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Partner Interview
Published January 11, 2023

Chronosphere: Datadog, Prometheus, & Centralizing Observability

Executive Bio

Sales Account Manager at Chronosphere

Interview Transcript

Disclaimer: This interview is for informational purposes only and should not be relied upon as a basis for investment decisions. In Practise is an independent publisher and all opinions expressed by guests are solely their own opinions and do not reflect the opinion of In Practise.

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Let’s talk about Chronosphere as a starting point. Could you walk through the ideal customer profile for Chronosphere and what type of accounts you focus on mainly? You went over as strategic tech first; I'd love to hear more detail.

At a high level, the key segment that Chronosphere focuses on is companies interested in centralizing observability, across the majority of their businesses or business units. That tends to be companies looking towards open-source technologies as well, looking at Prometheus or OTel, OpenTelemetry; there's a significant shift going on the market, which I'm sure you're familiar with. Companies interested in moving along with that shift tend to be a good profile, as well as companies with cost concerns with their metrics. They're collecting a lot of metrics across their systems, and they define metrics as a critical differentiation for them, not just in troubleshooting but also in how they operate the app. That's the most significant difference in our fundamental perspective versus Datadog or other companies.

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Could you talk through the story behind Chronosphere? I know it came out of Uber, but what’s the story for the company?

A bit of the background is that the two founders and the head of engineering all worked at Uber, and there are multiple core engineers who all worked at Uber. When Uber moved to cloud native, they saw exponential data growth on the metric side. The reason is, say you're running 1,000 VMs, and now you've moved to cloud native. You’ve been able to consolidate, so now you're running 800 VM. Now you're running 10,000 containers on top of those VMs, and you're collecting metrics from 10,000 different individual resources, whereas before, you were only collecting metrics from 1,000. When they made that shift, they were hitting above a billion metrics a second, as an application, and they needed a way to have something scalable.

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