AWS, GitOps, AI Infrastructure
KubeKite helps startups and enterprises build secure, scalable cloud platforms using AWS, GitOps, and modern AI infrastructure for chatbots, MCP servers, RAG systems, Ray workloads, and optimized inference.
Productized services
Clear service definitions for teams that need platform architecture, implementation, and operating patterns without a large consulting footprint.
Build a production foundation for AI workloads with EKS or Kubernetes, GitOps, CI/CD, secrets, observability, AI data storage, and deployment paths for chatbots, MCP services, models, or RAG systems.
Make deployments repeatable, auditable, and safer across environments with practical GitOps patterns plus Kubernetes-native test gates for AI services and platform changes.
Identify waste, improve workload placement, and prepare for GPU or AI demand without letting cloud spend, Ray workloads, or inference scaling become the hidden platform risk.
Platform blueprint
KubeKite focuses on the pieces AI teams usually underestimate: secure delivery, identity, runtime operations, MCP and RAG data paths, chatbot reliability, observability, and cost-aware scaling.
Request a blueprint reviewAI platform capabilities
KubeKite turns fast-moving AI experiments into reliable platform capabilities your engineering team can deploy, test, troubleshoot, secure, and scale across AI startup and enterprise environments.
Run smoke, load, endpoint, and RAG evaluation checks close to the workloads they validate.
Capture deployment, incident, audit, and recovery workflows as executable operational assets.
Connect alerts, telemetry, logs, runbooks, and platform context for safer AI-assisted troubleshooting.
Deploy secure retrieval, tool, and agent backends with identity, secrets, data access, and observability.
Design S3-compatible storage patterns for model artifacts, RAG sources, evaluation data, and logs.
Plan Ray clusters, GPU scheduling, quotas, team isolation, monitoring, and cost visibility.
Shape online and batch inference paths with model routing, autoscaling, and performance observability.
Help AI startup teams move from demo infrastructure to secure, repeatable, investor-ready platforms.
Why KubeKite
KubeKite focuses on the infrastructure decisions that determine whether AI and Kubernetes platforms become reliable operating systems or fragile clusters of scripts.
EKS modernization, multi-cluster operations, platform standards, and production runtime design.
Argo CD delivery, app-of-apps structures, environment promotion, rollback, and release auditability.
Vault, IAM, IRSA, secrets management, network boundaries, and DevSecOps guardrails.
Chatbot reliability, MCP and RAG paths, AI SRE workflows, Ray workloads, GPU readiness, and cost-aware scaling.
Founder-led credibility
Kubekite is a founder-led platform engineering company focused on production-grade Kubernetes and AI infrastructure.
We help teams design and operate scalable cloud platforms across EKS, GitOps, secure IAM and Vault integrations, RAG and chatbot systems, Ray-based AI workloads, and modern developer platforms.
Our focus is practical infrastructure engineering: reliability, operational clarity, and the real-world tradeoffs that appear when systems move from prototypes to production.
Proof and specificity
KubeKite is building concrete technical assets that answer the enterprise buyer question: what have you actually designed, implemented, and operated?
EKS topology, Argo CD delivery flows, Vault/IAM boundaries, RAG data paths, Ray clusters, and GPU pools.
AI startup platform foundations, GitOps modernization, chatbot/RAG infrastructure, and cost optimization stories.
Terraform modules, Argo CD app templates, deployment examples, runbooks, and CI/CD quality gates.
Practical writeups on EKS, GitOps, Vault + IRSA, RAG infrastructure, Ray workloads, and Kubernetes cost control.
Starter repositories for AI platform bootstrap, secure GitOps delivery, and production runbooks.
Technical content roadmap
KubeKite will publish practical guides, blueprints, and implementation walkthroughs around the platform problems teams are actively trying to solve.
Network boundaries, secrets, IAM, observability, and deployment paths for chatbots, MCP, RAG, and inference services.
App-of-apps structures, promotion workflows, rollback strategy, and audit-friendly GitOps operations.
Practical secrets and identity patterns for AWS-native Kubernetes platforms.
Workload sizing, cluster efficiency, GPU scheduling, Run:ai-style controls, and cost telemetry.
Retrieval data paths, MCP/tool servers, secrets, evaluation jobs, observability, and release workflows.
Free 30-minute platform architecture review
Share where your team is today. KubeKite will review your goals and help identify the platform, security, delivery, AI runtime, operating model, and cost decisions that matter first.