The client's inference workloads had outgrown their Kubeflow-based platform. What started as a flexible ML workflow environment had become difficult to operate at production scale: model rollouts were slow, serving paths were inconsistent, and the platform was not optimized for modern LLM and accelerator-heavy inference workloads.
We led an end-to-end modernization from the legacy Kubeflow environment to an enterprise-grade inference platform built on Ray, cloud-managed Kubernetes, vLLM, and NVIDIA Triton. The new architecture provided a unified serving fabric across GPU and TPU hardware while allowing the team to migrate workloads incrementally with no production downtime.
The existing platform relied heavily on Kubeflow for orchestration and ML workflow management. While Kubeflow provided a strong foundation for Kubernetes-native ML pipelines, the client's production inference needs had evolved beyond what the legacy implementation could comfortably support.
Several problems were becoming increasingly expensive to manage:
The goal was not simply to replace Kubeflow. The goal was to separate concerns: use the right tool for distributed serving, the right runtime for each model type, and the right infrastructure abstraction for accelerator-aware scheduling.
We designed a unified, multi-runtime inference platform that decoupled orchestration from model serving. Instead of forcing every workload through one serving path, the new platform allowed each model to run on the runtime and accelerator best suited to its behavior.
Ray served as the distributed compute and serving control plane. It provided a flexible foundation for managing distributed workloads, scaling replicas, routing traffic, and coordinating inference services across the cluster.
vLLM was used for high-throughput LLM serving, where features like continuous batching, efficient KV-cache management, and distributed inference support are critical for serving modern language models efficiently.
NVIDIA Triton Inference Server was used for non-LLM and multi-framework workloads, including models that required standardized model repositories, dynamic batching, ensemble pipelines, or support across multiple ML frameworks.
The platform ran on cloud-managed Kubernetes with dedicated GPU and TPU node pools. Accelerator-aware scheduling ensured that workloads landed on the right hardware based on model requirements, cost profile, and runtime compatibility.
Core architecture components included:
This gave the client a serving platform that was both flexible and controlled: teams could choose the right runtime for each model, but releases still followed a common operational standard.
We avoided a risky "big bang" migration. Instead, we ran the new platform alongside the existing Kubeflow environment and moved workloads incrementally behind a routing layer.
The first phase was discovery. We inventoried every production model and grouped workloads by traffic volume, business criticality, hardware requirements, latency sensitivity, framework, dependency complexity, and migration risk.
Next, we established baseline behavior for each model. This included latency, throughput, error rate, cost profile, model output parity, and operational dependencies. The baseline gave us a clear definition of success before moving traffic.
For each workload, we built the new serving path on the modern platform and validated it before production cutover. Where possible, we shadow-tested production traffic through the new service and compared outputs, latency, and resource usage against the legacy path.
Cutovers were performed model by model. Traffic was gradually shifted from the Kubeflow path to the new Ray-based serving platform using canary releases and controlled routing. Each migration had an immediate rollback path, allowing the team to revert quickly if latency, quality, or error rates moved outside acceptable bounds.
The migration process included:
This approach allowed the business to keep running while the platform was modernized underneath it.
The modernization produced a more reliable, scalable, and maintainable inference platform.
Model deployment became more repeatable because packaging, runtime configuration, promotion, and rollback workflows were standardized. Teams no longer had to manage every model release as a custom infrastructure exercise.
The platform also became more flexible. LLM workloads could run on vLLM, traditional and multi-framework models could run on Triton, and accelerator-specific workloads could be placed on GPU or TPU capacity depending on performance and cost requirements.
Operationally, the team gained clearer ownership boundaries. Kubeflow no longer had to carry responsibilities better handled by a production serving layer. Ray, vLLM, Triton, Kubernetes, and the model registry each served a specific purpose in the architecture.
The client gained: