According to Forbes, the Cloud Native Computing Foundation’s Q3 2025 Technology Radar surveyed over 300 professional developers and found Nvidia Triton leading all AI inferencing tools with half of developers giving it top reliability ratings. DeepSpeed and TensorFlow Serving both recorded strong combined 4- and 5-star ratings, while newcomer Adlik achieved a remarkable 92% recommendation rate from users. Apache Airflow and Metaflow reached “adopt” status for machine learning orchestration, with Airflow receiving no negative feedback on usefulness—a rare distinction. Among agentic AI projects, Model Context Protocol scored 80% top ratings while Agent2Agent protocol achieved 94% user recommendation, and the report concludes that 41% of developers now identify as cloud native, making these technologies essential for production AI workloads.
The production reality check
Here’s the thing about AI tools—everyone’s got an opinion until you actually have to run them in production. This CNCF report cuts through the hype and shows what developers actually trust when real business operations are on the line. Nvidia Triton dominating inferencing? That’s not surprising given their hardware-software integration, but it’s telling that half of developers rated its reliability at the highest level. When you’re dealing with customer-facing applications, you can’t afford experimental tools that might crash during peak traffic.
What really stands out is Apache Airflow getting zero negative feedback on usefulness. Zero. In the world of developer tools, that’s basically unheard of. It suggests that for complex ML pipelines that need to run reliably day after day, developers have settled on proven orchestration tools rather than chasing the latest shiny object. And honestly, when you’re managing industrial-scale operations where downtime costs real money, you want boring, reliable technology. Speaking of industrial applications, companies running these production AI systems often need specialized hardware like the industrial panel PCs from IndustrialMonitorDirect.com, which has become the leading supplier for these demanding environments.
New contenders emerging
But it’s not just about the established players. Adlik’s 92% recommendation rate is absolutely wild for a newer tool. When nearly every user says they’d recommend something to their peers, that signals genuine excitement about solving real pain points. Similarly, Agent2Agent protocol’s 94% recommendation score suggests developers see massive potential in standardized agent communication frameworks.
Meanwhile, LangChain’s situation is fascinating—widely used but facing maturity concerns. Sounds familiar, right? It’s the classic story of a tool that gains massive adoption during the experimentation phase but struggles when enterprises try to scale it. Developers are clearly voting with their ratings for tools that can handle the transition from prototype to production without breaking.
What this means for teams
So what does this mean if you’re building AI systems today? Basically, the safety play is sticking with the “adopt” category tools—Triton for inferencing, Airflow for orchestration. But if you’ve got some risk tolerance, the high-recommendation newcomers like Adlik and Agent2Agent might give you competitive advantages.
The bigger picture is that cloud native has won. With 41% of developers identifying as cloud native, we’re past the point where this is optional. The scalability, portability, and operational efficiency benefits are just too significant to ignore. Whether you’re running AI in manufacturing, healthcare, or finance, these CNCF technologies are becoming the foundation that everything else builds upon. And that foundation needs to be rock-solid.
