, and getting any one of them wrong is expensive. Organisations know the outcome they want — a chatbot, a document assistant, an automated workflow — but lack the expertise to translate that into the right model and infrastructure. Meanwhile, regulations like the EU AI Act are forcing organisations to take direct responsibility for how their AI behaves and what data it touches, making third-party API dependencies a governance liability. Without specialists spanning both AI and cloud infrastructure, companies resort to guesswork and costly trial-and-error. The result is wasted spend — in some cases over €30,000 per year on a single workload — missed timelines, and AI initiatives that either stall or introduce regulatory exposure the organisation is unprepared for

Self-hosted AI, fully under your control
Your AI runs in your environment — your cloud account, your region, your governance policies. This is the foundation for compliance with data protection obligations and the EU AI Act. InfraTailors removes the complexity that has made self-hosting prohibitive, so control and speed are no longer in tension.
The platform guides you through four connected stages:

1) Use Case to Model — An AI chatbot helps you define your needs in plain language and recommends the right LLM from a curated catalogue, balancing capability, licensing, and cost.
2) Model to Infrastructure — Benchmarking data and predictive models identify the optimal GPU configuration across ten cloud providers, tailored to your performance requirements and budget.
3) Infrastructure to Deployment — Production-ready OpenTofu/Terraform code is generated for you to deploy directly into your own environment. InfraTailors never has access to it.
4) Deployment to Observability — A plain-language dashboard with traffic-light indicators tracks performance, utilisation, cost, and quality in production — accessible to technical and non-technical stakeholders alike.

- Full chain, one platform — From use case definition to live observability, without stitching together separate tools, consultants, or manual processes.
- Right model, right infrastructure — Tailored recommendations that match your specific requirements and budget, not generic defaults.
- Cost control at every stage — Avoid over-specified models and over-provisioned hardware. Cost implications surface at every decision point.
- No specialist required — Any technical team can navigate the full deployment journey, from LLM selection to production monitoring.
- Data sovereignty by design — Your models and data stay in your environment. Telemetry is anonymised and EU-hosted, in line with GDPR.
- Research-backed accuracy — Grounded in PhD-level research on AI workload behaviour, developed at the University of Luxembourg's SnT research centre.