New report provides maturity and recommendation scores for tools and projects across AI inference, ML orchestration, and agentic AI platforms
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CNCF and SlashData’s new report highlights the top AI tools gaining traction in cloud native ecosystems, with NVIDIA Triton, Metaflow, Airflow, and Model Context Protocol leading in adoption and developer trust.
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For AI inference tools, NVIDIA Triton, DeepSpeed, TensorFlow Serving, and BentoML are the projects developers cumulatively placed in the adopt position. Further, NVIDIA Triton received the highest ratings for both maturity and usefulness.
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In the ML orchestration category, Airflow and Metaflow rose to the adopt position. Metaflow earned the highest maturity ratings, while Airflow had the highest usefulness ratings.
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In agentic AI platforms, Model Context Protocol (MCP) and Llama Stack reached the adopt classification. MCP leads on maturity and usefulness; meanwhile, Agent2Agent posted the highest recommendation score at 94%.
The Cloud Native Computing Foundation (CNCF®), which builds sustainable ecosystems for cloud native software, released new findings from the Q4 2025 CNCF Technology Landscape Radar report with SlashData, uncovering insight on developers’ experience and opinions on AI inference tools and engines, agentic AI platforms and projects, and ML orchestration tools.
“Organizations building and operating AI systems can’t treat tooling the way they did five years ago,” said Chris Aniszczyk, CTO, CNCF. “What this new research affirms is that cloud native principles from scalable infrastructure and orchestration are foundational not just for backend apps, but for inference pipelines and agentic AI systems. Choosing technologies rated ‘adopt’ helps reduce risk and increase productivity.”
Inference Tools Show Strong Utility and Momentum
NVIDIA Triton, DeepSpeed, TensorFlow Serving, and BentoML are the four tools most developers place in the adopt position.
- Triton leads across maturity (50% 5-star, 30% 4-star) and usefulness (41% 5-star, 38% 4-star), showing strong confidence from developers working on infrastructure-heavy AI workloads and reflected is its strength in powering production-grade inference workloads that businesses rely on.
Adlik was less used but most widely recommended with 92% of current or former users.
- This suggests tools that meet specialized enterprise needs, such as model optimization or hardware-specific deployments, can build loyal user bases even without dominating across categories.
Further, TensorFlow Serving and DeepSpeed show strong overall approval across broader use cases, supporting an enterprise need for flexible deployment paths. Meanwhile, lama had the lowest maturity ratings (23%), showing that not all tools are equally ready for enterprise-scale deployment.
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ML Orchestration Projects Mature Alongside Scale
Airflow and Metaflow were rated as adopt tools.
- Metaflow leading in maturity (84% gave it 4 or 5 stars) and Airflow topping usefulness and recommendation ratings.
- Notably, Airflow received no 1- or 2-star ratings in usefulness.
BentoML, a dual-category tool, earned an adopt rating for inference and trial for orchestration. This highlights that multi-role tools may succeed unevenly across domains.
Additionally, Argo Workflows and Kubeflow, both CNCF projects, were rated trial for orchestration.
Flyte and Seldon Core saw more moderate scores, with Flyte showing mostly 3-star ratings. This data points to room for growth among orchestration tools that serve general use cases but haven’t yet distinguished themselves.
“These findings show just how diverse the AI/ML toolchain has become,” said Liam Bollmann-Dodd, Senior Market Research Consultant at SlashData. “Metaflow and Airflow are excellent examples of how developer trust is earned through stability and fit-for-purpose design. But even newer projects like Flyte and Seldon Core are showing traction that signals opportunity for differentiation.”
Agentic AI Projects Emerge With Mixed Perceptions
Model Context Protocol (MCP) and Llama Stack were the only agentic AI tools to land in the adopt category.
- MCP had the highest combined 4- and 5-star usefulness scores (80%) and the broadest developer base among top tools. This indicates that structured, agent-based design is finding true traction for enterprise use cases like AI-powered customer support.
Agent2Agent (A2A), while newer and less mature, received the highest recommendation rate.
- 94% of users would recommend A2A. This shows that developer excitement and perceived trajectory can outpace current capabilities.
Cloud Native Patterns Essential for AI/ML
The data shows that even when developers don’t label themselves as cloud native, their AI/ML systems lean on cloud native architectures: containerization, orchestration, scalability, and reliability.
41% of AI/ML developers now identify as cloud native, which is expected to rise. CNCF continues to support this evolution by spotlighting reliable tools, supporting early projects, and offering community-driven guidance.
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