Self-Hosted AI

Feb 18, 2025
 | 
Product & Engineering

The Enterprise AI Adoption Challenge: A Security Leader's Perspective on Self-Hosting

Andrew Storms, VP of Security at Replicated, has seen firsthand how the push for innovation often clashes with enterprise security expectations. In this blog, he explores why self-hosted AI solutions are becoming a necessity, how they address key security concerns, and what AI startups can do to successfully navigate this evolving landscape.

Feb 7, 2025
 | 
Company

Why Small AI Companies Must Offer Self-Hosted Solutions to Win Enterprise Deals

For smaller AI companies looking to sell into the enterprise software space—whether using an open-source API or proprietary technology—offering a self-hosted version of your application will soon be non-negotiable for security-conscious ISVs. In fact, this transition is already underway.

Dec 16, 2024
 | 
Product & Engineering

Replicon Q4 - Summaries and Highlights

In RepliCon Q4, our quarterly virtual conference, we hosted sessions around Helm and defining air-gapped environments. We spoke with a current customer, H20.ai, about deploying AI-applications using our Embedded Cluster. Our VP of Product, Amber, reviewed product highlights from 2024 and looked ahead at the year to come.

Oct 11, 2024
 | 
Company

Announcing: The Commercial Software Distribution Handbook

Replicated is proud to announce the Commercial Software Distribution Handbook - a guide written for any and every company looking to distribute their enterprise software into self-hosted environments.

Mar 24, 2023
 | 
Case Studies

Customer Interview: AI Training Data Anonymization with Subsalt

In this customer interview, we delve into the ways in which Subsalt is utilizing Replicated to facilitate the anonymization of AI training. As an essential aspect of data privacy and security, anonymization ensures that sensitive information is protected and remains confidential. By using Replicated, Subsalt can create a secure environment where data is encrypted, and privacy is maintained throughout the AI training process.

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