Datadog Case Studies Eight Sleep achieves end-to-end observability with Datadog

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	Eight Sleep achieves end-to-end observability with Datadog Datadog

Eight Sleep achieves end-to-end observability with Datadog

Datadog

Technology Category	Analytics & Modeling - Real Time Analytics Application Infrastructure & Middleware - API Integration & Management
Applicable Industries	Healthcare & Hospitals
Applicable Functions	Product Research & Development Quality Assurance
Use Cases	Predictive Maintenance Real-Time Location System (RTLS)
Services	Cloud Planning, Design & Implementation Services Data Science Services
Challenge	Eight Sleep, a sleep fitness company based in New York City, was in need of a robust observability solution. The company wanted to better understand how users were experiencing its app and prevent any issues before they occurred. At the time, Eight Sleep used a solution that performed uptime testing for a few public endpoints, but it was too basic and lacked configurability. Engineers often got paged in the middle of the night for what ultimately proved to be false alarms. With a small development team, Eight Sleep needed a tool that could help it accomplish tasks quickly and easily. The company's competitors had three to four times as many engineers, so they needed a tool that could do the job it said it could do with minimum work required on their side. Read More
About Customer	Eight Sleep is a sleep fitness company based in New York City. The company has developed a proprietary technology that uses AI and machine learning models to track bio signals and optimize body recovery and rest through temperature control. The company makes a pod that uses sensors to track and improve sleep by dynamically adjusting mattress temperatures based on a user’s sleep stages. The sensors also record key health metrics for each user. Customers use a paired app to view sleep and health metrics, adjust temperatures, get insights, and more. Data collected from sensors is streamed to the company’s AWS cloud-hosted platform. Read More
Solution	Eight Sleep adopted Datadog Application Performance Monitoring (APM) to improve observability across the application. APM gave the company improved visibility into how the application was performing. The development team could also build dashboards to visualize telemetry from applications, which gave them easy visibility into latency or regressions from code changes. The team then onboarded Datadog Synthetic Monitoring to observe how its systems and applications were performing for end users using simulated requests and actions. Using Datadog Synthetic Monitoring enabled the team to compose multistep API synthetic tests that replicated user journeys, which gave them confidence they would be notified when something broke. Eight Sleep also recently added Datadog Real User Monitoring (RUM) to enable end-to-end visibility into user journeys, which enables his small development team to deliver new platform features and functionality faster. Read More Log in to view content
Contents

Technology Category

Analytics & Modeling - Real Time Analytics

Application Infrastructure & Middleware - API Integration & Management

Applicable Industries

Healthcare & Hospitals

Applicable Functions

Product Research & Development

Quality Assurance

Use Cases

Predictive Maintenance

Real-Time Location System (RTLS)

Services

Cloud Planning, Design & Implementation Services

Data Science Services

Challenge

Eight Sleep, a sleep fitness company based in New York City, was in need of a robust observability solution. The company wanted to better understand how users were experiencing its app and prevent any issues before they occurred. At the time, Eight Sleep used a solution that performed uptime testing for a few public endpoints, but it was too basic and lacked configurability. Engineers often got paged in the middle of the night for what ultimately proved to be false alarms. With a small development team, Eight Sleep needed a tool that could help it accomplish tasks quickly and easily. The company's competitors had three to four times as many engineers, so they needed a tool that could do the job it said it could do with minimum work required on their side.

About Customer

Eight Sleep is a sleep fitness company based in New York City. The company has developed a proprietary technology that uses AI and machine learning models to track bio signals and optimize body recovery and rest through temperature control. The company makes a pod that uses sensors to track and improve sleep by dynamically adjusting mattress temperatures based on a user’s sleep stages. The sensors also record key health metrics for each user. Customers use a paired app to view sleep and health metrics, adjust temperatures, get insights, and more. Data collected from sensors is streamed to the company’s AWS cloud-hosted platform.

Solution

Eight Sleep adopted Datadog Application Performance Monitoring (APM) to improve observability across the application. APM gave the company improved visibility into how the application was performing. The development team could also build dashboards to visualize telemetry from applications, which gave them easy visibility into latency or regressions from code changes. The team then onboarded Datadog Synthetic Monitoring to observe how its systems and applications were performing for end users using simulated requests and actions. Using Datadog Synthetic Monitoring enabled the team to compose multistep API synthetic tests that replicated user journeys, which gave them confidence they would be notified when something broke. Eight Sleep also recently added Datadog Real User Monitoring (RUM) to enable end-to-end visibility into user journeys, which enables his small development team to deliver new platform features and functionality faster.

Impact #1	Eight Sleep’s use of Datadog solutions helps it identify the root cause of issues and troubleshoot faster.
Impact #2	As a result, MTTR has gone from about a half hour to just minutes.
Impact #3	Eight Sleep’s development team is also confident their remediation steps will work.

Impact #1

Eight Sleep’s use of Datadog solutions helps it identify the root cause of issues and troubleshoot faster.

Impact #2

As a result, MTTR has gone from about a half hour to just minutes.

Impact #3

Eight Sleep’s development team is also confident their remediation steps will work.

Benefit #1	MTTR has gone from about a half hour to just minutes.
Benefit #2	Move toward 99.9 percent reliability on alarm dismissal and temperature controls.

Benefit #1

MTTR has gone from about a half hour to just minutes.

Benefit #2

Move toward 99.9 percent reliability on alarm dismissal and temperature controls.

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Overview

Eight Sleep achieves end-to-end observability with Datadog

Operational Impact

Quantitative Benefit