Private inferencedefinition and how it works in 2026
- Private inference
- Running LLM inference inside your security perimeter (VPC, on-prem, confidential compute) so prompts and outputs never leave your control. Mandatory for regulated industries.
Private inference means the model runs in infrastructure you control — your VPC, your data center, a confidential-computing enclave — rather than calling a shared hosted API. Prompts and responses never traverse a vendor's servers; data residency stays where compliance requires.
There are three common deployment patterns in 2026: (1) Self-hosted in your VPC via Bedrock/Vertex/Azure private endpoints — easy procurement, vendor-managed weights. (2) On-prem with downloaded weights (Llama, Mistral, DeepSeek, Qwen) running on your GPUs. (3) Confidential-computing enclaves (Intel TDX, AMD SEV) that even prevent the cloud provider from seeing the data — the highest-assurance option.
Use cases that justify the cost: healthcare PHI, financial PII, defense, sovereign-data jurisdictions. The TCO of private inference is materially higher than hosted (2–10× depending on scale), but the regulatory and trust requirements often leave no choice.
Frequently asked
Private inference vs on-prem — different things?+
On-prem is one form of private inference. Private inference is the broader category — includes on-prem, private VPC endpoints, confidential-computing enclaves. All share the property that data doesn't leave your control.
How much more expensive is private inference?+
2–10× of equivalent hosted-API inference, depending on scale. Below ~$100K/month in inference, the premium can be hard to defend. At enterprise scale + regulatory requirements, the premium is usually unavoidable.