By Every enterprise racing to deploy AI agents is hitting the same wall: local deployments expose sensitive credentials, cloud providers charge six figures just to choose a data region, and self-hosting brings infrastructure complexity that kills momentum before the first agent goes live. The tooling has outpaced the operational reality—and most organizations are learning this the hard way.
amazee.ai, a Mirantis company, has built amazeeClaw to close that gap. Rooted in a decade of managing Kubernetes infrastructure for customers across 20 countries, amazee.ai brings data sovereignty, containerized isolation, and one-click provisioning to OpenClaw—the open-source agentic AI platform that has become the most-starred repository on GitHub.
The Guest: Michael Schmid, CEO & Founder at amazee.ai
Key Takeaways
- OpenClaw agents live where teams already communicate—Slack, Telegram, WhatsApp, Signal—eliminating the context-switch overhead of dedicated AI apps and enabling team-wide shared agents that continuously improve.
- Running OpenClaw locally is deceptively dangerous: without OS-level sandboxing, the agent automatically discovers browser cookies, LinkedIn sessions, and local network devices and will use them—sometimes at a $400-per-night cost.
- amazeeClaw provisions a fully isolated, containerized OpenClaw instance in under 25 seconds—including container spin-up, security configuration, AI key injection, storage, and backups—at $15 per agent per month.
- Regional data residency (US, EU, Australia) is guaranteed for both the OpenClaw runtime and the LLM inference layer—no enterprise agreement required, a direct challenge to the industry’s “data residency tax.”
- amazeeClaw includes built-in budget controls so agents can’t run unsupervised overnight and generate unexpected LLM bills—a real failure mode Schmid experienced firsthand.
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In this exclusive interview with Swapnil Bhartiya at TFiR, Michael Schmid, CEO & Founder at amazee.ai, explains why the shift to agentic AI is fundamentally different from the ChatGPT era, what makes self-hosting OpenClaw a security liability most teams underestimate, and how amazeeClaw delivers production-grade managed hosting with guaranteed data sovereignty—at a price point that removes the traditional enterprise barrier.
OpenClaw vs. ChatGPT vs. MCP: Why Persistent, Embedded Agents Change Everything
The conversation opens with a foundational distinction that reshapes how enterprises should think about AI tooling. ChatGPT and similar interfaces operate as stateless, single-session tools. OpenClaw—the open-source agentic AI platform built in TypeScript—operates as a continuously learning assistant that stores context, builds memory over time, and lives inside the communication channels teams already use every day.
Q: What makes OpenClaw fundamentally different from ChatGPT or an MCP-based workflow?
Michael Schmid: “OpenClaw allows you to actually use it where you’re already communicating. It has integrations into Telegram, WhatsApp, Slack, Signal, iMessage—probably 50 other channels. In the past, if I wanted to talk to my AI, I had to open a specific app. Now with OpenClaw, I can talk where I’m already talking to my friends. The AI now comes to you. And the fact that it’s open source means anybody can add or remove models and connect to the ones they really want to use.”
On the self-improving memory architecture that separates OpenClaw from session-based tools:
Q: How does OpenClaw’s continuous learning actually work in practice?
Michael Schmid: “The whole idea of these agents is that they continuously self-improve and learn. Technically, it just stores text data in the background while it learns about you. If you ask it every day about one specific topic, the next day it might automatically solve the problem for you because you’ve asked it the last three times. It goes away from just a one-time question AI to an assistant that continuously learns and improves.”
The Two Enterprise Use Cases That Are Already Transforming amazee.ai Internally
Schmid draws directly from amazee.ai’s own deployment of over ten internal OpenClaw agents to define the two primary enterprise use cases that deliver measurable ROI. The distinction between personal assistant agents and team-shared agents is critical for enterprise AI adoption planning.
Q: How are you deploying OpenClaw inside amazee.ai today?
Michael Schmid: “I really see two main use cases. The first is to give every individual employee their own personal assistant—to answer emails, manage calendar, remind them of commitments. For every meeting, there’s a transcription happening. While I’m in the meeting and say ‘I’m going to do this until tomorrow,’ my own personal OpenClaw reads the transcript after the meeting and immediately creates a to-do list for me. The second use case is agents shared between teams. We have at least ten different OpenClauses doing all kinds of work—giving us access to data I would otherwise need to go to a website for.”
