How to evolve your tech stack to leverage LLMs in 2025

Generative AI is recognized as a transformative force across industries, necessitating companies to integrate AI-powered features into their offerings to remain competitive. By 2025, the landscape of large language models (LLMs) has matured significantly, with advanced models like OpenAI's GPT-5 and open-source alternatives such as DeepSeek V3.2 offering powerful capabilities. The narrowing quality gap between proprietary and open-source models presents enterprises with a complex decision when implementing LLMs, considering factors such as mission-critical needs, resource availability, and time-to-market pressures. Companies have multiple approaches, ranging from using third-party SaaS LLMs for rapid deployment to developing in-house models for complete control and customization. Success also increasingly depends on evolving the tech stack for AI integration, prioritizing observability and debugging, and ensuring comprehensive data context to enhance LLM performance, particularly in complex tasks like debugging distributed systems. This evolution underscores the importance of strategic planning and investment in the right tools and practices to maximize the benefits of AI technologies.