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Comparison

Privian vs self-hosted LLMs

When to choose self-hosted LLM inference, when to choose Privian in front of managed models, and how teams combine the two.

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At a glance

What each approach optimizes for

Self-hosted LLMs

Optimize for isolation. Prompts and responses never leave an environment you control. You take on inference operations, capacity planning and model maintenance as the cost of that isolation.

Privian in front of managed models

Optimize for prompt-level privacy on top of managed models. Supported entities are masked before the provider sees them, BYOK keeps the provider relationship yours, and the gateway retains no raw prompt or response bodies.

Side by side

Comparison

Categories chosen for what enterprise buyers actually decide on.

CategorySelf-hosted LLMsPrivian + managed models
Primary optimizationIsolation — data never leaves a controlled environmentPrompt-level privacy in front of managed model providers
PrivacyStrongest by construction — no third-party model sees promptsSupported entities masked before the managed model sees them; unsupported values pass through
Operational complexityHigh — model serving, GPUs, capacity planning, upgrades, evalsLow — hosted gateway, small JSON contract, BYOK for providers
Cost shapeMostly fixed (GPU capacity + ops headcount)Mostly variable (provider token spend + gateway usage)
LatencyBounded by your own infrastructureBounded by the upstream provider plus a thin gateway hop
ControlFull — model choice, weights, runtime, deployment topologyRouting, masking and BYOK; model behavior is the provider's
Maintenance burdenOngoing — model updates, security patching, observabilityMinimal — operated as a managed gateway
Flexibility of model choiceOpen-weight models you can run; closed models off the tableAny supported managed provider with a BYOK credential
Governance toolingWhatever you build or buy in your platformNot Privian's focus — pair with a governance layer if needed
IsolationStrong by constructionProvider boundary still exists — managed models see the masked prompt
Time to first requestWeeks to months, depending on infra maturityHours — sign up, BYOK, call the gateway

Fit

Choose self-hosted if…

  • Regulation, policy or contract requires that prompt and response data never leaves a controlled environment.
  • You have, or can build, the inference-platform capability (capacity, observability, model evals, on-call).
  • Your workload tolerates the cost shape of running model serving full time.
  • You need exact model and weight control — for example, fine-tuned open-weight models held private.

Fit

Choose Privian if…

  • You want to use managed models (GPT, Claude, Gemini) because of their quality, latency or feature coverage.
  • Your concern is prompt-level data exposure — names, emails, account ids, support text — not full data residency.
  • You want one place to enforce masking and BYOK across multiple providers without operating inference yourself.
  • You want to keep no raw prompt or response bodies at the gateway and route through your own provider credentials.

Hybrid patterns

Common ways teams combine both

Self-hosted and managed-via-Privian are not mutually exclusive.

  • Sensitivity-based routing

    Self-hosted for the highest-risk workflows

    Confidential, regulated or contractually-restricted workloads stay on self-hosted inference; lower-risk workflows use managed providers through Privian.

  • Region-aware

    Self-hosted in restricted regions

    Run an internal model in regions where managed providers are constrained, and use Privian elsewhere with prompt-level masking.

  • Use-case split

    Self-hosted for batch, managed for interactive

    Bulk processing of sensitive data runs against self-hosted models; user-facing features call frontier managed models through Privian.

Honest limitations

What Privian does NOT provide

If your requirement is on this list, choose another tool — or pair Privian with one.

  • Self-hosted model inference.
  • Prompt-injection or jailbreak defense.
  • Governance tooling (policy engines, per-tenant AI off-switches, fine-grained role workflows).
  • End-to-end audit logging of prompt and response content.
  • HIPAA / SOC 2 / PCI certifications at this time.

FAQ

Frequently asked questions

Should enterprises self-host LLMs?
Sometimes. Self-hosting is the right answer when regulation, policy or contract require that data never leaves a controlled environment, or when you have a strong reason to control the model and runtime end to end. It is not automatically the right answer for every privacy concern — many teams reach the privacy posture they need by combining managed models with prompt-level masking and BYOK.
Does self-hosting eliminate privacy risk?
No. It eliminates the third-party-model boundary, which is a meaningful risk reduction, but it does not by itself remove the work of data classification, retention, access control or output handling. Self-hosted inference shifts privacy work inward; it does not delete it.
Can I use managed models safely?
Many teams do. The common pattern is to mask sensitive values before a prompt reaches the provider, route through your own provider credentials (BYOK), keep no raw prompts or responses at the gateway, and use the provider's own enterprise terms. Privian implements that pattern.
When does Privian make sense?
When you want to use managed model providers but reduce the prompt-level data they actually see, and you do not want to run inference infrastructure yourself. If you already need to self-host for isolation reasons, Privian is the wrong layer to solve that.
Can Privian and self-hosted models coexist?
Yes. Many teams route the highest-sensitivity workflows to self-hosted models and lower-risk workflows through Privian to managed providers. The two are not in tension.

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