Using serverless computing has a number of obvious benefits over traditional application infrastructure - you pay only for what you use, scale up or down immediately to match supply with demand, and avoid operating any server infrastructure at all.
However, implementing maintainable and scalable applications using serverless computing services like AWS Lambda poses a number of challenges. The absence of long-lived, user-managed servers means that states cannot be maintained by the service. Longer function invocation times (referred to as cold starts) become very important to track, because they impact the response time of the service and will impose additional cost. Additionally, the transition to smaller individual components (much like breaking a monolithic application into microservices) results in a simpler deployment model, but makes the system as a whole increasingly complex.Feb. 20, 2019 12:45 AM EST
Here to help unpack insights into the new era of using containers to gain ease with multi-cloud deployments are our panelists: Matt Baldwin, Founder and CEO at StackPointCloud, based in Seattle; Nic Jackson, Developer Advocate at HashiCorp, based in San Francisco, and Reynold Harbin, Director of Product Marketing at DigitalOcean, based in New York. The discussion is moderated by Dana Gardner, principal analyst at Interarbor Solutions.
Using serverless computing has a number of obvious benefits over traditional application infrastructure - you pay only for what you use, scale up or down immediately to match supply with demand, and avoid operating any server infrastructure at all.
However, implementing maintainable and scalable applications using serverless computing services like AWS Lambda poses a number of challenges. The absence of long-lived, user-managed servers means that states cannot be maintained by the service. Longer function invocation times (referred to as cold starts) become very important to track, because they impact the response time of the service and will impose additional cost. Additionally, the transition to smaller individual components (much like breaking a monolithic application into microservices) results in a simpler deployment model, but makes the system as a whole increasingly complex.
With the rise of Docker, Kubernetes, and other container technologies, the growth of microservices has skyrocketed among dev teams looking to innovate on a faster release cycle. This has enabled teams to finally realize their DevOps goals to ship and iterate quickly in a continuous delivery model. Why containers are growing in popularity is no surprise — they’re extremely easy to spin up or down, but come with an unforeseen issue.
However, without the right foresight, DevOps and IT teams may lose a lot of visibility into these containers resulting in operational blind spots and even more haystacks to find the presumptive performance issue needle. 
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