There’s a strange disconnect happening in technology conversations right now. Everyone’s talking about AI: the models, the prompts, the agents, the possibilities. Meanwhile, the infrastructure that actually runs these AI systems gets treated as a solved problem, barely worth mentioning.
This is a mistake.
Containerization and orchestration platforms like Kubernetes and AWS Elastic Container Service aren’t legacy technologies being displaced by AI. They’re the foundation that makes modern AI deployments possible. And if you’re a technology leader thinking about how to build AI-enabled products and services, your team’s container expertise matters more now than it did five years ago.
AI Doesn’t Run on Magic
Here’s a reality check for anyone who’s been dazzled by demos: AI workloads are computationally demanding in ways that traditional web applications never were. Large language models require GPUs. Inference services need to scale dynamically based on unpredictable demand. Training pipelines consume massive resources for hours or days at a time.
All of this runs on infrastructure. Specifically, it runs on containerized infrastructure orchestrated by platforms designed to handle exactly this kind of complexity.
When your AI-powered feature needs to spin up additional GPU instances because usage just spiked, that’s Kubernetes or ECS managing that scaling. When you need to deploy a new model version without downtime, that’s a container orchestration platform executing a rolling update. When you need to run inference at the edge while training in the cloud, that’s containers providing the portability to make it work.
The organizations successfully deploying AI at scale aren’t bypassing container infrastructure. They’re leaning into it harder than ever.
The Distributed Computing Renaissance
AI is driving a renaissance in distributed computing. Consider what a typical AI-enabled application might require:
Handles variable latency requirements and scales horizontally to meet demand.
For retrieval-augmented generation that need to maintain consistency across replicas.
Decouple inference requests from responses, smoothing load and improving reliability.
Reduce redundant inference calls, cutting latency and cost for repeated queries.
Track model performance, drift, and resource utilization across the full stack.
Spin up GPU clusters on demand, running for hours or days with efficient resource cleanup.
This is a distributed system of significant complexity. And we’ve spent the last decade developing tools specifically designed to manage this kind of complexity: container orchestration platforms.
Kubernetes was built for exactly this scenario: deploying and managing multiple interdependent services with different resource requirements, scaling characteristics, and failure modes. The skills your team developed deploying microservices translate directly to deploying AI workloads. The patterns you learned for handling stateful services apply to vector databases. The experience you have with GPU scheduling in Kubernetes translates to running inference workloads efficiently.
Why Container Skills Are More Valuable Now
Let us be specific about why container and orchestration expertise has become more valuable, not less:
GPU resources are expensive and constrained. Kubernetes has developed sophisticated GPU scheduling capabilities (device plugins, multi-instance GPU support, time-slicing) that require deep expertise to configure correctly. Teams without strong container skills end up wasting GPU capacity or hitting availability walls they can't resolve.
AI workloads often span multiple environments by necessity. Containers provide the abstraction layer that makes portability possible across AWS ECS, Google Kubernetes Engine, and on-premises clusters. Organizations without this foundation end up with environment-specific solutions that are expensive to maintain and difficult to optimize.
AI introduces new compliance requirements around model provenance, training data lineage, and inference auditing. Container images provide immutable artifacts that can be signed, verified, and traced. The same IaC and GitOps practices that work for traditional applications apply directly to AI deployments.
AI compute costs can quickly become the largest line item in your cloud bill. Managing them requires right-sizing inference containers, implementing spot instance strategies for training, and auto-scaling based on queue depth. These are container orchestration problems that require deep knowledge of pod resource requests, node affinity, and priority classes.
The Skills Gap Is Real
Here’s what concerns us as we work with organizations building AI capabilities: many teams have underinvested in container skills precisely during the period when those skills became most critical.
The past few years saw a lot of abstraction layers built on top of Kubernetes. Managed platforms promised to hide the complexity. And for simple workloads, they delivered. You could deploy without understanding much about what was happening underneath.
AI workloads aren’t simple. They have unusual resource requirements, complex networking needs, and failure modes that managed platforms weren’t designed to handle. When things go wrong (and they will) teams need engineers who understand containers, orchestration, and the infrastructure underneath.
The organizations struggling most with AI deployment are often those that outsourced their container expertise to managed services and now find themselves unable to troubleshoot performance problems, optimize resource utilization, or implement the custom scheduling their workloads require.
What Technology Leaders Should Do
If you’re leading a technology organization, here’s our advice:
Do you have engineers who deeply understand Kubernetes or ECS? Who can write custom operators, debug networking issues, and optimize resource scheduling? If these skills are concentrated in one or two people, you have a risk you should address.
Container orchestration skills are foundational for AI deployment success. This isn't about checking a certification box. It's about building genuine expertise that will pay dividends as your AI ambitions grow.
There's a temptation to adopt platforms that promise to make Kubernetes disappear. Be cautious. These abstractions work until they don't, and when they break, you need people who understand what's underneath. Choose tools that enhance container capabilities rather than hiding them.
Whether you're on Kubernetes, ECS, or another orchestration platform, invest in understanding it deeply. The platform you choose matters less than how well you understand and operate it.
AI deployments aren't just about the model. They're about the entire system of services, data stores, and pipelines that support it. Approach AI infrastructure with the same rigor you'd apply to any distributed system architecture.
How VergeOps Can Help
At VergeOps, we’ve been helping organizations build and operate containerized infrastructure for years.
Container and orchestration training. We offer workshops and training programs, and hands-on coaching designed to build deep expertise in Kubernetes, ECS, and related technologies. Our training is hands-on and practical, focused on the real-world scenarios teams encounter when deploying complex workloads.
Platform optimization. If you’re running AI workloads and struggling with cost, performance, or reliability, we can help identify opportunities to improve. Often, small changes in how workloads are scheduled and scaled can dramatically impact both cost and capability.
Skills assessments and roadmaps. Not sure where your team stands or what skills to prioritize? We can evaluate your current capabilities and help you build a development plan that aligns with your AI ambitions.
The organizations that will succeed with AI are those that recognize it as an infrastructure challenge as much as a machine learning challenge. Containerization and orchestration aren’t relics of the pre-AI era. They’re essential capabilities for the AI era we’re entering.
If your container skills have atrophied while everyone was focused on prompts and models, now is the time to rebuild them. The AI future runs on containers, and the teams that understand that will have a significant advantage.