Data Driven

rank La Vigne, technical marketing manager at Klique, takes the KubeCon stage to talk about GPU control and token control for the SUSE AI Factory. You may not know the name Klique yet — it's ClearML, same DNA, same software, evolved for modern AI implementations, sitting on top of the foundation SUSE already gives you.

Walking the expo floor, Frank kept hearing two themes: cost control for token spend, and how you integrate AI into Kubernetes. Klique's answer is governed self-serve access, with four pieces: an AI development studio for practitioners, an orchestration engine so admins can manage GPUs, CPUs, and cloud APIs on one screen, a governance engine with spend visibility and auditability, and an AI engine that can sit in front of every model with spend control behind it. Enable, control, publish, and measure — for every user, AI actor, or agent.

SUSE AI Factory is built for managing the hardware, in a way that's comfortable for Kubernetes engineers. Klique runs on top of that and talks to the AI practitioner. The question Frank would ask IT ops: would you trust your data scientists with access to your Kubernetes cluster? The answer should be no. That's not their wheelhouse. It's the same GPUs and the same hardware, viewed through two lenses — one for the platform team in SUSE Rancher, one for the AI teams. That turns the cluster into pools, policies, and models. The end user sees a well-defined endpoint. Admins keep governance over what people can do and what they can spend, so you're talking to Dana before the surprise bill shows up at the end of the month.

The LLM gateway is one governed entry point to every model. You can change keys and models to adjust cost without sending a note to every developer to update an endpoint or an environment variable. Spend controls include cost quotas, wallet budgets, and budget metering, authenticated through the gateway and metered per session. A gateway alone, Frank says, is a commodity; tying it to cost, budget, and metering is the point.

In the live view, a tenant is at 62% of their allocation. Everybody's green except Dana, who's spending all the tokens. Maybe Dana has a good reason. Now you know who to ask — in a friendly way — instead of reverse-engineering it through financial alchemy when the bill arrives.

This episode is the audio from Frank's talk at KubeCon. The video is linked in the comments. Frank also pointed people to his LinkedIn if they want to talk more about Klique.

Welcome to Data Driven, where this week’s episode comes to you live from KubeCon 2026. The conversation focused on the emerging intersection of artificial intelligence, data, and DevOps, highlighting how Kubernetes is poised to play a pivotal role in advancing AI innovation. A key theme that emerged was the rise of tokenomics and the shifting economics of AI, particularly as organizations balance cloud and on-premise solutions in pursuit of cost control and privacy. The discussion explored the growing need for collaboration between IT admins, AI builders, and data scientists, and introduced new strategies for managing AI infrastructure in a rapidly evolving hybrid world. Tune in for insights into the future of AI deployment—and what it means for your career.

Welcome to a special episode of Data Driven, coming to you from day zero of KubeCon North America 2026 in Salt Lake City.
The conversation focused on the evolving intersection between data, AI, and DevOps, with particular attention to the role of Kubernetes and its growing impact on both worlds. A key theme that emerged was the surprising disconnect between traditional data professionals and Kubernetes, even as the technology increasingly underpins cloud data services and AI workloads. The discussion explored how the convergence of DevOps and AI is forcing a new kind of collaboration, raising questions about tooling, infrastructure, and the right skill sets for future enterprise AI platforms.
Several points were raised, including the challenges of managing token costs, the need for scalable data solutions, and the importance of creating safe, productive spaces for both infrastructure admins and AI builders. Join us as we dive into the observations and trends shaping the future of data and AI at this year’s KubeCon.
Links
Watch the original live stream -https://www.youtube.com/watch?v=G2tZDGLw23o

Welcome to another episode of Data Driven, the podcast where the worlds of artificial intelligence, data science, and data engineering intersect. In this episode, the conversation focused on the crucial role of data quality, automation, and reporting in healthcare analytics.
The discussion explored topics such as Electronic Visit Verification (EVV) systems, challenges and strategies in maintaining accurate and reliable data, and the balance between leveraging automation tools like Power BI, Python, and SQL with maintaining compliance and stakeholder trust. Insights were shared on practical data engineering pitfalls, troubleshooting in real-life scenarios, and advice for those starting out in the data field.
A key theme that emerged was the importance of human judgment in collaboration with technology, and the necessity of continual learning and curiosity in an ever-evolving industry. Whether you're a seasoned data professional or just beginning your analytics journey, this episode offers practical wisdom and firsthand experiences from the front lines of data-driven decision making.
Links
Watch on YouTube -https://www.youtube.com/watch?v=XdwFCO4EQbE

Time Stamps
00:00 Tracking and Analyzing Caregiver Timesheets
06:26 Collaborating and Automating with BI Tools
09:09 Choosing the right data tools
12:51 Ensuring Data Quality Standards
17:41 AI's role in tech industry
19:35 Automating Data Management and Architecture
22:56 Setting business requirements for reports
28:20 Troubleshooting Power BI time zones
31:35 Troubleshooting and career growth journey
36:15 Stay curious and keep learning
37:53 Wrapping up the show

The conversation focused on the complex realities of deploying AI agents in enterprise environments, moving beyond the hype to examine what these agents should be allowed to do versus what they can do. One concept discussed was the critical importance of defining clear boundaries and authorizations for AI agents—emphasizing that failures often stem from insufficiently thought-out controls rather than the underlying models themselves 02:08.
A key theme that emerged was the necessity of human oversight, especially for high-stakes decisions in sectors like healthcare and finance 11:45. The discussion explored evolving practices in agent governance, accountability, and the growing need for transparency as AI systems become increasingly sophisticated and embedded in daily workflows 22:12.
Several points were raised, including the risks of over-reliance on AI, the need to maintain auditability, and the consequences of blurring the line between human and AI agency. Set against the backdrop of a rapidly shifting technological landscape, this episode offers practical insights for organizations seeking to harness AI responsibly, while staying alert to new ethical and operational challenges.
Links
Nitin on LinkedIn -https://www.linkedin.com/in/nitingupta-profile/
Watch on YouTube -https://youtu.be/2g_AZqLRib8

Time Stamps
00:00 Challenges in Enterprise AI Agents
04:14 Comparing AI tools: Muse vs. GrokBot
08:25 Challenges in AI Adoption and Trust
11:45 Establishing AI-human decision boundaries
13:18 Automating Agent Supervision
18:49 Determining AI vs. human tasks
19:49 Automating payments with AI
23:13 Human vs. AI Decision-Making
27:24 Using AI for presentation feedback
32:16 Boosting productivity for small businesses
35:20 Ethical questions in tech industries
36:35 Impact of AI on Society
41:26 Prioritizing tasks for automation
43:20 Learning from major tech frameworks