Prem AI Blog

Sign in Subscribe

Latest — 28 Feb 2026

On-Premise AI Architecture: Complete Enterprise Deployment Guide for 2026

Most enterprise AI architecture guides start with the wrong question. They ask “cloud or on-prem?” when they should ask “what are we actually trying to protect, and what does our organization need to function?” The result: teams build infrastructure that doesn’t match how their organization actually adopts AI, or

More issues

PremAI vs Google Vertex AI: Privacy, Flexibility, and Cost Compared

Google Vertex AI is growing fast in enterprise. Gemini models, Vertex AI Studio for prompt design, deep GCP integration. For cloud-native teams on Google Cloud, it’s a compelling platform. Then you ask the hard question: Can I run this on my own infrastructure? Google’s answer is no. Vertex

PremAI vs Azure OpenAI: Which Enterprise AI Platform Gives You More Control?

Azure OpenAI is the default enterprise choice for many organizations. Microsoft ecosystem integration, OpenAI’s models, enterprise support agreements. For teams already deep in the Microsoft stack, it’s the path of least resistance. Then you hit the wall. “Can we run this on our own infrastructure?” No. “Why are

Private RAG Deployment: Building Zero-Leakage Retrieval Pipelines for Enterprise

Private RAG deployment matters when you’re handling sensitive data. But here’s the problem: most implementations leak information at multiple points without teams realizing it. We’ve seen this firsthand. The BadRAG attack’s optimized variant achieves 98.2% success rate by poisoning just 0.04% of your document

Private AI for Customer Support: Building LLM Helpdesks That Don’t Leak Customer Data

Private AI for customer support is no longer optional for regulated industries. The moment your support agent sends a ticket containing an account number to GPT-4, you’ve created a data flow your compliance team needs to document, justify, and defend. OpenAI’s enterprise API doesn’t train on your

Llama vs Mistral vs Phi: Complete Open-Source LLM Comparison for Enterprise (2026)

There is no "best" open-source LLM. Only the right LLM for your specific task, hardware, and constraints. That's not a cop-out. It's the reality every enterprise discovers after deploying their first model. The team that picked Llama 3.3 70B for a classification task

Best Open-Source LLMs for RAG in 2026: 10 Models Ranked by Retrieval Accuracy

The best LLM for RAG is two models working together. Your embedding model determines whether you retrieve the right chunks. Your generation model determines whether you turn those chunks into accurate answers. Pick the wrong combination and you'll feed irrelevant context to a capable LLM, or feed perfect

vLLM vs SGLang vs LMDeploy: Fastest LLM Inference Engine in 2026?

SGLang and LMDeploy are the fastest LLM inference engines in 2026, both delivering approximately 16,200 tokens per second on H100 GPUs. vLLM follows at around 12,500 tokens per second, a 29% gap. The best engine depends on your workload: SGLang excels at multi-turn conversations, LMDeploy dominates quantized model

What Is Confidential AI? The Security Gap Your Encryption Doesn’t Cover

Confidential AI protects data during processing, not just at rest or in transit. Learn how TEEs, attestation, and encrypted inference secure enterprise AI workloads.

Enterprise AI Security: 12 Best Practices for Deploying LLMs in Production

TL;DR: This guide covers 12 actionable security practices for production LLM deployments, mapped to OWASP's LLM Top 10 (2025) and Agentic Top 10 (2026). Each practice includes implementation code, threat context, and prioritization guidance. Enterprise AI security requires more than wrapping an LLM in a firewall. Production