AI factory: training, inference, vLLM, MLOps

Your internal AI, hosted on your terms, at a controlled cost

LLM APIs are perfect for prototyping, ruinous for industrializing. An AI farm hosts your models, data and pipelines: the cost becomes an amortized investment, not a ballooning invoice.

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Use cases

Internal LLM on your documents and business knowledge, without data leakage
Fine-tuning open source models (Llama, Mistral, Qwen) on your data
High-volume inference: assistants, RAG, extraction, classification
Strict GDPR compliance: healthcare, legal, finance, defense

Reference architecture

Serving: vLLM or TGI with continuous batching and quantization: the best per-GPU throughput in open source.
Training: Fine-tuning pipelines (LoRA, QLoRA, full) orchestrated by Slurm or Kubernetes depending on scale.
Data: Dataset ingestion, vectorization and versioning, with high-performance storage for checkpoints.
MLOps: Model registry, continuous evaluation, canary deployments and drift monitoring in production.

What you receive

01
API vs self-hosted cost study on your real volumes, with a priced break-even point
02
Deployed AI stack: serving, pipelines, monitoring
03
Models evaluated on your use cases with quality metrics
04
Runbook, training and full handover

FAQ

Can an open source model really replace a proprietary API?: For most business use cases (RAG, extraction, classification, internal assistants), yes: an open source model fine-tuned on your data matches or exceeds generic API quality. We prove it with a quantified evaluation before any deployment.
At what volume does self-hosting become profitable?: As a rule of thumb, from a few tens of millions of tokens per day at sustained usage, break-even lands between 12 and 24 months. The audit computes it precisely with your volumes and target models.

Request a technical audit

Reference architecture

Serving: vLLM or TGI with continuous batching and quantization: the best per-GPU throughput in open source.
Training: Fine-tuning pipelines (LoRA, QLoRA, full) orchestrated by Slurm or Kubernetes depending on scale.
Data: Dataset ingestion, vectorization and versioning, with high-performance storage for checkpoints.
MLOps: Model registry, continuous evaluation, canary deployments and drift monitoring in production.

FAQ

Can an open source model really replace a proprietary API?

For most business use cases (RAG, extraction, classification, internal assistants), yes: an open source model fine-tuned on your data matches or exceeds generic API quality. We prove it with a quantified evaluation before any deployment.

At what volume does self-hosting become profitable?

As a rule of thumb, from a few tens of millions of tokens per day at sustained usage, break-even lands between 12 and 24 months. The audit computes it precisely with your volumes and target models.