On-Premise Deployment
Self-hosted open-source models, your infrastructure, your data. Full control — and full responsibility for every layer of the stack.
Security Surface
On-premise deployments eliminate API-level data exposure but introduce infrastructure-level responsibilities. Every component you host is a component you must secure.
Model Weight Security
Open-source model weights stored on local GPUs or servers are a high-value target. If an attacker accesses the model files, they can extract training data artefacts, fine-tuning data, or deploy a poisoned model in its place.
Prompt Injection
Open-source models may have weaker built-in guardrails than commercial APIs. Prompt injection attacks — where user input manipulates the model's instructions — can be more effective against self-hosted models without additional hardening.
Inference Server Exposure
Self-hosted inference servers (vLLM, Ollama, TGI) expose HTTP endpoints internally. If network segmentation is poor, these become lateral movement targets — an attacker on the network can query your model directly.
Data Pipeline Leakage
RAG pipelines, embedding stores, and context databases all contain sensitive business data. On-prem means this data never leaves your network — but internal access controls are critical.
MCP Server Attack Surface
MCP servers bridge your agents to internal tools and databases. Each server is an authenticated connection point — a compromised MCP server gives an attacker the same access the agent has.
Supply Chain (Model Downloads)
Downloading models from Hugging Face or other registries introduces supply chain risk. Compromised model weights could contain backdoors or biased behaviour not present in the original release.
Why On-Premise?
Data Sovereignty
No data leaves your network. Required for regulated industries (healthcare, finance, government) and organisations with strict data residency requirements.
Cost at Scale
API costs scale linearly with usage. Self-hosted inference has high fixed costs but low marginal costs — at volume, it's significantly cheaper.
Customisation
Fine-tune models on your data, control tokenisation, adjust inference parameters, and run specialised models not available via API.
Latency Control
No round-trip to external APIs. Co-locate inference with your data for sub-100ms response times when it matters.
Considering an on-premise deployment?
I help plan and build self-hosted AI infrastructure — from GPU provisioning to production monitoring.