Best of Cloud Native — January 2026

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   Article

   CNCF·16w

   Introducing Kthena: LLM inference for the cloud native era

   Kthena is a new open-source sub-project of Volcano designed for LLM inference orchestration on Kubernetes. It addresses production challenges like low GPU/NPU utilization, latency-throughput tradeoffs, and multi-model management through intelligent routing, KV Cache-aware scheduling, and Prefill-Decode disaggregation. The system includes a high-performance router and controller manager that support topology-aware scheduling, gang scheduling, autoscaling, and multiple inference engines (vLLM, SGLang, Triton). Benchmarks show 2.73x throughput improvement and 73.5% TTFT reduction compared to random routing. Backed by Huawei Cloud, China Telecom, DaoCloud, and other industry partners.

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2. 2
   Article

   CNCF·19w

   Viettel joins CNCF as a Gold Member

   Viettel, Vietnam's largest telecommunications and technology group, has joined the Cloud Native Computing Foundation as a Gold Member. The company operates one of Southeast Asia's largest open source cloud infrastructures with extensive production deployments of OpenStack and Kubernetes supporting enterprise, government, and critical national infrastructure workloads. Viettel will contribute operational expertise from telecom environments and real-world experience operating cloud native platforms at national scale, with focus on security, data sovereignty, and large-scale production reliability.

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3. 3
   Article

   CNCF·18w

   CNCF Announces Dragonfly’s Graduation

   Dragonfly, a cloud native image and file distribution system, has graduated from CNCF after demonstrating production readiness and widespread adoption. The project uses peer-to-peer technology to distribute container images, OCI artifacts, and AI models at scale, saving up to 90% storage bandwidth and reducing launch times from minutes to seconds. Major organizations including Ant Group, Alibaba, Datadog, DiDi, and Kuaishou use Dragonfly to power large-scale container and AI workloads. Since joining CNCF, the project has seen over 3,000% growth in code contributions, expanding from 45 contributors across 5 companies to 271 contributors across 130+ companies. Future development will focus on accelerating AI model distribution using RDMA, optimizing image layouts for AI workloads, and implementing load-aware scheduling.

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