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April 30, 2025

Enterprise Guide: Choosing Between On-premise and Cloud LLM and Agentic AI Deployment Models

This guide helps enterprises choose between on-premise and cloud deployment models for AI and LLMs. It analyzes key factors like security, availability, customization, and costs, highlighting the pros and cons of each option. On-premise solutions offer higher control, security, and cost advantages at scale, while cloud solutions provide faster startup and lower initial investment. Leaders must align deployment decisions with business needs, data sensitivity, and operational goals to maximize AI’s value and long term ROI.

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April 24, 2025

Allganize Launches Secure MCP-based Agent Builder for Enterprise AI

Allganize has launched a secure MCP-based Agent Builder within its Alli platform, enabling enterprises to create powerful AI agents optimized for on-premise and SaaS environments. Unlike other solutions, Allganize ensures secure, isolated MCP component execution to meet strict data security and compliance standards. The Agent Builder works with Agentic RAG, requiring no code, and supports use cases like enterprise search, data analytics, and submittal reviews. Allganize currently supports over 300 clients across finance, energy, manufacturing, and government sectors.

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April 22, 2025

The emergence of DeepSeek-R1, and what we must not overlook – Part 2

We are introducing DeepSeek-R1 in two blog posts that have recently surprised us. In this Part 2, we discuss the paradigm shift brought by DeepSeek-R1. Previously, in Part 1, we covered the background of its emergence and detailed the technical aspects of the model. Although the posts are long, we recommend reading both for a complete understanding.

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