Deployment and management of large language models within your infrastructure and security boundary - cloud, hybrid, or fully private.
Deploying an LLM at enterprise scale is more than an API key - it's model selection, infrastructure sizing, cost management, security boundary decisions, and ongoing operational management.
Advatech deploys and manages LLM infrastructure to your specific requirements, whether that's a managed cloud API, a self-hosted open-source model, or a fully private deployment within your own environment.
Choosing the right model against your accuracy, latency, cost, and data residency requirements.
Right-sized compute infrastructure for your actual expected load, not guesswork.
Open-source model deployment within your own infrastructure for full data control.
Ongoing monitoring and optimization of inference cost against usage patterns.
Model fine-tuning against your specific domain and use case where it materially improves results.
Uptime, latency, and output quality monitored continuously in production.
Data residency, latency, cost, and accuracy requirements defined upfront.
Model and infrastructure selected and sized against those requirements.
Deployed to cloud, hybrid, or fully private infrastructure as required.
Ongoing monitoring, cost optimization, and reliability management.
Yes - self-hosted deployment of open-source models within your own environment is a core option, for organizations that can't send data to a third-party API.
Through right-sized infrastructure, model selection appropriate to the task complexity, and ongoing usage monitoring - not defaulting to the largest, most expensive model for every request.
Both - fine-tuning is applied where it materially improves results for your specific domain, not as a default step.