Eight Sleep achieves end-to-end observability with Datadog
Customer Company Size
SME
Region
- America
Country
- United States
Product
- Datadog Application Performance Monitoring
- Datadog Synthetic Monitoring
- Datadog Real User Monitoring
Tech Stack
- AWS Cloud
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Productivity Improvements
- Customer Satisfaction
Technology Category
- Analytics & Modeling - Real Time Analytics
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
About The 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.
The 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.
The 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.
Operational Impact
Quantitative Benefit
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