Get Started

Comparison

Privian vs Private AI

Both Private AI and Privian protect sensitive data before it reaches AI models. This page compares them on product philosophy, deployment model, target customers, AI architecture and enterprise adoption strategy — so founders, engineering teams and enterprise buyers can pick the right fit for their organisation.

Get StartedRead the docs

Quick summary

At a glance

Choose Privian if you are building or scaling an AI product and need to become enterprise-ready quickly — an opinionated, AI-native privacy platform that ships as a hosted gateway on the LLM path, without adding new infrastructure to run or govern.

Private AI

Choose Private AI if you are an organisation with a mature enterprise privacy programme and broader privacy infrastructure needs — where AI is one of several data flows that must fit into an existing privacy platform, deployment footprint and governance model.

Privian

Choose Privian if you are building or scaling an AI product and need to become enterprise-ready quickly — an opinionated, AI-native privacy platform that ships as a hosted gateway on the LLM path, without adding new infrastructure to run or govern.

Definitions

What each product is

Comparison

Side-by-side comparison

Grounded in publicly available product positioning. Where we are not confident, we say so.

CapabilityPrivate AIPrivian
Target customerOrganisations with mature enterprise privacy programmes and broader privacy infrastructure needsAI startups and AI SaaS companies scaling toward enterprise customers
Product philosophyBroad privacy platform intended to serve many data flows and environments across the organisationOpinionated, AI-native privacy platform focused end-to-end on the AI product path
Deployment approachFlexible deployment across managed, on-prem and self-hosted footprints to match enterprise infrastructure preferencesHosted gateway on the LLM path; adopted by changing an endpoint, no new infrastructure to operate
Architecture philosophyPlatform architecture designed to be embedded into an organisation's existing privacy and data stackGateway architecture designed around a clean AI data path: detection → masking → BYOK provider call → rehydration in a single in-memory pass
AI-native workflowsAI is one workload among many; AI-specific flows are assembled by the customer around the broader platformPrompt privacy, rehydration, LLM routing, provider abstraction and trust posture are all first-class in the product
Prompt privacyAchievable when the customer wires the platform into the prompt path as part of a broader privacy designBuilt-in default: supported entities are masked before egress and rehydrated in the response on every request
LLM gateway capabilitiesNot positioned primarily as an LLM gateway; LLM routing typically lives elsewhere in the customer's stackNative LLM gateway across OpenAI, Anthropic, Google and other providers with BYOK and no raw-prompt persistence
Developer experiencePlatform-scale integration effort aligned with enterprise change-management and rollout cyclesOpenAI-style JSON contract; typical AI team can integrate in hours, not sprints
Time to implementationWeeks-to-months when factoring in platform deployment, integration and organisational rolloutDays: BYOK provider keys, point the SDK at Privian, ship
Enterprise readinessServes organisations that already operate enterprise privacy programmes and infrastructureDesigned to give AI-native companies enterprise-grade posture — prompt privacy, BYOK, zero retention, trust documentation — without becoming an infrastructure operator
Security review supportAligned with enterprise buyer processes typical of established privacy platformsEnterprise Trust Package, Blueprint, Data Path documentation and answers to common AI security questionnaires shipped as product
Trust resourcesVendor-published enterprise materials; see private-ai.com for the canonical setPublic Trust Center, Blueprint, Enterprise Trust Package, AI Vendor Risk Assessment, AI Vendor Due Diligence Checklist, AI Security Questionnaire
Operational complexityHigher when self-hosted or embedded across the organisation; matches customers that expect to operate privacy infrastructureLow: nothing to install, deploy or scale beyond BYOK credentials and an endpoint change
Best fitLarger organisations aligning AI with an existing enterprise privacy programme and infrastructureAI-native companies that need enterprise-grade AI privacy posture quickly, without adding architectural complexity

Architecture

Architecture differences

The application sends a raw prompt to the gateway. The gateway replaces sensitive values with placeholders and forwards the masked prompt to the LLM provider. The provider returns a response with placeholders. The gateway rehydrates placeholders to the original values before returning the response to the application. The provider never sees original values.ApplicationRaw promptPrivian gatewayMask · Route · RehydrateLLM providerSees masked prompt onlypromptmasked promptresponse w/ placeholdersrehydratedBYOK trust boundary
Prompt path through a privacy-first gateway— Original values never cross the BYOK boundary.

Private AI

Private AI's architecture is platform-oriented and deployment-flexible. It is built to slot into an organisation's existing privacy stack across multiple data flows and environments, with deployment options (managed, on-prem, self-hosted containers) that suit organisations that already run privacy infrastructure and want AI to conform to it.

Privian

Privian's architecture is AI-native and gateway-first. Each LLM request runs through detection → masking → BYOK provider call → rehydration in a single in-memory pass, exposed through a small OpenAI-style JSON contract. The design goal is that an AI team can adopt enterprise-grade prompt privacy in days by changing an endpoint — with no new infrastructure to deploy, scale or maintain.

