As of 2025, the MLOps landscape is characterized by a diverse array of open-source and closed-source tools and platforms that facilitate the development, deployment, and monitoring of machine learning models. The landscape includes over 90 tools categorized into areas such as experiment tracking, data labeling, feature stores, model deployment, and more. Open-source solutions are favored for their flexibility and adaptability, while closed-source options offer enterprise-grade features and dedicated support. In addition, the emergence of LLMOps, or foundation model training frameworks, signifies a shift towards managing large language models with specialized tools such as Guardrails and LangChain. The article highlights the importance of evaluating MLOps tools based on factors like cloud strategy, integration with existing tech stacks, and user support. It also delves into core features necessary for effective MLOps, such as scalability, security, and compliance, while emphasizing the growing role of responsible AI practices and the integration of advanced technologies like serverless GPUs and vector databases.