Scalable Inference Architectures: Designing Cloud-Native Patterns for Real-Time and Batch AI Services
Author(s): Santosh Pashikanti
Publication #: 2512023
Date of Publication: 11.09.2025
Country: United States
Pages: 1-9
Published In: Volume 11 Issue 5 September-2025
DOI: https://doi.org/10.62970/IJIRCT.v11.i5.2512023
Abstract
As organizations move from experimental machine learning (ML) projects to AI-driven products, the bottleneck has shifted from model training to scalable, reliable, and cost-efficient inference. In production, the same model is often consumed through multiple access patterns: low-latency online APIs, streaming pipelines, and high-throughput batch jobs. Each pattern has distinct latency, throughput, and cost requirements, yet most enterprises still deploy inference on ad-hoc stacks one-off REST services, tightly coupled ETL jobs, or cloud-specific endpoints leading to duplicated effort, inconsistent governance, and poor GPU utilization.
In this paper, I present a set of cloud-native reference architectures and patterns for serving AI models at scale across real-time, streaming, and batch workloads. The designs assume Kubernetes as the common substrate, combine specialized model servers (e.g., TensorFlow Serving, NVIDIA Triton, KServe, Seldon Core, Ray Serve) with API gateways and service mesh for traffic management, and rely on elastic GPU and CPU pools for cost-efficient scaling. I describe system requirements and design principles, detail a layered architecture for the inference control plane and data plane, and show how to realize three core inference patterns: online APIs, streaming inference, and offline batch scoring. A representative enterprise case study demonstrates how these patterns can be combined to support millions of daily predictions with strict latency SLOs while reducing infrastructure cost and operational toil. I close with a discussion of trade-offs, limitations, and practical lessons learned for teams standardizing inference architectures in large organizations.
Keywords: AI inference, model serving, Kubernetes, GPU autoscaling, API gateway, service mesh, real-time inference, streaming inference, batch inference, Triton Inference Server, TensorFlow Serving, KServe, Seldon Core, Ray Serve, cloud-native architecture.
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