Privacy & security

Privacy and security positioning

What Privian optimises for

  • Prompt-level data protection
  • Supported PII and sensitive-value masking
  • Privacy-first routing with BYOK
  • No raw-prompt persistence; structural observability only

What Privian does NOT claim

  • Prompt injection or jailbreak defence
  • HIPAA / SOC 2 / PCI certification
  • Tool / function calling security guarantees
  • Downstream model behaviour guarantees

When to choose

When to choose Private AI

  • You are a larger organisation with an established enterprise privacy programme, and AI is one of several data flows that must fit into it
  • Your privacy, security and platform teams standardise on a single privacy platform across many workloads and environments
  • On-prem or self-hosted deployment is a hard requirement from your privacy or infrastructure organisation
  • Your buying, security and rollout processes are optimised for platform-scale deployments rather than AI-team-scale adoption

When to choose

When to choose Privian

  • You are an AI startup or AI SaaS company that needs to become enterprise-ready quickly on the AI privacy dimension
  • You want an opinionated, AI-native path — prompt privacy, BYOK and trust documentation — instead of assembling a platform yourself
  • You want a hosted gateway you can adopt by changing an endpoint, with no new privacy infrastructure to run
  • Enterprise sales, security reviews and procurement questionnaires are becoming the gating factor on AI deals and you need trust assets fast

Framework

Evaluation decision

  1. 01

    Assess Private AI

    Choose Private AI if you are an organisation with a mature enterprise privacy programme and broader privacy infrastructure needs — where AI is one of several data flows that must fit into an existing privacy platform, deployment footprint and governance model.

  2. 02

    Assess Privian

    Choose Privian if you are building or scaling an AI product and need to become enterprise-ready quickly — an opinionated, AI-native privacy platform that ships as a hosted gateway on the LLM path, without adding new infrastructure to run or govern.

  3. 03

    Validate scope

    Confirm required capabilities and current limitations against the evaluation criteria.

Transparency

Honest limitations

Privian is in active development. Listing what it does not do today is part of how we earn trust — expect this list to shrink over time.

  • No tool / function calling in the current beta
  • No native provider token streaming yet (stream: true is artificially chunked)
  • No OpenAI SDK drop-in compatibility
  • No claim to detect or block prompt injection or jailbreaks
  • No Norwegian fødselsnummer masking yet
  • No custom user-defined entity types yet
  • No HIPAA / SOC 2 / PCI certifications at this time

FAQ

Frequently asked questions

What is the difference between Private AI and Privian?
Both platforms protect sensitive data before AI model interactions. The main differences are philosophical and organisational. Private AI is generally suited to organisations with mature enterprise privacy programmes that want a broad privacy platform spanning many data flows and deployment models. Privian is an AI-native privacy platform for teams building AI products that need enterprise-grade posture — prompt privacy, BYOK, zero retention, trust documentation — without operating additional infrastructure.
Is Privian a simplified or less capable alternative to Private AI?
No. Both products are technically capable of protecting sensitive data before AI model interactions. Privian is opinionated rather than simplified: it is optimised end-to-end for teams building and scaling AI products, with prompt privacy, an LLM gateway, rehydration, BYOK and trust resources delivered as one product path.
Which fits an AI startup or AI SaaS company better?
Typically Privian, because it is designed to give AI-native companies enterprise-ready posture on the AI privacy dimension in days rather than sprints, without adding infrastructure to run. Private AI is generally a better fit when the organisation already has an enterprise privacy programme and wants AI to fit into it.
Which fits a larger enterprise better?
It depends on how AI privacy is governed. Organisations that already run a broader privacy platform and want AI to conform to it often prefer Private AI. Organisations where AI product teams own the AI privacy path and need to move at product speed often prefer Privian. Many enterprises end up using both — one for organisation-wide privacy programmes, one on the AI product path.
Can Private AI and Privian be used together?
Yes. A common shape is a broader privacy platform such as Private AI at the organisation level for enterprise-wide privacy programmes, with Privian on the AI product path providing the LLM gateway, prompt privacy and trust posture that AI teams and their enterprise buyers expect.
What is Prompt Privacy?
Prompt Privacy is the discipline of minimising what personal or sensitive content reaches an LLM provider. In Privian this is implemented as masking supported entities before the provider call and rehydrating them in the response, with no raw-prompt persistence.
How does time to implementation differ?
Privian is designed for adoption in days: BYOK provider credentials, change the endpoint, and prompt privacy is live across supported providers. Private AI implementations are typically scoped like enterprise privacy platform rollouts and align with the customer's broader deployment, integration and change-management cycles.
How does enterprise readiness compare?
Both target enterprise-grade privacy outcomes. Private AI serves organisations that already operate enterprise privacy programmes. Privian is designed to give AI-native companies the artefacts enterprise buyers ask for — Trust Center, Enterprise Trust Package, Blueprint, Data Path, AI Vendor Risk Assessment, AI Vendor Due Diligence Checklist and AI Security Questionnaire responses — as part of the product.
Does Privian block prompt injection?
No. Privian focuses on prompt-level data protection — masking supported personal and sensitive values before they reach the model. It does not claim to detect or block prompt injection or jailbreaks. If injection defence is your primary requirement, a dedicated LLM firewall is a better fit.
Does Privian support native streaming?
Not in the current beta. The gateway accepts stream: true and returns artificially chunked text, but it does not pass through native provider token streams yet.

Related

Related resources

Other comparisons

Plans & pricing

See pricing for Privian — a privacy-first alternative to Private AI

BYOK, zero retention, prompt-level masking. Pricing is published transparently; Privian is in beta and limits may change.

View pricingRead the docs

Start building

Try Privian

Get StartedView pricing