Over the past year, AI inferencing has become significantly more resource-intensive due to the exponential growth in the size and capabilities of large language models (LLMs). These models are not only larger but also more capable, powering a wide range of applications from advanced reasoning and instruction-following to highly specialized, domain-specific tasks.

As these workloads grow in both scale and strategic importance, Kubernetes has emerged as the preferred platform for deploying inference services, offering the scalability and ecosystem maturity needed to operationalize LLMs effectively.

Kubernetes is well-suited for inference workloads, providing a flexible platform to containerize models, scale based on demand and integrate telemetry and observability tools. However, as organizations expand globally or require tighter control over cost and compliance, single-cluster deployments may not be sufficient.

To meet these expanding needs, AI service providers are turning to multicluster inferencing, where LLM workloads are distributed across multiple Kubernetes clusters. While multicluster inferencing offers benefits like regional redundancy, data locality and better resource utilization, it also introduces a new layer of complexity.

Challenges With Multicluster AI Inferencing

• Consistency of LLM deployments across clusters: One core challenge is ensuring that model deployments remain consistent across clusters. Without a centralized management framework, teams must manually replicate inference pipelines, manage configuration drift and ensure that updates are propagated without downtime — all of which are error-prone and difficult to scale.
• Efficient use of scarce compute: AI workloads often rely on GPU or other accelerated resources, which are expensive and not always available in every location or cluster. Multicluster deployments need intelligent mechanisms to place workloads where suitable GPU compute and other accelerated resources are available, without sacrificing latency or performance.
• **Performance and availability of inference endpoints: **Providing business-critical AI services means low latency and high availability are non-negotiable. Inferencing endpoints must respond quickly, scale with demand and gracefully fail over if a cluster or location becomes unavailable, all while maintaining compliance and service-level agreements (SLAs) across geographies.

To address these challenges, two CNCF projects — Kubernetes AI Toolchain Operator (KAITO) and KubeFleet — are emerging as key players in the modern multicluster AI world.

KAITO: Optimize and Deploy AI Workloads and Resources

KAITO provides a declarative mechanism for managing LLM workflows. It supports:

• Managing both prebuilt and bring-your-own (BYO) models with KAITO workspaces.
• Automated resource provisioning for a range of LLM sizes.
• Multinode storage and compute optimizations.
• Out-of-the-box telemetry for inference health and performance insights.

By abstracting inference into custom Kubernetes resources, KAITO ensures that models are deployed consistently across clusters with minimal manual intervention.

KubeFleet: Intelligent Workload Placement Across Clusters

KubeFleet is a multicluster workload orchestrator designed to facilitate workload placement on Kubernetes. It can evaluate cluster properties, including resource availability, to place deployments on the best-suited cluster. Whether you’re trying to optimize GPU usage, ensure geo-redundancy or seamlessly promote updates to your inference engine across test, staging and production clusters, KubeFleet gives you the control you need.

Combine KAITO and KubeFleet for Seamless Multicluster AI

While KAITO ensures cluster-level inference services are well-defined and consistent, KubeFleet drives the global placement strategy:

• KubeFleet detects where GPU compute is available while ensuring selection of those clusters is optimal based on key properties such as cost, location and resource availability.
• KAITO deploys models into clusters matched by KubeFleet’s placement strategy, ensuring models are placed where they can run efficiently.
• KAITO manages the cluster, handling model preparation, resource allocation and observability.

This division of labor enables a well-differentiated architecture: KubeFleet focuses on where AI workloads should go, and KAITO handles how they run once they arrive.

Together, KubeFleet and KAITO form a powerful tool set for building scalable and efficient AI inference pipelines across any number of clusters.

Conclusion

Multicluster AI inferencing offers clear advantages in resilience, performance and compliance, but only once the operational complexity is tamed. KAITO and KubeFleet help address this complexity by:

• Ensuring consistent model deployment and life cycle management.
• Optimizing workload placement across clusters.
• Providing the tools needed to scale AI inference efficiently.

If you’re running AI services on Kubernetes and are looking to scale out, it’s time to explore KAITO and KubeFleet. Together, they provide a clean, declarative and intelligent approach to global AI inference at scale.

Join the KubeFleet and KAITO Communities

KubeFleet and KAITO are at the forefront of solving real-world challenges in multicluster AI inferencing. As these tools mature, the future of AI on Kubernetes depends on the insights, feedback and contributions of the broader cloud native community.

Whether you’re a platform engineer, machine learning (ML) practitioner or open source contributor, we invite you to get involved. Help us shape the roadmaps, contribute to features, share use cases and collaborate on building a more intelligent and scalable AI infrastructure across clusters.

Get started today:

• KAITO project on GitHub
• KubeFleet project on GitHub

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