Llm on René Zander | AI Automation Consultant

German PII Redactor: Covering the 5% Blind Spot in SAP Data Masking

Tue, 21 Apr 2026 12:00:00 +0000

The problem

Every SAP shop copies production data into dev, QA, and training landscapes. It is how you reproduce customer bugs on real payloads, load-test a release, and train end-users on data that looks like what they will see on Monday.

Every copy is a compliance event. DSGVO, Art. 5 requires personal data to be processed only for legitimate purposes and pseudonymised where practical. Most DACH enterprises have bought a deterministic masking tool — SAP TDMS, Delphix, Informatica TDM, IBM InfoSphere Optim — and wired it into the copy job. The tool rewrites classified columns: KNA1-NAME1 becomes Mustermann, BSEG-IBAN becomes a fake IBAN that still passes checksum, USR02-BNAME becomes USER042. That covers the ~95% of PII that lives in schema-aware, row-level columns.

How to Choose an LLM for Production: 7 Criteria That Matter

Fri, 17 Apr 2026 07:00:00 +0200

Most teams pick an LLM for production the wrong way. They read a leaderboard, pick the top model, and wire it into an endpoint. Six weeks later they hit a rate limit during a traffic spike, or a compliance reviewer asks where EU data is processed, or the p99 latency kills a user-facing flow. Then the real selection work starts, under pressure, in production.

This guide is how to choose an LLM for production the right way, before any of that happens. I run AI agents and LLM-backed automations for DACH clients, and every production deployment I’ve shipped went through the same seven-criteria filter. The order matters. Skip one and you will find out later, usually on a weekend.

Self-Hosted LLM vs API Cost: Break-Even Analysis (2026)

Thu, 16 Apr 2026 09:00:00 +0200

Every few months a client asks me the same question. “We’re burning $8k/mo on Claude. Should we self-host Llama?” The answer is almost always no, and the reason has nothing to do with whether the model is good enough. It has to do with what a GPU costs when it’s idle, and how much engineering time it takes to keep a serving stack healthy at 3am.

This guide breaks down self-hosted LLM vs API cost with real numbers. Hetzner GPU pricing, RunPod and Lambda hourly rates, Claude Sonnet 4.6 and Haiku 4.5 token pricing, and the break-even points that actually matter. The goal is to give you a decision framework, not a marketing pitch for either side.

LLM API Comparison 2026: Best API for Production

Wed, 15 Apr 2026 08:00:00 +0200

I have five LLM providers wired into production code. Not in side projects. Real things I get paid to maintain. After two years of swapping between them, retrying failed calls at 3am, and debugging tool-use schemas, I have opinions.

This is an LLM API comparison focused on what actually matters when you ship. Not benchmark leaderboards. Not marketing spec sheets. Features, SDK quality, failure modes, tool-use reliability, and whether the docs will waste your afternoon.

LLM API Cost Comparison 2026: Framework, Not a Stale Table

Sat, 11 Apr 2026 13:00:00 +0200

Every llm api cost comparison I see online has the same problem: it goes stale in two weeks. Providers drop a new tier, another one halves their output price, a reasoning model ships at triple the cost. By the time the post ranks on Google, the numbers are wrong and the rankings are meaningless.

So this piece is not a table you check once. It is the framework I use to model llm api pricing for my own production workloads, plus a snapshot of list prices as of April 2026, plus four realistic scenarios run through that framework. The scenarios are the point. Plug your own traffic into them, change the model, get a defensible monthly cost number.

Self-Hosted LLM on Kubernetes: A Production vLLM Deployment

Sun, 05 Apr 2026 07:00:00 +0200

Most teams asking about self-hosted LLM Kubernetes deployments should not be running Kubernetes for this at all. The honest answer is that vLLM on a single GPU box, wrapped in systemd or Docker Compose, covers more use cases than anyone wants to admit. Kubernetes earns its keep only when you already run it, or when you need horizontal scaling, multi-tenant isolation, or proper rolling deploys across a GPU node pool.

RAG Pipeline Tutorial: Build a Production Document Q&A System with Qdrant and Claude

Wed, 01 Apr 2026 09:00:00 +0200

Most RAG tutorials ship a toy. You paste a PDF, it answers one question, and the moment you point it at 500 documents the retrieval goes sideways and Claude hallucinates half the citations. This one is the opposite. I am going to walk through the pipeline I actually run in production, line by line, with the tradeoffs called out where they bit me.

The verdict first. If your corpus is under 200k tokens and rarely changes, skip RAG and stuff it all into Claude’s context window. If your corpus is larger, changes often, or you need hard citations, build this RAG pipeline tutorial end to end with Qdrant, a local embedding model, and Claude Sonnet 4.6. That is the sweet spot for cost and quality in 2026.