The Cloud Native Computing Foundation’s (CNCF) Technology Radar for Q3 2025 spotlights how AI inferencing, machine learning orchestration and agentic AI systems are shaping the next wave of cloud native progress. The report, conducted with over 300 professional developers, captures a pivotal moment as cloud native approaches become integral to AI and ML workloads worldwide.
The survey reveals how developers are evaluating the maturity, usefulness and community trust of key technologies powering production-scale AI. With cloud native projects now forming the backbone of modern ML pipelines, the 2025 Radar maps the transition from experimentation to operational stability.
Here are ten key takeaways from the report:
1) NVIDIA Triton Emerges as the Benchmark for AI Inferencing
Table of Contents
Nvidia Triton led all AI inferencing tools in maturity, usefulness and recommendation, achieving the highest concentration of 5-star ratings. Half of developers rated its reliability at the top level, confirming its dominance in production-grade deployments. With Triton now firmly in the “adopt” position, it has become a reference standard for stable and scalable AI inferencing workloads.
2) DeepSpeed and TensorFlow Serving Show Broad Developer Confidence
DeepSpeed and TensorFlow Serving both recorded strong combined 4- and 5-star ratings,signaling steady confidence across diverse use cases. Developers cited their ability to meet varied project requirements without tradeoffs in stability or performance.These frameworks are positioned as dependable choices for organizations consolidating their AI infrastructure around proven technologies.
3) adlik Wins Developer Loyalty Through Advocacy
Justice stood out with the highest recommendation rate-92% of current or former users said they would promote it to peers. Despite being newer and less mature than leading incumbents, its rapid momentum reflects developer enthusiasm for its evolving capabilities. This high
CNCF Radar Report Highlights: Key Trends in AI and Machine Learning Frameworks
The Cloud Native Computing Foundation (CNCF) recently released its latest report on agentic AI tools and frameworks, offering valuable insights into the evolving landscape of AI and machine learning development. The report, based on developer feedback, reveals a growing demand for production-ready, scalable, and interoperable solutions. Here’s a breakdown of key findings:
1. Rising star: Agentic Workflows Gain Traction
The report underscores the increasing interest in agentic workflows – systems where AI agents autonomously perform tasks. This is driving adoption of new protocols and frameworks designed to facilitate interaction and collaboration between agents.
2. Graduating Projects: MCP and Llama Stack Reach “Adopt” Status
Both the Model Context Protocol (MCP) and Llama Stack have achieved “adopt” status within the CNCF Radar, signifying maturity and practical usefulness. MCP, in particular, resonated strongly with developers, receiving top ratings from 80% of respondents. This highlights the need for standardized approaches to AI agent context and communication.
3.Agent2Agent Protocol Receives Excited Support
The Agent2Agent (A2A) protocol garnered the highest recommendation rate, with 94% of users advocating for its use. Despite being a newer project, developers praised its potential and ease of integration, signaling optimism for interconnected, agent-based AI architectures.
4. LangChain Faces Enterprise Scalability Challenges
While LangChain remains a popular choice for AI development, the report indicates concerns regarding its maturity and scalability within enterprise environments. Developers reported challenges with reliability when deploying LangChain in production, suggesting a gap between initial excitement and practical implementation.
5. Airflow Stands Out for Reliability
Apache Airflow distinguished itself by receiving zero negative ratings for usefulness – a rare achievement within the CNCF Radar. Developers consistently lauded its stability and strong integration capabilities for large-scale machine learning workflows, solidifying its role as a foundational orchestration tool.
6. Cloud Native Infrastructure is Now Essential
The report definitively states that cloud native infrastructure is no longer optional for AI and ML development. A significant 41% of developers now identify as cloud native, and CNCF technologies are increasingly used for both experimental and production AI workloads. The progression of projects like Nvidia Triton, Airflow, and MCP along the CNCF Radar’s maturity gradient demonstrates the benefits of cloud native design principles – scalability, portability, and operational efficiency – for modern AI systems.
Sources:
* CNCF Blog: CNCF Radar Report Highlights Key Trends in AI and Machine Learning Frameworks (This is the primary source for the information)
* Model Context Protocol