Secure and scalable AI with Kubernetes — where DevOps meets SecOps

Jasmina Dimitrievska
May 28
3 min read

Generative AI is no longer experimental — it’s operational.

When companies want to take control of their language models and build AI solutions in Kubernetes, they need both DevOps precision and SecOps discipline.

At SDNit, we help organizations run open-source AI models in a way that is scalable, secure, and cost-effective — without vendor lock-in.

Why not just run AI in the cloud?

It’s easy to think, “We’ll just use AWS Bedrock, SageMaker, or Google Vertex AI.” But for many DevOps consultants, Cloud consultants, and AI teams, those platforms simply aren’t enough.

If you:

want full control over your model,
run your own fine-tuned LLM,
need optimized GPU utilization,
or work with quantized models to reduce costs,

…then managed solutions quickly become too limiting. Serverless is great for prototypes — but not for production-grade AI.

Why more organizations are choosing Kubernetes

Kubernetes isn’t the answer to everything, but for production AI infrastructure, it’s hard to beat. It gives DevOps experts and AI/MLOps consultants full control over performance, cost, and scalability.

The advantages are clear:

Scalability: horizontal autoscaling, GPU awareness, and efficient resource utilization.
Portability: run on GCP (GKE), AWS EKS, or on-premises — the choice is yours.
Observability: integrate Prometheus, Grafana, and OpenTelemetry.
Resource efficiency: maximize GPU cluster utilization with spot instances and node affinity.
Control: no vendor lock-in — you own the stack.

This is where infrastructure consultants and DevOps consultants play a key role — building environments that remain stable even under heavy load.

Building blocks of the modern AI stack

1. Model serving — vLLM, TGI, or Triton?

vLLM is currently the gold standard for open inference, with support for batching, token streaming, and quantization.
TGI (Text Generation Inference) is optimized for Hugging Face models and easy to integrate.
NVIDIA Triton is enterprise-grade, but requires more setup and operational overhead.

Our recommendation: start with vLLM — stable, fast, and strongly supported by the community.

2. GPU nodes — the foundation of performance

Build a dedicated GPU node pool using taints, labels, and node affinity. Install the NVIDIA device plugin and GPU drivers directly on the nodes.

This is where our Cloud consultants and Infrastructure as Code experts can automate the entire environment using tools like Terraform and Ansible.

3. Model storage — speed versus flexibility

There are three main approaches:

A. Bake the model into the Docker image (fastest)

B. Download the model from S3 at startup (most flexible)

C. Use a shared PVC volume (balanced approach)

In most cases, we recommend baking the model into the image for faster startup times — but the right choice depends on how frequently you update model versions.

4. Autoscaling and performance

Use HPA + KEDA to scale based on GPU utilization, latency, or request volume. Combine this with Cluster Autoscaler for full elasticity.

SDNit’s DevOps consultants can help fine-tune the scaling strategy to keep operational costs low without compromising the user experience.

Observability and security

Running AI models is just as much about security and networking as it is about compute power.

Prometheus + Grafana provide full visibility into latency and GPU utilization.
OpenTelemetry enables tracing across the entire request chain.
Zero Trust security consultants secure model access, API keys, and network segmentation.
SecOps consultants handle logging, incident response, and compliance.

AI without security is a risk — not an investment.

6. Cost optimization — performance without waste

Use spot GPUs and scale down when demand decreases.
Quantize models (INT4/INT8) to reduce VRAM usage by up to 50%.
Measure token throughput and GPU latency to make smarter scaling decisions.
Continuously monitor the cluster — otherwise costs can escalate quickly.

This is where we combine DevSecOps consulting with hands-on operations — balancing security, cost, and performance.

When DevOps, NetOps & SecOps work together

AI projects become sustainable when DevOps, NetOps, and SecOps work in sync.

DevOps ensures the infrastructure is automated and reproducible. NetOps optimizes network performance and minimizes latency. SecOps makes sure everything is secure, compliant, and traceable.