GKE for AI/ML Workloads: When Do You Need Kubernetes?

Ganesh Sharma
9 hours ago
10 min read

Somewhere in the planning of nearly every AI project, a technical leader has to answer a deceptively simple infrastructure question: does this need Kubernetes, or is a fully managed platform enough? Get the answer wrong in one direction and a small team drowns in cluster administration they never needed. Get it wrong in the other direction and a growing AI workload hits a wall that a managed platform was never built to handle. Google Kubernetes Engine, GKE, sits at the center of that decision for teams building on Google Cloud.

This blog explains what GKE actually offers for AI and ML workloads, when a business genuinely needs it instead of a simpler managed option, how implementation generally works, and how it compares to the other infrastructure choices available.

GKE, Defined

A Managed Layer, Not a Managed ML Platform

GKE is Google's managed implementation of the open source Kubernetes container orchestration system, providing a scalable, flexible platform for running containerized workloads, including AI and ML applications, but it is an infrastructure layer rather than a machine learning platform in its own right.

How Does GKE Differ From Vertex AI?

Vertex AI is Google Cloud's unified platform for building, training, and deploying models, designed so a small team, even a single ML engineer, can work without managing infrastructure directly. GKE instead gives a team full control over every layer of the stack, from GPU scheduling to container networking to which open source ML tools and serving frameworks get used.

Why Kubernetes Became the Default for AI Infrastructure at Scale

Kubernetes has become the de facto standard for teams that need deep infrastructure control for AI training and inference, with companies including IBM, Meta, NVIDIA, and Spotify running their AI and ML workloads on it, and by 2026 the majority of organizations building generative AI applications and autonomous AI systems rely on Kubernetes to power them.

The Real Decision Point for Kubernetes

The honest answer is that most teams do not need GKE on day one, and the decision usually comes down to team composition and workload characteristics rather than raw ambition.

Signals That Point Toward Vertex AI Instead

A team of fewer than five ML practitioners without dedicated MLOps engineers is generally better served by Vertex AI, since it removes the operational burden of cluster management, GPU scheduling, and infrastructure tuning that Kubernetes assumes someone on the team already knows how to handle.

Does Your Team Already Manage Kubernetes for Other Workloads?

A team that already runs a platform engineering function managing Kubernetes clusters for other workloads will find adding AI and ML workloads a natural extension rather than a new discipline, particularly when the team needs to choose exact frameworks and libraries, customize scheduling policies, or run cost-optimized spot instances for training.

A Third Option Many Teams Overlook

For applications that simply call a managed model provider such as OpenAI, Anthropic, or Vertex AI's own hosted models, Cloud Run is often the simpler default, handling authentication, rate limiting, and request scaling, including scaling to zero during idle periods, without the operational overhead Kubernetes assumes.

Capabilities Purpose-Built for AI on GKE

Beyond general container orchestration, GKE has been extended with capabilities aimed specifically at the demands of training and serving large models.

How Does GKE Handle GPU and TPU Scheduling?

GKE simplifies operating both GPUs and Cloud TPUs at scale, orchestrating large workload training and inference across accelerators while supporting popular serving frameworks such as Hugging Face TGI, vLLM, and JetStream.

The GKE AI Conformance Program

Because setting up a Kubernetes cluster correctly for AI and ML workloads can be genuinely complex, Google introduced a Kubernetes AI conformance program that defines a standard for clusters to ensure they can reliably and efficiently run these workloads, reducing the risk of a misconfigured cluster undermining a training or inference job.

Support for the Newest Workload Types

GKE surfaces native support for modern AI workload types directly in its tooling, including JobSets, RayJobs, and PyTorchJobs for training, alongside deployment resources purpose built for inference serving at scale.

Is GKE the Right Infrastructure Choice for Your Business?

GKE tends to be the right choice for organizations that need deep infrastructure control, workload portability, or a highly customized, high-performance AI platform that a fully managed service cannot provide.

GKE's cluster management has a free tier to get started, with ongoing costs driven primarily by the compute, GPU, and TPU resources consumed, and cost efficiency improving considerably for predictable, high-volume workloads through committed use discounts and spot instances. See the Pricing section below for more detail.

Whether GKE is the right choice ultimately comes down to whether the operational control it offers is worth the platform engineering expertise it requires. For teams without that expertise already in house, the faster path is usually Vertex AI first, with a move to GKE only once genuine scale or customization needs justify the added complexity.

Getting Started With GKE for AI Workloads

Provisioning a Conformant Cluster

A team provisions a GKE cluster configured to meet the Kubernetes AI conformance standard, ensuring the cluster is set up correctly to reliably handle GPU or TPU scheduling and the other demands specific to AI workloads.

Choosing Training and Serving Frameworks

Unlike a managed platform, GKE requires the team to select its own training frameworks and inference serving stack, such as vLLM or JetStream, based on the specific models and performance requirements of the workload.

