On-premise vs cloud AI: when to keep data in-house

The default is cloud, but it is not always right

Cloud AI is the obvious choice for most teams. No hardware to manage, no GPUs to procure, and models that improve without your involvement. For many workloads, cloud APIs from OpenAI, Anthropic, or AWS Bedrock are the fastest path to value.

But cloud AI has limits. Every prompt you send crosses your network boundary. For banks, hospitals, defence organisations, and law firms, that is not a trade-off you can accept. When your data is the competitive advantage, cloud AI becomes a liability.

When on-premise AI makes sense

Regulated industries with strict data residency requirements. Healthcare organisations processing patient records under DISHA or HIPAA. Banks handling transaction data under RBI guidelines. Government agencies with classified workloads.

High-volume inference workloads where cloud API costs become prohibitive. If you are processing thousands of documents per day, the per-token cost of cloud APIs can exceed the amortised cost of owning GPUs within months.

Air-gapped environments where internet connectivity is unavailable or restricted. Defence installations, mining operations, and remote manufacturing plants need AI that works without a network connection.

What you need to run AI on-premises

A GPU server with enough VRAM to load your chosen models. For a 7B parameter model, a single GPU suffices. For 70B models or multi-user setups, you need 2-4 GPUs with NVLink interconnects.

A serving framework like vLLM or TGI that handles concurrent requests efficiently. A vector database for RAG pipelines. An authentication layer that connects to your existing LDAP or SSO.

The hard part is not the hardware. It is the integration: connecting AI to your document management system, your ERP, your internal knowledge base. This is where most DIY deployments stall.

The middle path: pre-configured AI servers

You do not have to build from scratch. Pre-configured AI servers ship with models, frameworks, and security policies already installed. Your IT team racks the server, connects it to the network, and users start querying the same day.

This approach gives you full data sovereignty with the simplicity of a managed service. Models can be updated by the vendor without exposing your data. Support contracts cover monitoring, patching, and incident response.