This report identifies key demand-side trends in the large language model operations space, helping enterprises improve their AI operations management capabilities. It provides an overview of key observations and business challenges that Avasant considers important to highlight in this space.

Why read this Market Insights?

The AI operations management landscape has evolved beyond machine learning to large language model operations (LLMOps), driven by enterprise adoption of generative AI (Gen AI) and government mandates for responsible AI. LLMOps platforms enhance efficiency through real-time local AI inference, data localization compliance, and improved infrastructure visibility with dynamic workload scaling. To further support Gen AI scalability, platform vendors are integrating cognitive features such as real-time anomaly detection for monitoring model performance and data drift, no-code/low-code development studios, and GPU splitting for optimized resource allocation.

The Large Language Model Operations Platforms 2024–2025 Market Insights™ aids organizations in identifying important market trends and expectations, guiding strategic decisions to optimize operational efficiency and performance in AI operations management.

Methodology

The industry insights presented in this report are based on our ongoing interactions with enterprise CXOs and other key executives, targeted discussions with platform vendors, subject matter experts, and Avasant Fellows, analyst insights from primary and secondary research, and lessons learned from consulting engagements.

Table of contents

About the report (Page 3)

Executive summary (Pages 4–7)

• • Definition and scope of LLMOps platforms
  • Avasant recognizes 12 top-tier vendors with large language model operations capability
  • Key enterprise LLMOps platform trends shaping the market

Demand-side trends (Pages 8–14)

• • LLMOps adoption grew by seven times, driven by the enterprise’s need to operationalize Gen AI deployments and comply with government mandates.
  • On-premises and hybrid platform deployments have doubled in a year, driven by heightened data security concerns, lower latency needs, and greater demand for customization.
  • High-tech enterprises lead the adoption of LLMOps platforms, closely followed by healthcare and life sciences and insurance sectors.
  • Vendors are offering cognitive features across the value chain to enhance LLMOps capabilities.
  • Cost and resource optimization remains a major challenge for enterprises implementing Gen AI across workflows, driving the adoption of LLMOps platforms.
  • With the rise of autonomous AI agents, enterprises are transitioning to AgentOps.

Key contacts (Page 15)

Read the Research Byte based on this report. Please refer to Avasant’s Large Language Model Operations Platforms 2024–2025 RadarView™ for detailed insights on the platform vendors and supply-side trends.