On the HubSpot and billing integrations that replace dashboard navigation with natural language queries:
Q: Can you give a specific example of a shared team agent in production?
Michael Schmid: “We have a Dave Claw—that’s what our billing system is called. I can go and say, ‘I have a QBR coming up with this customer. Can you tell me everything that happened in the last six months?’ And it will tell me how much their usage changed, how many deployments they had. Now it can also go to the LinkedIn of the customer and see what has changed, what they’ve published. So if the next employee asks for a QBR, the OpenClaw by itself will go and look for the company information online as well. We’re working together on improving these agents that help the whole company.”
Why Local and Self-Hosted OpenClaw Is a Security Liability—And What the Real Risks Are
The most technically substantive section of the interview addresses a risk most OpenClaw enthusiasts overlook: the operating system’s inability to sandbox an AI agent that has autonomous goal-seeking behavior. Schmid is direct: installing OpenClaw on a personal computer or home network is not a safe production environment for enterprise data.
Q: What are the real security risks of running OpenClaw locally?
Michael Schmid: “Operating systems by default don’t really have very good sandboxing capabilities. OpenClaw will automatically look for cookies—it will find your LinkedIn cookie, your X cookie, all the different things on your computer that are there, and it will happily use them. Having an OpenClaw that has access to all your personal data, to all your cookies, to all your data by default, I think is a big problem. We have seen already some security issues around that. And even if you install it on a Mac Mini and give it access to your network, there’s still a lot in your local network that can be seen—file sharing, cameras, routers that can automatically be configured.”
On the amazeeClaw architectural response—containerization with explicit, conscious credential grants:
Q: How does amazeeClaw address these risks at the infrastructure level?
Michael Schmid: “We’re running it in the cloud, meaning it does not have access to your local environment unless you specifically give it access. Plus we’re running it inside a container, meaning it does not by default have access to any credentials except you specifically decide, ‘I will give you this credential.’ It’s always a conscious decision by the owner of that claw. It cannot by default find all these things and try to use them in either good or bad ways.”
Data Sovereignty Without a Six-Figure Enterprise Agreement
For enterprises operating under GDPR, Swiss data protection law, Australian Privacy Principles, or sector-specific regulations, data residency is not optional. Schmid identifies a structural market failure: the major AI providers charge $100,000+ enterprise agreements simply to guarantee where data is processed—a cost amazeeClaw eliminates at the platform level.
Q: How does amazeeClaw handle data sovereignty for regulated industries?
Michael Schmid: “What we guarantee is that if you select the EU, everything around this OpenClaw happens inside the EU, in a data center that is physically located in the EU. That includes the LLM itself running in Europe. That’s a service that if you try to get from the big AI providers, you’re going to have to buy an enterprise agreement that costs you at least $100,000 a year just to be able to define data residency. There’s also this data residency tax where companies charge you a lot more just to choose the region. I don’t believe that’s a good thing. Every country is writing more laws that things can only run in their regions—and we want to offer solutions so customers can sleep well.”
Kubernetes-Native Multi-Tenancy, Budget Controls, and the Road Ahead
amazeeClaw is built on Lagoon, amazee.io’s open-source Kubernetes orchestration layer that manages 100+ clusters across the globe. Each OpenClaw instance runs in a dedicated container with full tenant isolation, auto-scaling, and penetration-tested separation. The platform is pursuing ISO 27001 and SOC 2 Type 2 certification. Schmid also addresses a critical operational lesson: agentic AI can run autonomously overnight and generate unexpected LLM costs—amazeeClaw includes a configurable monthly budget cap to prevent exactly that.
Q: What’s coming next for amazeeClaw—what’s in the pipeline?
Michael Schmid: “One of the cool things is agent-to-agent communication. Imagine you have a finance claw and an accounting claw that need to talk to each other to figure out something—instead of copy-pasting data between them, why don’t they just talk to each other? That’s not a simple problem from a security point of view, so that’s one of the things we’re looking at. We’re also actively monitoring other agentic harnesses. OpenClaw is not the only one. As we know from history, it wasn’t clear in the beginning that Kubernetes was going to be the winner—so we will continuously monitor and research other tools.”
To try amazeeClaw free—two weeks, $10 in AI credits, no credit card required—visit amazee.ai or go directly to claw.amazee.ai.
