Running a private LLM involves understanding different privacy definitions and hardware costs. Physical privacy means prompts never leave your controlled machine, while contractual privacy relies on provider agreements. Technical privacy uses hardware that encrypts data, preventing operators from seeing prompts.Option one, a local LLM on your own hardware, offers the highest physical privacy. However, it involves a significant upfront cost for powerful GPUs, limited concurrency, and potential obsolescence. This setup is ideal for a single user with steady workloads, but requires ongoing time for maintenance.Option two involves cloud providers with contractual privacy guarantees. Major cloud services offer enterprise contracts that prevent prompt usage for training and allow for zero data retention configurations. This approach is suitable for large organizations needing service level agreements and established legal recourse.Option three, a private LLM gateway, offers a balance between local and cloud. These gateways use trusted execution environments (TEEs) for hardware-level privacy. They provide pay-per-use pricing without upfront costs and allow for hardware verification of data privacy.Gateways can run confidential models locally or route to frontier models, though the latter may still be seen by original providers. A key challenge remains private memory in shared agents, where data from one user can be exposed to others. Research shows significant privacy violations in multi-user systems, even with private LLMs.To mitigate shared memory risks, scope memory per user, enforce access at the storage layer, and redact sensitive data before it's stored. Treat all shared memory as potentially public. The cheapest option depends on volume; confidential tiers are cost-effective for low usage, while owned hardware can be cheaper for high, steady volume after depreciation.Ultimately, the choice depends on individual needs: local for solo users, cloud for enterprises needing SLAs, and gateways for teams prioritizing verifiable privacy. Regardless of the route, address shared memory issues before enabling multi-user access.