Keep Your Data Safe and Your Costs Predictable with Private AI

The Problem: Relying solely on third-party LLM providers means sending potentially sensitive information outside your secure organization. This info potentially becomes part of their data ecosystem. This introduces significant risks:

• Data Leakage & Security Breaches: Your confidential information could be exposed through vulnerabilities or even used to train models that benefit your competitors.
• Loss of Data Sovereignty: You lose direct control over where your data resides and how it's processed, creating compliance nightmares for regulated industries.
• Runaway & Unpredictable Costs: API call-based pricing for powerful models can quickly escalate, especially with high-volume usage, making budgeting a guessing game.
• "Black Box" Operations: Limited visibility and control over the model's underlying infrastructure and update cycles can impact performance and reliability.

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The Solution: Private Model Deployments – Bringing AI Power In-House, On Your Terms.

Imagine harnessing the full power of cutting-edge open-source LLMs, like the formidable Llama 3 70B, but within the secure confines of your own environment. With private model deployments, you regain complete control, ensuring your data never leaves your sight and your AI strategy aligns perfectly with your operational and financial goals.

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How Private Model Deployments Put You in the Driver's Seat:

This isn't just about installing software; it's about architecting a secure, efficient, and customized AI powerhouse:

1. Choose Your Fortress – Private Cloud or On-Premise:

• Private Cloud Deployment: Leverage the scalability and managed services of cloud providers (AWS, Azure, GCP) by deploying models within your Virtual Private Cloud (VPC). Your data and models operate in an isolated segment, secured by your existing cloud security posture.
• On-Premise Deployment: For ultimate control and air-gapped security, deploy models directly onto your own hardware, such as servers equipped with powerful NVIDIA A100 GPUs. This is ideal for organizations with stringent data residency requirements or ultra-sensitive workloads.

2. Data Stays Home – Unbreakable Security: When your users interact with the LLM, or when your RAG system feeds it context, all data processing occurs within your controlled environment. Your prompts, your documents, your intellectual property – none of it is transmitted to an external LLM host. This virtually eliminates the risk of data leakage to third parties.

3. Safeguard Your Knowledge – Secure RAG Ecosystem:Your carefully curated RAG (Retrieval Augmented Generation) data – your company's knowledge base, product specifications, internal wikis – remains on your servers. The vector database and the document chunks it contains are only accessed by your privately hosted LLM, ensuring this critical strategic asset is never exposed.

4. Master Your Budget – Optimized GPU Cost Management:Running large models requires significant GPU power, which can be costly. Private deployments offer smart cost-saving strategies:

• Configure your infrastructure to activate GPU instances only when there's active demand for the LLM. No usage? No unnecessary GPU costs.
• Scheduled Hours of Operation: If your primary usage is during business hours, schedule your GPU resources to be active then and power down during off-peak times, drastically reducing operational expenses.

5. Full Control & Customization:You control the model version, update schedules, and fine-tuning processes. Tailor the LLM's parameters and integrate it seamlessly with your existing internal applications and workflows without external provider limitations.