Self Hosted LLM Services

The choice depends on requirements for data control, scalability, and cost structure. OpenAI API is suitable for quick launches and smaller artificial intelligence workloads, while ready-hosted infrastructure provides greater control over data, models, and infrastructure costs at scale.

We work with leading open-source llm self-hosted frameworks, including Llama models and other modern solutions from Hugging Face. This allows us to select the optimal models for specific tasks.

The foundation is GPUs with sufficient VRAM capacity for processing workloads. Fast RAM and high-performance storage arrays are also important for retrieval systems and vector databases.

Yes, we configure environments both in private cloud setups and on-prem company infrastructure. This ensures a high level of control and compliance with internal security standards.

For high-load processing workloads, we use Kubernetes orchestration, autoscaling, and containerized inference services. This enables resource distribution within the cluster and increases system throughput for large volumes of requests.

We reduce costs through model quantization and the use of efficient processing engines such as vLLM. Monitoring and telemetry allow control over GPU utilization and inference expenses.

Security is achieved through a multi-layered approach: isolated compute environments, access control, and secure data transfer channels. We also configure local access points to minimize the transfer of embeddings and requests to external networks.

Yes, modern technology stacks make it possible to build complex workflows where different models handle separate stages of the process. We integrate them into a unified system using orchestration frameworks.

Yes, we specialize in migrating artificial intelligence systems from paid cloud solutions to proprietary self-hosted infrastructure, helping adapt application logic and configure the new environment.

Depending on the complexity of the architecture and scaling requirements, a basic production-ready deployment usually takes from several weeks to 1–2 months. The use of ready-made pipelines and proven configurations significantly reduces launch time.