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Unlike SaaS AI services that route prompts and data to external providers, IntraLLM runs entirely within your infrastructure. This architecture ensures:

• Complete data sovereignty — Zero data exfiltration; all prompts, documents, and responses remain inside your network
• Full model control — Own the stack, fine-tune, update, or replace models without third-party dependencies
• Offline-first operation — Fully functional without internet connectivity or external API calls
• Deep customization — Train on proprietary datasets, enforce compliance filters, and align outputs with internal standards

IntraLLM is model-agnostic and integrates seamlessly with a wide range of LLM and multimodal back-ends:

• Ollama — Run open-source models (Llama 3, Mistral, Qwen, etc.) locally with minimal overhead
• OpenAI-Compatible APIs — Connect to any REST endpoint following OpenAI’s schema
• Bring Your Own Model (BYOM) — Deploy custom or fine-tuned weights with full version control
• Multimodal Engines — Unified pipelines for text, vision, audio, and video processing

Model routing, A/B testing, and fallback configurations are managed directly from the IntraLLM dashboard.

Yes. IntraLLM is engineered for large-scale, regulated environments:

• Single Sign-On (SSO) — SAML 2.0, OIDC, and LDAP integration
• User Groups & RBAC — Granular permissions across users, workspaces, and model endpoints
• Cluster Manager — Horizontal scaling, load balancing, and high-availability node orchestration
• GPU Monitoring & Cost Tracking — Real-time utilization dashboards, allocation policies, and inference metrics
• Workflow Automation Engine — Visual pipeline builder for multi-step AI operations, triggers, and integrations

Yes. IntraLLM operates fully offline when deployed behind your firewall or in restricted networks. This makes it ideal for:

• Government & Defense (CUI/ITAR compliance)
• Healthcare & Life Sciences (HIPAA/GxP environments)
• Financial Services & Banking (PCI-DSS, SOX)
• Industrial & Manufacturing (OT/IT segmentation)

No outbound network calls are required for inference, RAG, or model management.

IntraLLM provides an integrated lifecycle for model adaptation:

• Fine-Tuning — LoRA/QLoRA adapters, instruction tuning, and domain-specific alignment
• Evaluation Suite — Built-in metrics for accuracy, latency, toxicity, and adherence
• Knowledge Base Manager — Versioned document stores with access controls and audit logs
• Model Comparison — Side-by-side benchmarking across prompts, datasets, and back-ends

All training and evaluation jobs are monitored via the GPU dashboard with automatic retry and resource capping.

IntraLLM offers flexible, transparent licensing tailored to enterprise scale and deployment type. More details are available in the DOCS -> Introduction -> Pricing