Setting Up Cost-Optimized Scheduling

Teams running large training jobs commonly configure spot instances and committed use discounts to bring down compute costs, along with custom scheduling policies suited to how their specific training workloads behave.

How Does GKE Work Alongside Vertex AI Rather Than Replacing It?

Many organizations do not choose exclusively between the two. A workload can be trained or served on GKE while still calling into Vertex AI's model monitoring, feature storage, or experiment tracking capabilities through the Vertex AI SDK, combining GKE's infrastructure control with Vertex AI's higher level MLOps tooling where it adds value.

Actual implementation details vary depending on the specific accelerators used, the scale of the workload, and how much of the surrounding MLOps tooling a team builds versus borrows from Vertex AI.

Advantages and Limitations of GKE for AI Workloads

Where GKE Delivers the Most Value

Advantage	Details
Full infrastructure control	Teams choose exact frameworks, libraries, and serving infrastructure rather than working within a managed platform's constraints.
Strong GPU and TPU support	Native orchestration of accelerators simplifies running large scale training and inference.
Workload portability	Kubernetes-based workloads are generally easier to move across environments than platform-specific alternatives.
Cost efficiency at scale	Spot instances and committed use discounts can make GKE more cost-effective for predictable, high-volume workloads.
Proven at scale	GKE already powers AI workloads for major enterprise customers and leading frontier model builders.

What Are the Trade-Offs of Using GKE?

Limitation	Details
Requires real platform expertise	GPU scheduling, container networking, and cluster management assume a team already comfortable with Kubernetes.
More operational overhead	Managing manifests, node pools, and scaling policies requires dedicated engineering time a managed platform would otherwise absorb.
Loses built-in MLOps tooling	Using GKE without Vertex AI's higher level services means forgoing built-in model monitoring, feature storage, and experiment tracking unless integrated separately.
Overkill for small teams	Teams under roughly five ML practitioners without dedicated MLOps engineers generally take on more complexity than the workload justifies.

How Much Does GKE Cost for AI Workloads?

GKE offers a free tier for cluster management, so getting started with Kubernetes does not require upfront cluster costs. Ongoing costs are driven primarily by the compute, GPU, and TPU resources consumed by training and serving workloads, with cost efficiency improving significantly at scale through spot instances and committed use discounts for predictable, high-volume usage.

Visit this page for more pricing info: https://cloud.google.com/kubernetes-engine/pricing.

GKE Compared to Other Infrastructure Choices

GKE is one of several ways to run AI and ML workloads on Google Cloud, and the right choice depends heavily on team expertise, workload characteristics, and how much control is genuinely needed.

GKE and Vertex AI

Vertex AI removes infrastructure management entirely, letting a small team train, deploy, and monitor models without a dedicated MLOps function. GKE trades that simplicity for full control over every layer of the stack, which only pays off once a team has the platform engineering expertise to use that control well.

GKE and Cloud Run

Cloud Run handles the majority of production AI workloads that simply call a managed model provider's API, offering authentication, rate limiting, and scale-to-zero behavior without any Kubernetes complexity. GKE becomes necessary specifically when a workload needs direct GPU or TPU access and fine-grained control that a simple API proxy does not require.

GKE and Self-Managed Infrastructure Outside Kubernetes

Some organizations run AI workloads on raw virtual machines without any orchestration layer at all. This offers maximum low-level control but requires building the scheduling, scaling, and reliability features that GKE already provides out of the box, which is why most teams that need this level of control choose Kubernetes over building it themselves.

GKE and Other Cloud Providers' Kubernetes Services

Amazon EKS and Azure Kubernetes Service offer comparable managed Kubernetes experiences within their respective ecosystems. The choice between GKE and these alternatives typically comes down to existing cloud provider relationships rather than a fundamental difference in Kubernetes capability itself.

Which Teams Get the Most Value From GKE?

GKE tends to be the right choice for organizations that:

Already have a platform engineering team managing Kubernetes for other workloads
Need to train or serve models using specific frameworks a managed platform does not support
Run large scale, GPU or TPU heavy training jobs where cost optimization through spot instances matters
Require workload portability across environments rather than platform lock-in
Are building infrastructure to support multiple AI teams or projects over time, not just a single model

Does Choosing GKE Actually Improve AI Infrastructure Outcomes?

GKE itself does not make a model better, but choosing infrastructure that matches a team's actual expertise and workload demands directly affects whether an AI system stays reliable and cost-effective as it scales.

Teams that adopt GKE without the platform expertise to operate it well often end up with unreliable clusters and higher costs than a managed alternative would have produced, while teams that stay on a fully managed platform past the point where they genuinely need more control tend to hit cost or customization ceilings that force a disruptive migration later. The right outcome depends on matching the infrastructure choice to where a team actually is today, not where it might be in the future.

