Nov 12, 2025

AI systems are shaping the next wave of cloud native development. 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 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 Adlik 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 net promoter score underscores a strong sense of community confidence in Adlik's trajectory. 4) Airflow and Metaflow Take the Lead in ML Orchestration Apache Airflow and Metaflow reached the “adopt” category for machine learning orchestration, reflecting widespread satisfaction with their maturity and usefulness. Metaflow topped maturity rankings, while Airflow received the highest usefulness and recommendation ratings. Both have proven central to managing complex ML pipelines that demand automation and reproducibility. 5) BentoML Finds Dual Success Across AI and ML Domains BentoML secured an “adopt” position in inferencing and a “trial” position in ML orchestration, confirming its versatility across domains. While developers appreciate its functionality, fewer consider it core to their workflows. The findings suggest that cross-domain tools can succeed but may face limits to leadership in specialized categories. 6) Model Context Protocol and Llama Stack Define Agentic AI Maturity Among agentic AI projects, Model Context Protocol and Llama Stack achieved “adopt” status for maturity and usefulness. MCP demonstrated the broadest appeal, with 80% of developers awarding top ratings. This performance highlights growing demand for frameworks that standardize AI agent context and communication. 7) Agent2Agent Captures Enthusiastic Endorsement Agent2Agent protocol achieved the strongest advocacy among all agentic AI tools, with 94% of current and former users recommending it. Though newer and less mature, developers recognized its strong potential and smooth integration into existing ecosystems. Its high recommendation score reflects optimism for agent-based architectures that connect multiple AI systems seamlessly. 8) LangChain's Popularity Faces Enterprise Reality Check While LangChain remains widely used, developer sentiment flagged concerns about maturity and scalability. Many cited challenges integrating it into enterprise environments, leading to lower reliability ratings. This gap between hype and practical resilience underscores the growing demand for production-ready agent frameworks. 9) Airflow Achieves Zero Negative Ratings on Usefulness Apache Airflow was uniquely rated with no negative feedback on usefulness, a rare distinction in the CNCF Radar. Developers praised its stability and integration strength across large-scale ML workflows. This reinforces Airflow's position as a foundational tool for orchestrating reliable, repeatable machine learning processes. 10) Cloud Native Patterns Now Central to AI and ML Development The report concludes that cloud native infrastructure is no longer optional for AI and ML practitioners. With 41% of developers now identifying as cloud native, CNCF technologies underpin both experimental and production workloads. The Radar's maturity gradient spanning projects like Nvidia Triton, Airflow and MCP, illustrates how cloud native design principles enable scalability, portability and operational efficiency for next-generation AI systems.