Blog
Multi-Tenancy Patterns and Which One Is Correct for You
One core topic in Platform Engineering is multi tenancy. Natively Kubernetes supports this either by dedicated namespaces or dedicated clusters per tenant. Both come with advantages and disadvantages and need to be selected based on the use-case at hand. However they sit at two ends of tenant isolation. With this article we are trying to shed some light on mechanisms for tenant isolation, that sit between those opposites. All of these models is really a story about which layer of the stack people have chosen to bolt tenancy onto, and what they gave up to do it, as Kubernetes has no built in concepts of "tenancy". Therefore, the following 4 patterns will be discussed: Namespaces, separate clusters, vCluster and Capsule.
Four days of hacking in Potsdam
From 21st to 24th of July, the Naira team joined multiple open-source projects from the ApeiroRA and NeoNephos ecosystem in the SAP Innovation Center in Potsdam for a four-day hackathon. Our goal was simple to state and a lot more fun to build: bring sovereign inference and AI engineering closer together and figure out where the pieces of the wider ecosystem actually fit.
Meine eigene KI: Ein lokaler Chatbot als Praktikumsprojekt
Artificial intelligence has become part of our everyday lives. Many people use programs such as ChatGPT without knowing exactly what happens in the background or where their data is sent. During my internship, I wanted to understand how an AI chatbot works and develop my own chatbot that runs directly on my computer.
Serving LLMs in Production: vLLM, KServe, and the Deployment Decisions That Matter
GPU memory pressure, batching inefficiencies, and autoscaling complexity make LLM serving fundamentally different from traditional ML. We compare three production deployment strategies for vLLM — standalone, KServe, and BentoML — then go deep on KServe with deployment patterns, canary rollouts, and its role in a broader LLMOps platform.
Running Multi-Tenant GPU Workloads on Scaleway with vCluster
Namespace-per-team is the default way to share a GPU cluster. It's also the wrong one. This is what true multi-tenancy looks like in Terraform.
The AI Gateway Landscape: agentgateway, LiteLLM, Kong and Envoy AI Gateway
Throughout out recent events we have seen that AI Gateways have become the single most important entry point for companies. Because every team building with LLMs eventually hits the same issue: it starts with a single <Your Favorite AI Provider> key for one service and eighteen months later you have a dozen services, five model providers, a pile of MCP tool servers and no single place to answer "who's calling what, at what cost, under what policy."