How Does CodersArts Help With GKE for AI Workloads?

We help businesses decide whether GKE, Vertex AI, Cloud Run, or some combination is the right infrastructure choice for their specific AI workloads, then implement whichever path fits. This includes provisioning conformant GKE clusters for teams that need deep infrastructure control, and helping teams that started on GKE prematurely transition to a more manageable platform when that better fits their actual needs.

Our experience includes projects such as setting up GPU and TPU optimized GKE clusters for large scale model training, building hybrid architectures that combine GKE for serving with Vertex AI's monitoring and experiment tracking, and helping clients avoid taking on Kubernetes complexity before their team and workload genuinely justify it. This experience helps clients choose infrastructure that fits their actual stage rather than defaulting to the most powerful option available.

Frequently Asked Questions

Is GKE Necessary for Every AI Project?

No. Most small to mid-sized teams, particularly those without dedicated platform engineering expertise, are better served starting with Vertex AI or Cloud Run, and only moving to GKE once genuine scale or customization needs justify the added operational complexity.

How Is GKE Different From Vertex AI?

GKE is a managed Kubernetes orchestration layer that gives full control over infrastructure, while Vertex AI is a managed machine learning platform that handles infrastructure automatically. Many organizations use both together rather than choosing exclusively.

Why Do Larger Organizations Choose GKE Over Vertex AI?

Larger organizations with existing platform engineering teams choose GKE because it offers full control over frameworks, scheduling, and serving infrastructure, along with cost efficiency at scale through spot instances and committed use discounts that a fully managed platform does not expose.

What Is Required to Get Started With GKE for AI Workloads?

A typical starting point involves provisioning a GKE cluster configured to meet the Kubernetes AI conformance standard, selecting training and serving frameworks suited to the workload, and setting up GPU or TPU scheduling appropriate to the models involved.

Can GKE and Vertex AI Be Used Together?

Yes. A common pattern is training or serving models on GKE while still using Vertex AI's SDK to access model monitoring, feature storage, or experiment tracking, combining infrastructure control with higher level MLOps tooling where it adds value.

Do I Need Kubernetes Expertise on My Team to Use GKE?

Yes, meaningfully. GKE assumes familiarity with GPU scheduling, container networking, and the broader Kubernetes ecosystem, which is why teams without dedicated platform engineering expertise generally see faster results with a managed platform instead.

What Should a Business Evaluate Before Choosing GKE for AI Workloads?

A business should evaluate whether it already has platform engineering expertise in house, how much customization and control the workload genuinely requires, expected scale and whether cost optimization through spot instances matters, and whether a managed alternative like Vertex AI or Cloud Run could deliver the same outcome with less operational burden.

What Services Does CodersArts Offer?

Beyond GKE and other AI and RAG specific delivery and partnership work, CodersArts offers a wider range of services that agencies, businesses, and individual developers regularly rely on, whether as part of a partnership or on their own.

AI and RAG Development

Custom AI and RAG development, starting from proof of concept through to full production builds, along with broader LLM and generative AI development for businesses building AI-powered products and internal tools.

Consultation

Project consultation for businesses and agencies evaluating an AI or infrastructure initiative, helping assess feasibility, recommend the right technical approach, and scope a project before committing to full development.

One-on-One Mentorship

Personalized, expert-led mentorship for developers and teams looking to build hands-on AI, machine learning, or infrastructure engineering skills, with guidance tailored to individual or team goals and current experience level.

Dedicated Team and Team Augmentation

Dedicated AI and infrastructure engineering teams, or engineers who work as an extension of an existing in-house or agency team, scaling up or down based on project needs.

Ongoing Support and Maintenance

Post-launch monitoring, optimization, and maintenance for AI systems and infrastructure already in production, helping ensure performance and reliability do not degrade over time.

Job Support Services

Remote job support for developers and engineers working on live AI, infrastructure, or LLM projects, including pair programming, code reviews, workflow setup, debugging, and help meeting sprint deadlines under expert guidance.

Corporate and Team Training

Structured training and workshops for teams looking to build internal AI and infrastructure capability, covering hands-on implementation as well as best practices for evaluation and production readiness.

White-Label and Partnership Delivery

CodersArts also partners with agencies, consultancies, and technology companies to deliver AI and infrastructure development on their behalf, whether white-label, co-branded, or embedded alongside an existing team.

Whether you are a business deciding between GKE and a managed platform, an agency looking for a delivery partner, or a developer seeking hands-on mentorship, CodersArts offers services to support your AI journey.

Reach out at contact@codersarts.com or visit www.codersarts.com to discuss your GKE, Vertex AI, or broader AI infrastructure project.

Continue Exploring AI Infrastructure and Enterprise Resources

If you found this blog helpful, explore more AI, RAG, and enterprise AI resources from CodersArts AI to see how organizations are applying these systems to real world applications.