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John Jainschigg

Director of Open Source Initiatives

John Jainschigg is Director, Open Source Initiatives at Mirantis, and is a cloud engineer, software developer, and technology journalist. Previously, he was Executive Director of ZiffDavis Enterprise's Internet and Community Laboratory and Online Editor in Chief of United Business Media's Dr. Dobb's Journal of Software Development.

All posts by John Jainschigg

Cloud securityMarch 13, 2026

Hybrid Cloud Management: Best Practices and Tools

Explore hybrid cloud management solutions and proven best practices to enhance visibility, automation, and control across environments.

AIMarch 06, 2026

AI Observability: Tools and Best Practices

Enterprises running AI workloads in production need clear visibility into model behavior, cost, and reliability. AI observability addresses that need by combining real-time monitoring, tracing, and quality evaluation. Teams use it to optimize performance and control spend; it also helps meet compliance requirements.

AIMarch 06, 2026

Optimizing Inference Costs: The Complete Guide

Most of your AI budget will go to inference, not training. Every prompt, every API call, every generated token adds to the bill. For enterprises putting AI into production, that makes inference cost the main lever for sustainable scale.

AI InferenceMarch 04, 2026

Improving Inference Latency: Guide and best practices

Inference latency is the single most visible performance metric in modern AI systems. It is the delay between when a user or system sends a request to an AI model and when a useful response (or the first token, in streaming) is returned, and it directly shapes user experience, cost, and scalability. When inference latency is high or unpredictable, applications feel sluggish, resource bills climb, and scaling becomes harder. Getting inference latency right matters for any team running AI inference in production.

AIFebruary 23, 2026

LLM Optimization: Techniques and Guide

The way you place models near data, schedule and bin pack GPU workloads, and enforce multi-tenant and sovereignty boundaries has more impact on real-world outcomes than marginal changes in model architecture. This guide focuses on that version of LLM optimization: optimizing how you deploy and operate LLM powered applications on GPUs so that performance, cost, and compliance constraints are all satisfied.

AIFebruary 13, 2026

Meeting AI Compliance Requirements: The Definitive Guide

Enterprises face mounting pressure to meet AI compliance requirements as regulatory frameworks take effect across the globe. Let's review what meeting AI compliance requirements looks like, why it matters, and how to future-proof your business against evolving regulatory demands.

ci/cdFebruary 04, 2026

How to Achieve Composable Architecture: Guide and Best Practices

Composable architecture is emerging as the standard for organizations that want flexibility, modularity, and independence in how they design software and infrastructure. Instead of locking into a single stack or vendor, teams assemble platforms from reusable components that can be mixed, upgraded, and scaled according to need.

AIJanuary 16, 2026

How to Achieve Sovereign AI: Guide and Best Practices

This guide shows you how to build AI infrastructure that keeps your data, models, and operations sovereign under your control, while also strengthening operational agility.

John Jainschigg

Director of Open Source Initiatives

All posts by John Jainschigg

Hybrid Cloud Management: Best Practices and Tools

AI Observability: Tools and Best Practices

Optimizing Inference Costs: The Complete Guide

Improving Inference Latency: Guide and best practices

LLM Optimization: Techniques and Guide

Meeting AI Compliance Requirements: The Definitive Guide

How to Achieve Composable Architecture: Guide and Best Practices

How to Achieve Sovereign AI: Guide and Best Practices

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