RAG vs Fine-Tuning: Choosing the Right LLM Approach for Your Product
Both RAG and fine-tuning improve LLM performance on your specific use case — but they solve different problems. Here's how to choose.
MLOps / DevOps
Kubernetes
Container orchestration for ML workloads and microservices at scale
5000Max nodes per cluster
72%Enterprise adoption rate
Auto-scaleZero-touch scaling
CNCFCloud Native Foundation
HOW WE USE IT
Kubernetes in our stack
We deploy and manage production Kubernetes clusters for ML serving, microservice architectures, and high-availability applications. Helm charts, ArgoCD GitOps, Prometheus monitoring, and proper RBAC are standard deliverables in every K8s engagement.
CAPABILITIES
What we deliver
- EKS, GKE, and AKS managed cluster setup
- Helm chart development and management
- ArgoCD GitOps deployment pipelines
- Prometheus + Grafana observability stack
- Horizontal Pod Autoscaler for ML inference
- RBAC, network policies, and security hardening
USE CASES
How we apply Kubernetes
ML Model Serving Platform
K8s deployment of multiple ML models with HPA for traffic-based scaling and canary deployments.
Microservices Architecture
Service mesh with Istio, circuit breakers, distributed tracing with Jaeger, and centralized logging.
GitOps CI/CD Pipeline
ArgoCD-managed deployment pipeline where every git commit to main triggers automated production deployment.
Engineering Stack
Built with the tools that matter
38 production-grade technologies — every one battle-tested in shipped products.
Frequently Asked Questions
Didn't find what you were searching for? Reach out to us at [email protected] and we'll assist you promptly.
Kubernetes is the right choice when you need fine-grained control over scaling, networking, and resource allocation — particularly for AI inference workloads, microservices architectures with complex service-to-service communication, or workloads that do not fit serverless constraints (execution time limits, cold start latency, memory limits). Serverless (Lambda, Cloud Run) is better for event-driven, short-lived tasks. Kubernetes pays off when you have 5+ services, GPU workloads, or advanced autoscaling requirements.
A production Kubernetes cluster includes: namespaces for environment isolation, resource requests and limits on all workloads, Horizontal Pod Autoscaler for dynamic scaling, a managed ingress controller with TLS termination, cert-manager for certificate management, Prometheus and Grafana for observability, Network Policies for zero-trust networking, and GitOps deployment with ArgoCD or Flux. We use managed Kubernetes services (EKS, GKE, AKS) to reduce operational overhead.
Setting up a production-grade Kubernetes cluster with CI/CD, observability, and security hardening typically takes 4-8 weeks. Migrating an existing application from Docker Compose or EC2 to Kubernetes takes 4-6 weeks. Adding GPU node pools for ML inference workloads takes 2-3 weeks on top of an existing cluster.
FROM OUR CLIENTS
Built with teams who ship
The team took our AI concept from whiteboard to production in 10 weeks. The architecture they designed handles 10x our expected load with no issues.
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SERVICES THAT USE KUBERNETES
Related service offerings
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