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Security audits should not end with a PDF nobody uses.mkdev’s Kubernetes Security Audit combines interviews, hands-on cluster analysis and concrete recommendations, including backlog-ready user stories your team can actually act on.Check out the page and schedule a call: https://mkdev.me/b/audits/kubernetes-security-audit

AWS Fargate is still one of the most practical ways to run containers on AWS when you want production infrastructure without managing EC2 instances, node groups, patching, or cluster capacity.You still need to understand the pieces around it: ECR for the image, ECS task definitions for CPU, memory, ports and IAM roles, an Application Load Balancer for routing traffic, Route 53 or Cloud Map for service discovery, and CloudWatch for logs. Fargate removes server management, but it does not remove architecture.This mkdev article walks through the full path using a simple Spring Boot application, from local build to a running ECS service behind a load balancer.Useful if you want to understand what “serverless containers” actually means in AWS.Read the article here: https://mkdev.me/posts/aws-fargate-101-run-production-applications-without-servers

Need practical cloud and AI knowledge?mkdev webinars cover Google Cloud Run & Databases, AWS Load Balancer Controller 101, and Scaling AI Across Your Business.Check them out and register here: https://mkdev.me/webinars

Custom customer domains are a common SaaS feature, but SSL automation can become painful fast. This article shows how to handle it with Cloudflare, AWS ALB and a setup that can scale beyond a few domains. Read the full guide: https://mkdev.me/posts/how-to-setup-dynamic-customer-domains-with-ssl-cloudflare-and-aws-alb

A vector database can help your AI system understand “what the user means.” But your business probably also needs to know “which records are allowed,” “from which period,” “under which category,” and “according to which rules.”That is why choosing a database for AI should not be a trend-driven decision. Vector databases, relational databases, NoSQL systems, and data warehouses all have their place. The difficult part is understanding where semantic search ends and structured retrieval begins.The more serious the AI product, the more important this distinction becomes.Before building another RAG prototype, it is worth asking whether the problem is actually semantic, structured, or a combination of both.https://mkdev.me/posts/which-database-when-for-ai-are-vector-databases-all-you-need

Cloud projects don’t fail because AWS or GCP lack options. They fail because there are too many options, too many shortcuts and not enough clarity. mkdev helps teams design practical cloud solutions that fit their business. Check out the page and schedule a call: https://mkdev.me/b/consulting/public-cloud

Cloud cost optimization usually starts with quick wins: delete unused resources, rightsize oversized instances, clean up old snapshots, shut down non-production environments outside working hours, and add budget alerts.But the bigger savings usually come later, when teams start treating cost as an architectural constraint.That means choosing the right purchase model for steady workloads, using spot capacity where interruptions are acceptable, moving rarely accessed data to cheaper storage tiers, avoiding unnecessary cross-region traffic, and designing systems that scale with demand instead of running at peak capacity all the time.The goal is not to spend as little as possible. The goal is to stop paying for waste while keeping reliability and performance where they need to be.https://mkdev.me/posts/the-ultimate-guide-to-cloud-cost-optimization

From DevOps and Cloud to AI: get the latest thoughts by Pablo and Kirill on all the news topics + a collection of personally curated interesting links, every other week in your Inbox! Subscribe to mkdev dispatch here: https://mkdev.me/categories/newsletter

Public IP, private IP, Cloud Run, Cloud SQL, Serverless VPC Connector… Google Cloud networking can get confusing fast. This video breaks down one practical setup step by step. Watch it now.https://www.youtube.com/watch?v=MeynQIt3TD8

ClickOps is annoying when you have one project. It becomes dangerous when you have many.That applies to OpenAI as much as it applies to AWS, Kubernetes or any other infrastructure platform. Once you have multiple teams, multiple projects, service accounts, API keys, limits and access rules, manual configuration becomes a source of inconsistency.The Open Source Terraform Provider for OpenAI was built around that problem. It brings OpenAI administration into Terraform, so teams can manage resources in code instead of relying on screenshots, tribal knowledge and “who created this key?” conversations.There is also a more experimental side: using OpenAI platform APIs inside Terraform workflows, including model responses and image generation, and even combining them with cloud providers like AWS.It is a fun example, but the larger point is serious: GenAI platforms need the same engineering discipline as the rest of your infrastructure.https://mkdev.me/posts/announcing-the-open-source-terraform-provider-for-openai

Infrastructure problems rarely announce themselves early. mkdev audits look into your cloud, Kubernetes and security setup, identify what needs improvement, and turn it into a practical action plan for your team. Check out the page and schedule a call: https://mkdev.me/b/audits

At small scale, microservices feel manageable.At larger scale, every service needs to find other services, communicate securely, expose useful telemetry, support traffic shifting, and follow consistent authorization rules. Doing this separately in every application quickly becomes a mess.That is where service mesh comes in. It gives platform teams a common layer for service-to-service communication, usually through a control plane and a data plane made of proxies.Google Cloud’s Anthos Service Mesh, now Cloud Service Mesh, is one way to bring this model into GKE. It can simplify parts of the operational story, especially if you want managed mesh capabilities. But it also introduces important tradeoffs around sidecars, Envoy, Istio APIs, GKE Dataplane V2, eBPF, and Cilium.The article is a good reminder that “managed” does not mean “you do not need to understand it”.In 2026, service mesh is still powerful. It is also still something you should adopt deliberately.https://mkdev.me/posts/is-google-cloud-anthos-service-mesh-a-mess

This free course is all about understanding ArgoCD from the ground up. We will look at what ArgoCD does, why it matters, and how it organizes projects, applications, and deployments through its main features.Articles: https://mkdev.me/posts/what-is-argo-cd-and-why-would-you-need-gitopsVideo: https://www.youtube.com/playlist?list=PLozcbFx8FoPHUHoKfuSrkMO0ulZD-CHHu

Trying to reduce your Google Cloud Run costs?Start with the less obvious places: VPC connectors, direct egress, and whether CPU really needs to be always allocated.We explain both tips with a real billing example here: https://mkdev.me/posts/2-simple-tips-to-reduce-your-google-cloud-run-costs

Prompt engineering is not security engineering.This is one of the hardest lessons for product managers building with GenAI. A system prompt may look like a clean set of rules, but it is not the same as traditional application logic. It does not guarantee behavior. It is more like a very strongly worded suggestion to the model.That matters when your AI feature is exposed to users. A customer-facing assistant might be told not to reveal sensitive data, not to generate illegal content, not to override company policies, and not to take dangerous actions. But malicious users can still try to bypass those instructions through jailbreaks or prompt injection attacks.The business impact is not theoretical. A badly controlled AI system can create reputational damage, legal exposure, data leakage, or operational incidents. For PMs, that means AI features need proper boundaries beyond “we wrote a careful prompt.”Good GenAI product management means asking: What can the model access? What actions can it trigger? What happens if the user tries to manipulate it? What checks exist outside the model itself?We covered the practical risks product managers should understand in this article.Read it here: https://mkdev.me/posts/genai-security-risks-for-product-managers-dd73bdc2-4f2e-4227-93b3-375da081d906