AI gateways in the enterprise

• Verfasst von: Posta, Christian E. [VerfasserIn]
• Verfasst von: Jausovec, Peter [VerfasserIn]
• Titel: AI gateways in the enterprise
• Titelzusatz: securing and governing GenAI and LLM API integration in the enterprise
• Verf.angabe: Christian Posta and Peter Jausovec
• Ausgabe: [First edition].
• Verlagsort: Sebastopol, CA
• Verlag: O'Reilly Media, Inc.
• Jahr: 2025
• Umfang: 1 online resource (48 pages)
• Illustrationen: illustrations
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: Intelligence artificielle ; Informatique
• Sach-SW: Systèmes d'information
• Sach-SW: Réseaux de stockage (Informatique)
• Sach-SW: Apprentissage automatique
• K10plus-PPN: 1921169303
• K10plus-PPN:
  Universitätsbibliothek
• Lokale URL UB: Zum Volltext
• Lokale URL UB: Bibliothek der Medizinischen Fakultät Mannheim der Universität Heidelberg
• Bibliothek/Idn: UW / m4696535738
• Lokale URL Inst.: Zum Volltext

Abstract

Generative AI and large language models (LLMs) have the power to transform your business--but is your infrastructure ready? This report explains why traditional networking solutions fall short and how AI gateways provide a secure, scalable foundation for AI adoption. Authors Christian Posta and Peter Jausovec introduce AI gateways as a critical tool for protecting API keys, enforcing data governance, and controlling costs--ensuring that enterprises can deploy AI safely and responsibly without security risks or runaway expenses. Designed for IT leaders, architects, and platform engineers, this report offers practical strategies for securely exposing LLMs, optimizing API consumption, and managing enterprise-wide AI initiatives. Whether you're planning your first AI implementation or refining an existing strategy, this guide provides essential insights to help you future-proof your AI infrastructure. Discover why AI gateways are the key to secure, safe GenAI integration Learn how to safely consume LLMs without compromising data security Control costs with semantic caching, rate limiting, and governance frameworks Evaluate when to use pretrained models or invest in custom AI solutions Implement an AI infrastructure that integrates seamlessly with enterprise systems.