The "boring" truth about successful AI
From Demos to Dollars: Quiet Engineering, Big Commercial Pay-offs
Deploying generative AI systems is an engineering discipline rather than a science project. Foundation models and novel prototypes win headlines, but the commercial race will be decided in the production trenches—where reliability, cost, and governance matter more than benchmark scores. These infrastructure shifts are now separating fragile demos from revenue-generating services, and deserve the focus of chief technologists and investors alike.
Orchestrating Inference
Kubernetes has gained traction as a control layer for artificial intelligence workloads, with 54% of advanced users deploying machine learning and AI applications on it according to a 2024 Cloud Native Computing Foundation survey. Organizations implementing these systems typically add specialized inference engines such as vLLM through frameworks like KServe for latency-sensitive applications, or Ray Serve and Ray Data for Python-native scheduling. While Kubernetes provides advantages in scaling and fleet management, some teams rely on alternatives including standalone Ray clusters or serverless GPU platforms, making their infrastructure decisions based on specific performance requirements and operational capabilities rather than following a single industry standard.
The Emerging Compute Stack
As the industry matures, a de-facto standard is emerging for AI compute, built on a foundation of proven open-source technologies. Many engineering teams are converging on a layered recipe: Kubernetes as the container orchestrator to manage cluster resources, Ray as the distributed compute engine to scale Python and AI workloads, and PyTorch as the primary training framework, often augmented by specialized inference engines like vLLM. This combination provides a robust, scalable, and flexible platform for moving from prototype to production. Robert Nishihara’s recent deep dive, “An Open Source Stack for AI Compute,” provides a detailed blueprint of this architecture and the roles each layer plays.
Containerization Makes Deployment Boring
The aim is to make shipping an AI application as routine as launching a web service. Enter the mantra of “making AI boring” with containerization. By bundling models and their dependencies into portable, uniform containers, teams bring order to deployment chaos. This approach, borrowed from modern software engineering, treats the shipping of an AI model not as a bespoke research project but as a repeatable, predictable logistical exercise. The benefits are clear: faster iteration, fewer errors, and the ability to manage AI assets with the same rigor as any other critical software.
Making Every GPU Count
With the cost of high-end GPUs soaring, the economics of AI have shifted from raw computational power to efficient resource allocation. Data centers typically achieve less than 50% utilization rates for inference workloads on their AI accelerators—a costly inefficiency that has spurred the development of GPU virtualization technologies enabling multiple models to share single processors. Cross-platform standards such as WebGPU amplify these efficiency gains by reducing the need to maintain separate builds for each GPU architecture—whether Nvidia, AMD, or Intel. For enterprises deploying AI at the network edge, where hardware diversity is the norm, such portability transforms what was once a complex integration challenge into routine infrastructure management.
Scaling Distributed Training Across Clusters and Regions
For most organizations, training models with hundreds of billions of parameters now requires computational resources that exceed what any single data center can provide. Companies are responding by distributing these workloads across multiple facilities that can span regions. This approach depends on two advances: high-performance caching systems that maintain data-transfer speeds sufficient for GPU utilization, and orchestration software capable of coordinating disparate computing clusters regardless of location. By aggregating under-utilized processing capacity from wherever it exists—whether in regions with surplus electricity or facilities with idle machines—teams can reduce both queue times and training costs, transforming geographic dispersion from a constraint into an economic opportunity.
The Network Strikes Back
Network infrastructure has become the hidden constraint in AI training operations, where data must traverse thousands of interconnected processors without creating bottlenecks. Leading technology companies are exploring specialized alternatives like RoCEv2 and InfiniBand, which minimize latency when synchronizing computations across massive GPU arrays. The Linux Foundation's Essedum initiative represents a new approach, deploying machine learning algorithms to dynamically optimize network routing and traffic patterns during live training sessions. Given that large language model training can consume millions of dollars in compute time over several days, even marginal improvements in network efficiency—reducing idle time by single-digit percentages—yield substantial financial returns. This shift mirrors earlier transitions in high-performance computing, where networking evolved from an afterthought to a primary design consideration once computational resources reached sufficient scale.
Platform Engineering for the AI Stack
When data scientists operate without constraints, they generate custom scripts and services at a pace that outstrips compliance reviews. Platform engineering addresses this through internal developer portals that offer standardized & pre-approved tools—so-called "golden paths"—for building, deploying, and managing AI services. According to recent surveys, many enterprises have established platform engineering teams to manage this balance between developer autonomy and organizational governance. The approach permits rapid deployment while maintaining the audit trails and risk controls that boards and investors now regard as table stakes for AI initiatives.
Where AI Meets Infrastructure
Together, these shifts represent a maturation of the AI industry. The focus is moving from what is merely possible to what is practical and profitable at scale. The winners in the next phase of AI will be defined not just by the brilliance of their models, but by the quiet efficiency and resilience of the infrastructure that powers them.
If you want to deepen your AI-engineering skills—and swap notes with practitioners tackling the same challenges—AI_dev in Amsterdam this August is a timely forum. Sessions range from vector search and agentic systems to MLOps, backed by practical case studies and frank hallway conversations for teams taking AI from prototype to production. In my capacity as Program Chair, we aimed to create a forum where the focus shifts from “what's possible” to “what works in production”.
Ben Lorica edits the Gradient Flow newsletter. He helps organize the AI Conference, the AI Agent Conference, the Applied AI Summit, while also serving as the Strategic Content Chair for AI at the Linux Foundation. He is the host of the Data Exchange podcast. You can follow him on Linkedin, Mastodon, Reddit, Bluesky, YouTube, or TikTok. This newsletter is produced by Gradient Flow.