Why deploy LLMs privately? AI compute appliances vs. cloud GPU rental
More and more companies use large language models (LLMs) for knowledge bases, customer service and document processing. Public LLM services are the fastest way to start. But once the data is sensitive and usage grows, private deployment becomes hard to ignore.
Why consider private deployment
- Data security: Contracts, customer records and internal policies should stay inside your own environment.
- Long-term cost: When usage is steady and ongoing, the total cost of your own compute is more predictable.
- Stability and control: You are not affected by rate limits, price changes or API changes from an outside service.
Two common approaches
| Approach | Pros | Considerations |
|---|---|---|
| AI compute appliance | Hardware and software come preinstalled, data stays fully on-premises, long-term cost is controllable | Upfront purchase, plus a place to house it and staff to run it |
| Cloud GPU rental | Fast to set up, pay by the hour, good for short-term trials | Costs add up with long-term use, data sits on the cloud platform |
How to choose
- Validate before you invest: Run a POC on a small dataset to confirm the use case works.
- Consider how heavily you'll use it: Renting is more flexible for short trials. Long-term, steady, high-volume use is a better fit for an appliance.
- Consider where it will live: If you don't have a suitable server room, you can colocate the appliance in a data center and let the provider manage it.
Don't forget security
Running the model on-premises doesn't solve everything. AI applications still face risks such as prompt injection, sensitive data leaks and API abuse. Plan access control and auditing before launch, for example by deploying an AI Firewall.
HyperSource offers the AI Compute Appliance Pro 6000 / Pro 6000D and AI Application Development services, from use-case assessment to launch and operations. Contact our consultants to learn more.