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How Chronosphere Solved Observability Challenges In Containerized Environments & Created A Company Valued At $1.6B? | Martin Mao (CEO & Co-founder)

Chronosphere's CEO shares insights on building a modern observability platform, competing with cloud providers, and leveraging AI.

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Martin Mao is the co-founder and CEO of Chronosphere, an observability platform built for the modern containerized world and last valued at $1.6B. 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:

  • The specific observability challenges that arise when transitioning to containerized environments and microservices architectures, including increased data volume and new problem sources.

  • 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.

  • 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.

  • The innovative ways in which Chronosphere's products, including their observability platform and telemetry pipeline, improve the process of detecting and resolving problems.

  • How Chronosphere is leveraging AI and knowledge graphs to normalize unstructured data, enhance its analytics engine, and provide more effective insights to customers.

  • 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.

  • 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.

  • 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.

  • Chronosphere's architecture is purposely single-tenanted to minimize coupled infrastructures, ensuring reliability and continuous monitoring even when core components go down.

  • 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.

  • 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.

  • 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.

In this episode, we cover:

  • (00:00) Introduction to Martin and Chronosphere

  • (00:46) The origin story of Chronosphere at Uber

  • (01:35) The specific observability problems in containerized environments

  • (03:48) Available services at the time Chronosphere was founded

  • (05:08) Getting the first five customers

  • (06:42) The importance of observability and convincing initial customers

  • (08:50) Selling to companies operating at scale

  • (09:37) Chronosphere's products and business model

  • (13:14) Selling to customers using competitors' products

  • (14:10) The importance of predictable costs for customers

  • (15:45) Cloud cost management and observability

  • (18:11) Competing with cloud provider observability products

  • (23:16) Product development philosophy

  • (24:23) Examples of tech-forward customers

  • (26:57) Focus on containerized applications

  • (29:09) Marketing an observability product and customer acquisition

  • (32:03) Observability for AI companies

  • (36:42) Leveraging AI for Chronosphere's product

  • (42:50) Opportunities for adjacent products

  • (45:24) AI's impact on profitability and efficiency

  • (47:52) What Martin wishes he knew earlier about starting a company

  • (48:57) What Martin is consuming that's influencing his thinking

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Stay Curious.

Nataraj