Windows App 
MacOS (sillicon) 
You're losing enterprise clients to this.
When your AI feature runs on an external model provider, your clients' data leaves their network. For anyone handling confidential data, that's a dealbreaker.
Data leaves their network
Every call to an external LLM routes client data outside their trust boundary.
Their security team says no
CISO reviews the vendor. Sees data leaving the perimeter. Blocked — regardless of how good the feature is.
You can't guarantee what you don't control
Model training, log retention, provider-side breaches — you can't make promises about infrastructure you don't own.
The feature is built. Adoption stalls.
You've invested in the AI capability. Your highest-value clients won't touch it.
"But we have an enterprise account of our llm."
"Your data is not used to train our models" sounds nice, but these plans don't change the architecture. Your data still leaves your network and is processed on someone else's servers, and the guarantees stop at a contract.
Contracts aren't architecture.
A contract says data won't be used for training. But data is still transmitted, still processed, still logged on infrastructure you don't control.
The attack surface stays open.
External APIs introduce risk — prompt injection, data leakage, model-level exploits. These aren't resolved by a billing tier.
An open-source LLM deployed inside your own VPC. Managed by us.
Fully isolated, single-tenant infrastructure. No external API calls. No data in transit. Your security team can audit the entire stack.
Zero third-party exposure
The model runs inside your network. No query, no document, no session ever exits it.
~1/10th the cost of LLM APIs
At enterprise volumes, a fraction of what major LLM API providers charge.
Any open-source model. One click.
Switch between models as your needs change. No re-integration. No vendor lock-in.
Provisioned in an afternoon.
Managed end to end.
We handle the VPC, the model stack, and the ops. You ship the feature.
- Run any open-source model — switch as needed, no re-integration.
- Real-time GPU usage and billing in one dashboard.
- Native integrations with DigitalOcean, AWS, GCP and Azure.
Proven by GAIA Benchmark Testing
Barie adapts to your profession — becoming the AI agent you've always needed.
(Hardest)
* Human benchmark reference data sourced from the official GAIA paper. Full results: GAIA Paper (arxiv.org)
Three deployment options. One principle.
The model always runs inside your infrastructure. No external LLM calls. Ever.
Data never leaves your VPC.
Single-tenant, isolated VPC. No shared compute. No external model calls. Your security team can audit the architecture directly.
A fraction of what LLM APIs cost.
Predictable pricing. No per-token billing.
Any open-source model. One-click switching.
Llama, Qwen, Gemma, Mistral and others. Switch models without re-integration. No vendor lock-in to any single LLM provider.
Cloud (SaaS)
Get started immediately. No infrastructure to provision or manage.
- Zero setup, immediate access
- Full feature set
- Credit-based packages
On-Premises
Your GPUs. Your building. Barie installs and manages everything — you keep full physical control.
- Air-gapped · zero data egress
- GDPR / HIPAA / SOC 2 aligned
- Auto-install: CUDA, Docker, vLLM
Why Enterprises Pick Barie Over Public AI Platforms.
Lives Inside The Tools Your Team Already Runs On.
Barie acts where your work happens — without exporting a single record.
The things your CISO will ask first.
Quick answers to the most common questions.
