Launching your own AI infrastructure is a necessity for companies in the market in 2026. Stanislav Tibekin, co-founder of IT integrator Nixys, shared typical mistakes when setting up an AI circuit and identified 5 critical components without which your LLM infrastructure is a set of disparate solutions.

In recent months, Nixys has been actively receiving requests for deploying their own LLM. And in almost every project, the same mistake:

Business chooses a model but does not consider the infrastructure around it. From presale in 2026 based on a customer case, Stanislav listed current business requirements and difficulties at each stage.

✳️The goal of the project is to build a unified circuit for deploying and operating LLM and CV models with WebUI and API. The customer company wants to create a full-fledged internal platform for AI.

5 critical components:

1️⃣Orchestration and load balancing
LLM inference is memory-intensive. A mistake in placement – and an expensive GPU A100 with 80 GB sits idle because two models that do not fit were placed on it under 95% utilization.

2️⃣Role/access management and quotas system
Centralized management without blocks inside the system is important. There should be no colossal bills for cloud GPUs. By the way, FinOps for AI starts with this item (those whose business is built on AI will understand what we are talking about).

3️⃣Unified user experience
Developers should not have to figure out how to form requests to the system. They need to be provided with both a convenient web chat (like Open WebUI) and a stable API for integration into the company's own products.

4️⃣Model lifecycle and storage
Even in small teams, the question may arise – where to get a trained version of the model I need? Teams in DevOps faced similar problems before.

5️⃣Monitoring and observability
Without end-to-end request logging, tracing, and inference metric monitoring, it is impossible to understand what is causing slowdowns, which users created the load, and how to optimize costs.

Companies that implement LLM in 2026 are already competing at the level of AI infrastructure quality.

Your own LLM is a full-fledged platform with data, accesses, containers, and metrics.
Without an infrastructure circuit, even a successful pilot risks turning into an expensive and uncontrollable adventure.

More about how to calculate TCO, manage AI infrastructure, and avoid typical LLM implementation mistakes – in ITibekin FM.