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Introducing AIOStack

•AIOStack Team•5 min read

Learn how AIOStack helps you discover and monitor AI services in your cloud infrastructure with eBPF-powered sensors.

Discovering Shadow AI in Your Infrastructure

In modern cloud environments, AI and ML workloads are proliferating faster than ever. Developers are integrating LLM APIs, running inference models, and deploying AI agents - often without centralized visibility or oversight. This creates a "Shadow AI" problem where organizations don't know what AI services are running, how they're being used, or what data they're accessing.

AIOStack solves this problem by providing automatic discovery and observability for AI/ML workloads in Kubernetes clusters.

How It Works

AIOStack uses eBPF (extended Berkeley Packet Filter) technology to provide deep visibility into your applications without requiring any code changes or application restarts. Here's what makes it powerful:

Zero-Instrumentation Tracing

Unlike traditional monitoring solutions that require you to add SDKs or agents to your code, AIOStack automatically detects:

  • LLM API calls (OpenAI, Anthropic, Cohere, Hugging Face)
  • ML library usage (PyTorch, TensorFlow, scikit-learn)
  • AI agent workflows and multi-step executions
  • Python, Node.js, Java, and Go applications

Privacy-First Design

AIOStack monitors metadata and patterns, not content. It can detect that your application is calling an LLM API, but it never captures the actual prompts or responses. This makes it safe to use in production environments with sensitive data.

Kubernetes-Native

Deploy as a DaemonSet with a single command. AIOStack follows Kubernetes security best practices with proper RBAC controls and can be removed just as easily.

Key Features

  1. Automatic Discovery: Find all AI/ML workloads across your cluster
  2. Real-Time Monitoring: Track API calls, latency, and usage patterns
  3. Shadow AI Detection: Identify unsanctioned AI tool usage
  4. Agent Workflow Visualization: Understand complex multi-step AI executions
  5. Multi-Runtime Support: Works with Python, Node.js, Java, and Go

Getting Started

Installing AIOStack is straightforward:

curl -fsSL https://raw.githubusercontent.com/aurva-io/AIOstack/main/install.sh | bash

Within minutes, you'll have visibility into all AI/ML activity in your cluster.

What's Next?

We're actively developing new features including:

  • Enhanced agent workflow visualization
  • Cost tracking and optimization recommendations
  • Custom alerting for unusual AI usage patterns
  • Integration with major observability platforms

Check out our documentation to learn more and get started today.

Join the Community

AIOStack is open source and we welcome contributions! Whether you want to report issues, suggest features, or contribute code, we'd love to hear from you.