Private AI buyer alternatives
A serious buyer will not compare a new venture only with Lumo. The real alternatives range from a local model to an enterprise contract with a frontier-model vendor, to a verifiable confidential-computing provider.
The useful question is not “which product is private?” It is which party can read the content at each stage, what evidence supports that answer, and whether the workflow needs the product’s features.
Comparison
| Alternative | Live plaintext boundary | Main evidence | Best fit |
|---|---|---|---|
| Local AI | Customer endpoint | Local egress and configuration checks | Routine sensitive work that fits local models |
| Customer-owned deployment | Customer administrators and hardware | Deployment records and access controls | Teams with operating capacity |
| Confidential AI computing | Verified workload plus residual hardware trust | Client attestation and release policy | Tasks that need stronger remote models |
| Hosted privacy chat or proxy | Provider model runtime | Published policy and contract | Convenient lower-risk chat |
| Enterprise frontier AI | Named provider and enabled tools | Contract, DPA, retention, and tenant settings | Capability and enterprise integration |
| Ordinary consumer AI | Provider under consumer terms | Public product policy | Non-sensitive work only |
The rows are trust models, not a permanent ranking of vendors. A single provider can offer more than one route.
Hosted privacy chat and proxies
Lumo and Brave Leo are consumer-friendly privacy chat products. They reduce retention, training, and identity exposure relative to ordinary consumer chat, but hosted inference remains a live plaintext boundary.
Duck.ai and Kagi add a proxy pattern. They can minimize what an upstream model provider learns about an account or IP address, and they negotiate useful retention limits. They do not turn an ordinary upstream model API into confidential computation.
Confidential-inference specialists
Apple Private Cloud Compute, Venice, Tinfoil, and Privatemode show the state of the art for attested cloud inference. Their systems differ in client verification, release transparency, model choice, and what features remain available inside the boundary.
This is the closest technical comparison for an optional cloud boost. It is not a blank market, so a new venture needs a better professional workflow, local document handling, and visible assurance evidence.
Enterprise frontier AI
OpenAI, Anthropic, Microsoft Copilot, Google Gemini, and Mistral AI are the likely capability and procurement benchmarks. Their business products can offer no-training commitments, DPAs, retention controls, regional options, and established administration.
Those are substantial controls. They are usually contractual and operational controls, not a cryptographic assertion that the provider cannot read live plaintext while serving the request. Files, prompt caches, safety systems, chat history, web grounding, remote MCP tools, and connectors each need separate review.
Procurement screen
For every candidate, record:
- whether prompt, history, retrieval index, and attachments leave the customer environment
- who sees live plaintext during inference
- the exact training, retention, abuse-monitoring, and feedback terms for the chosen plan
- location as a contractual or configuration fact, rather than an inference from a brand name
- every connector, grounding feature, tool, cache, and support route
- whether a client can verify a released workload or only relies on the provider’s statement
- model licence, portability, export, deletion, audit, and termination rights
The venture should make this comparison part of Private AI services#AI procurement and assurance. Its different offer is not that all competitors are unsafe. It is a control plane that makes the customer’s actual choice visible, minimal, and difficult to downgrade silently.