Smarter Scheduling for Smarter Workloads.

KubeSched envisions a future where Kubernetes scheduling is not just efficient but intelligent, adaptive, and optimized for the most demanding workloads. Our goal is to redefine how resources are allocated in cloud-native environments, ensuring that every workload—whether AI/ML, HPC, or enterprise applications—receives the precise compute, memory, and network resources it needs, exactly when it needs them.

By integrating advanced scheduling techniques like Fractional GPU Scheduling, Gang Scheduling, Network-Aware Scheduling, and Deadline-Aware Scheduling, KubeSched aspires to set the industry standard for fairness, performance, and cost efficiency. We aim to empower organizations to maximize their infrastructure investments, reduce latency, and improve operational agility—making Kubernetes clusters smarter, more responsive, and more scalable than ever before.

KubeSched is not just a scheduler; it’s the foundation for the next generation of intelligent workload orchestration.

While the default Kubernetes scheduler works well for general-purpose workloads, it often falls short when handling complex, high-performance, and resource-intensive applications. If you're running AI/ML training, high-performance computing (HPC), or latency-sensitive applications, you need more than just basic scheduling—you need intelligence, efficiency, and adaptability.

Other solutions, like custom scheduler extensions and third-party orchestrators, can provide partial improvements, but they often come with trade-offs:

Default Kubernetes Scheduler – Provides general workload placement but lacks advanced scheduling optimizations for GPUs, network-aware decisions, or deadline-sensitive jobs.

Custom Schedulers – Require significant development effort, maintenance overhead, and often lack native integration with Kubernetes' evolving ecosystem.

Third-Party Orchestrators – Can be powerful but may introduce vendor lock-in, complex configurations, and additional operational costs.