AI is becoming increasingly ingrained in every facet of business. But how can organizations unlock its full potential? How can they confidently rely on AI for key business operations? Behind the buzz, a range of strong and silent workhorse technologies, such as Kubernetes, operate in the background to secure AI’s foundations and ensure its future success, longevity, and reliability within your company.
How Kubernetes Powers AI
Kubernetes (also known as K8s) is an open-source platform originally developed by Google and turned over to the open-source community in 2014. It deploys, automates, and manages containerized applications, i.e., it acts as a “data center,” which can eliminate many time-consuming manual processes for developers. You can think of Kubernetes as the conductor of an orchestra, keeping individual components (or sections) working as and when they should be.
When it comes to AI, K8s plays a key role throughout the entire AI lifecycle: development, training, and inference. K8s is particularly important in the latter two phases.
As of 2023, almost half of organizations were using Kubernetes for AI workloads. Reasons for this include scalability, cost-efficiency, and reliability. Let’s explore each in more depth.
1. Scalability
Every business experiences periods of higher and lower demand. For example, a US e-commerce site could get huge traffic volumes of customer service chatbot requests during a Black Friday sale but relatively low volumes during US overnight hours. Kubernetes auto-scales in response to fluctuating traffic, user requests, and volume of data being processed. It can handle complex needs and allow companies to resource accordingly without delay. This benefit is most significant in the inference phase because that’s where the greatest variance in workload is, since demand is user-generated. Training is much more predictable and controllable, so autoscaling isn’t as relevant. Training is also a discrete activity, whereas inference is constant as long as your AI app is available, so the potential long-term benefits of autoscaling for inference are almost infinite!
In addition, the adaptable and automated nature of K8s greatly simplifies workflow processes for engineers. Organizations can easily scale their infrastructure up or down in both the training and inference phases without the need to rely on human intervention to manage the physical hardware. This can reduce human resource costs for businesses.
2. Cost-Efficiency
The flexibility of Kubernetes is also beneficial for companies in financial terms. Training AI models, for example, is expensive and time-consuming, with the largest models predicted to cost over a billion dollars to train by 2027.
Kubernetes offers workload automation and enables you to allocate the computing resources needed dynamically and automatically. This saves you money because you only pay for the resources your AI workload actually uses. So, if your inference processing needs are lower on weekends, Kubernetes will automatically scale resources down in the week and up on weekends as demand fluctuates. Without this autoscaling, you’d have to pay according to weekend demand all week long, throwing away cash and, therefore, profit.
3. Reliability
While the world of AI is not new, the hype around it is. Kubernetes has been around for a decade and is, therefore, a relatively mature technology that companies can trust to manage their AI endeavors. Its stability and dependency enable developers to streamline processes and work more efficiently. In addition, Kubernetes is auto-healing, which means that it actively detects and resolves issues, minimizing downtime. That’s why, for many businesses, Kubernetes is the backbone of their company’s AI computing infrastructure.
What Does the Future Hold for K8s and AI?
There is still room for growth in terms of future functions of Kubernetes. The continued rise of AI means that cloud-based infrastructure will grow and evolve to meet new use cases. In terms of futureproofing your AI workload, integrating Kubernetes now to support the ever-growing range of AI use cases is more important and timely than ever.
Get the Power of K8s for AI with Gcore Managed Kubernetes
At Gcore, we make it simple to take advantage of the benefits Kubernetes provides for AI workloads. Gcore Managed Kubernetes is easy to deploy, manage, and scale based on your needs. Whether you’re looking to train a model with Gcore GPU Cloud or your app is ready to deploy via Gcore Inference at the Edge, we can help.
Our latest ebook, Accelerating AI with Kubernetes, provides a more technical, in-depth look at how Kubernetes benefits AI development, training, and inference.
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Gcore recognized as a Leader in the 2025 GigaOm Radar for AI Infrastructure
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So, in this article, we explore how L3/L4 network security is evolving to meet new network security challenges and how AI strengthens defenses against today’s most advanced threats.Smarter threat detection across complex network layersModern threats blend into legitimate traffic, using encrypted command-and-control, slow drip API abuse, and DNS tunneling to evade detection. Attackers increasingly embed credential stuffing into regular login activity. Without deep flow analysis, these attempts bypass simple rate limits and avoid triggering alerts until major breaches occur.Effective network defense today means inspection at Layer 3 and Layer 4, looking at:Traffic flow metadata (NetFlow, sFlow)SSL/TLS handshake anomaliesDNS request irregularitiesUnexpected session persistence behaviorsGcore Edge Security applies real-time traffic inspection across multiple layers, correlating flows and behaviors across routers, load balancers, proxies, and cloud edges. 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Branches, mobile endpoints, IoT devices, and multi-cloud services all represent potential entry points for attackers.Real-world example: IoT malware infection at the branchMalware-infected IoT devices at a branch office can initiate outbound C2 traffic during low-traffic periods. Without local inspection, this activity can go undetected until aggregated telemetry reaches the central SOC, often too late.Modern edge security platforms deploy the following:Real-time traffic inspection at branch and edge routersBehavioral anomaly detection at local points of presenceAutomated enforcement policies blocking malicious flows immediatelyGcore’s edge nodes analyze flows and detect anomalies in near real time, enabling local containment before threats can propagate deeper into cloud or core systems. Decentralized defense shortens attacker dwell time, minimizes potential damage, and offloads pressure from centralized systems.How Gcore is preparing networks for the next generation of threatsThe threat landscape will only grow more complex. Attackers are investing in automation, AI, and adaptive tactics to stay one step ahead. Defending modern networks demands:Full-stack visibility from core to edgeAdaptive defense that adjusts faster than attackersAutomated recovery from disruption or compromiseDecentralized detection and containment at every entry pointGcore Edge Security delivers these capabilities, combining AI-enhanced traffic analysis, real-time mitigation, resilient failover systems, and edge-to-core defense. In a world where minutes of network downtime can cost millions, you can’t afford static defenses. We enable networks to protect critical infrastructure without sacrificing performance, agility, or resilience.Move faster than attackers. Build AI-powered resilience into your network with Gcore.Check out our docs to see how DDoS Protection protects your network
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