Martin Mao is the co-founder and CEO of Chronosphere, an observability platform built for the modern containerized world. Prior to Chronosphere, Martin led the observability team at Uber, tackling the unique challenges of large-scale distributed systems. With a background as a technical lead at AWS, Martin brings unique experience in building scalable and reliable infrastructure. In this episode, he shares the story behind Chronosphere, its approach to cost-efficient observability, and the future of monitoring in the age of AI.
What you’ll learn:
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The specific observability challenges that arise when transitioning to containerized environments and microservices architectures, including increased data volume and new problem sources.
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How Chronosphere addresses the issue of wasteful data storage by providing features that identify and optimize useful data, ensuring customers only pay for valuable insights.
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Chronosphere’s strategy for competing with observability solutions offered by major cloud providers like AWS, Azure, and Google Cloud, focusing on specialized end-to-end product.
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The innovative ways in which Chronosphere’s products, including their observability platform and telemetry pipeline, improve the process of detecting and resolving problems.
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How Chronosphere is leveraging AI and knowledge graphs to normalize unstructured data, enhance its analytics engine, and provide more effective insights to customers.
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Why targeting early adopters and tech-forward companies is beneficial for product innovation, providing valuable feedback for further improvements and new features.
How observability requirements are changing with the rise of AI and LLM-based applications, and the unique data collection and evaluation criteria needed for GPUs.
Takeaways:
- Chronosphere originated from the observability challenges faced at Uber, where existing solutions couldn’t handle the scale and complexity of a containerized environment.
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Cost efficiency is a major differentiator for Chronosphere, offering significantly better cost-benefit ratios compared to other solutions, making it attractive for companies operating at scale.
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The company’s telemetry pipeline product can be used with existing observability solutions like Splunk and Elastic to reduce costs without requiring a full platform migration.
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Chronosphere’s architecture is purposely single-tenanted to minimize coupled infrastructures, ensuring reliability and continuous monitoring even when core components go down.
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AI-driven insights for observability may not benefit from LLMs that are trained on private business data, which can be diverse and may cause models to overfit to a specific case.
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Many tech-forward companies are using the platform to monitor model training which involves GPU clusters and a new evaluation criterion that is unlike general CPU workload.
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The company found a huge potential by scrubbing the diverse data and building knowledge graphs to be used as a source of useful information when problems are recognized.
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