Private LLM

Your agents' language model, running on your infrastructure

Lira supports both public and private model deployments. For enterprises that can't send conversation data to a third-party API, the same LLM that powers your agents runs entirely inside your own VPC or data center.

Enterprise deployments · dedicated onboarding · custom SLAs

What is a private LLM?

A private LLM is a language model deployed on infrastructure a single organization controls — its own servers, VPC, or data center — rather than called over the public internet through a shared third-party API. No prompt, transcript or customer data leaves that environment. Lira supports open-weight models (via TensorRT-LLM and similar serving stacks) deployed this way, alongside your choice of public model providers when private hosting isn't required.

Capabilities

What you get

Self-hosted inference

Serve the model from your own GPU infrastructure — on-premise or in your cloud VPC.

Zero third-party data exposure

Prompts, transcripts and customer data never leave your network boundary.

Model flexibility

Deploy the open-weight model of your choice — swap models without re-architecting your agents.

No added latency

Inference runs on infrastructure sized for your call volume, not a shared multi-tenant queue.

Full audit trail

Every inference call is logged on infrastructure you already control and audit.

Fallback options

Configure a public-model fallback for resilience without changing your default private deployment.

Use cases

Who this is built for

Banks & financial services

Meet data-residency and regulatory requirements that prohibit sending customer data to external APIs.

Healthcare providers

Keep patient conversations within infrastructure covered by your existing compliance program.

Government & public sector

Satisfy sovereignty requirements that mandate the model run within a specific jurisdiction.

FAQ

Common questions

Does this replace my existing infrastructure team's involvement?

No — private LLM deployment is a joint effort. Our team helps size and configure the serving stack; your infrastructure team owns the environment it runs in.

Can I still use a public model for some agents and private for others?

Yes. Model choice is configured per agent, so you can run a private LLM for regulated flows and a public model elsewhere.

What hardware does this require?

Depends on model size and call volume — our team sizes this with you during a technical scoping call rather than quoting a generic number.

Ready to deploy private llm?

Talk to our team about your infrastructure, compliance and scale requirements — we'll design the deployment around them.

Book a demo →