Privian vs LiteLLM

• Breadth of provider routing across 100+ models matters more than data minimisation
• You want to self-host the proxy and keep full infrastructure control
• You need streaming or tool/function calling today, not in a future beta
• An open-source, self-hostable license is a hard requirement

• You want supported personal and sensitive data masked before it leaves your boundary, not just routed
• You want a hosted gateway with one documented data path, rather than an operational responsibility you assemble yourself
• An enterprise buyer or security reviewer needs evidence of what leaves your systems and who holds credentials
• You want BYOK with encrypted-at-rest keys and zero raw-prompt retention out of the box

• 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

Is Privian a LiteLLM alternative?

Privian is an alternative specifically for teams whose priority is masking sensitive data before it reaches the model, with a hosted gateway and one documented data path. If your priority is routing breadth across many providers or self-hosted control, LiteLLM is a capable, purpose-built open-source project and likely the better fit.

Is Privian a replacement for LiteLLM?

Only if your primary requirement is privacy-first routing rather than open-source provider abstraction. LiteLLM optimises for normalising many provider APIs and self-hosted control; Privian optimises for masking sensitive data before egress and giving a security reviewer one documented path.

Can Privian work alongside LiteLLM?

Yes. Some teams use LiteLLM as an in-cluster abstraction layer across providers and place a privacy-first gateway in front of the final egress hop to the model. The two are not mutually exclusive.

Who is LiteLLM for?

Teams that want an OpenAI-compatible interface across a large number of providers, prefer to self-host and modify open-source infrastructure, and need features such as streaming and tool calling that are already mature in the underlying providers.

Who is Privian for?

Teams whose primary constraint is what reaches the model, not how many providers they can route to — typically because a customer, security reviewer or compliance process is asking what data leaves the boundary before it reaches an LLM.

How does credential handling differ?

With a self-hosted LiteLLM proxy, you configure and hold provider credentials inside your own deployment, and you are responsible for how they are stored and rotated. Privian uses BYOK: your provider key is stored AES-GCM encrypted at rest and decrypted only in-process at request time, on Privian's hosted gateway.

How does retention differ?

LiteLLM's retention behaviour — logs, spend tracking, callback data — depends entirely on how you configure and operate your own deployment. Privian does not persist raw prompts or responses on its hosted gateway; only structural counters are retained for billing and observability.

Does Privian mask data before it reaches the provider?

Yes. Supported personal and sensitive entities are detected in the assembled prompt and replaced with deterministic placeholders before the request is forwarded to the model provider, then rehydrated in the response. LiteLLM's routing layer forwards the prompt as given; masking is not a stated focus of the project.

What does Privian not support yet?

Privian's current beta does not support streaming, tool/function calling, JSON mode, multimodal input or the Responses API. If you depend on any of these today, LiteLLM — or continuing to call the provider directly — is the more capable choice until Privian's beta covers them.

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.

To see how the underlying approach works in the product, read the LLM gateway and PII masking product pages, or check current pricing before evaluating LiteLLM against Privian.

• LLM Gateway

• AI Security Layer

• Data path

• Pricing and packaging

• PII Masking

• Prompt Security

• LLM Security

• Architecture

• Security

• Trust Center

• Privian Blueprint (evaluation package)

• Enterprise AI procurement framework

• AI vendor risk assessment

• AI vendor due diligence checklist

• AI security questionnaire