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The phrase “serverless computing” appears contradictory at first, but for years now, successful companies have understood the benefit of using serverless technologies to streamline operations and reduce costs. Still, this model is not ideal in every situation. So what exactly does “serverless” mean, and how can your organization benefit from it?

Traditional computing models rely on virtual or physical machines, where each instance includes a complete operating system, CPU cycles, and memory. VMware commercialized the idea of virtual machines, and cloud providers embraced the same concept with services like Amazon EC2, Google Compute, and Azure virtual machines. Serverless computing provides a newer approach that simplifies manageability and reduces costs.

Serverless computing is a cloud-based, on-demand execution model where customers consume resources solely based on their application usage. Unlike a traditional virtual machine-model where customers must build and manage an entire VM, serverless computing provides the ability to purchase only the CPU cycles and memory needed to support an application using an event-based pay-per-use model. This allows teams to sidestep much of the cost and time associated with managing hardware, platforms, and operating systems on-premises, while also gaining the flexibility to scale rapidly and efficiently.

Within this paradigm, it is possible to run entire architectures without touching a traditional virtual server, either locally or in the cloud. Serverless resources are highly flexible and are customized based on the application. REST APIs, authentication, databases, email, and video processing all have a home on serverless platforms. Services scale to meet demand. There is no need to plan for extra resources, update operating systems, or install frameworks. The provider is essentially your system administrator.

To answer the question ‘what is serverless?’ it helps to understand how it works. In a serverless architecture, applications are distributed to meet demand and scale requirements efficiently. Customers are only billed when an application is used, so this is particularly cost-effective in environments where applications run on-demand.

AWS Lambda functions are an example of how a serverless framework works:

When an application is triggered, it can cause latency as the application starts.

Inefficiencies cost technology companies up to $100 billion per year. Performing updates, installing software, and resolving hardware issues requires up to 17 hours of developer time every week.

Cloud-hosted managed services eliminate the minute day-to-day tasks associated with hosting IT infrastructure on-premises. Security, databases, and programming languages effortlessly remain up to date and secure in the serverless model. AWS Cognito, for example, is billed as an always up-to-date authentication service that complies with rigorous industry standards.

Developers stay focused on developing technologies rather than the underpinnings of applications. Operations teams focus on building consistent delivery pipelines instead of wrangling multitudes of hardware.

Despite being hosted externally, you still have direct control over the finer details such as the languages and versions of tools used. AWS Lambda allows developers to use NodeJS or Python while you can control nearly every detail of a REST API.

With a pay-per-use model, resources never go to waste. Unlike on-premises machines, shared servers, or rented virtual machines, there is no cost for downtime. Computing power is no longer a depreciating asset.

Serverless vendors make resources available exactly when you need them. There is no guesswork about the number of instances you need. Containers spawn and disappear behind the scenes based on need, with billing based on actual usage.

Serverless applications can save an organization time, effort, and resources. Still, benefits are task-specific. Services tend to run on warehouse-scale computers meant more for edge applications than high-performance computing.

You’ll benefit from serverless computing when:

Each of these applications requires minimal compute time. The average request is handled, processed, and returned quickly.

Not every application is an ideal candidate for serverless. Since you pay per transaction, an application that works under constant stress may not be a good fit.

Data usage, request handling, and processing time accumulate. Apps and services running around the clock or under extremely heavy loads become expensive quickly.

At the other end of the spectrum, infrequent requests lead services to terminate the containers running your applications. This creates latency when they need to restart.

Because serverless applications typically run in specialized environments, administrators worry about having adequate monitoring and observability capabilities. Serverless application providers do provide basic monitoring and insights, but the features are limited.

In practice, organizations tend to combine multiple services to accomplish an objective. This makes it difficult to achieve observability using built-in monitoring through AWS CloudWatch or Azure Application Logging because they work on a per-service or even per-function basis.

Meanwhile, the amount of information tracked grows exponentially over time. Vendors offer no off-the-shelf way to track services while logs expand rapidly. You can build your own data warehouse-driven dashboards in AWS Quicksight or tools such as Tableau, but creating, maintaining, and modifying these assets can be challenging and time-consuming.

Your team should incorporate performance metrics, errors, and access logs into your monitoring platform. Bugs, security, and throttling related slowdowns are concerns. Monitoring these issues in a service built from the ground up is a painstaking process.

While building custom applications is expensive, using an automatic and intelligent observability platform such as Dynatrace to monitor your serverless environments allows you to avoid flying blind and incurring the cost of building code and dashboards from the ground up.

Dynatrace’s Sofware Intelligence Platform automatically discovers applications, processes, and services running across hybrid, multicloud, and serverless environments in real-time. It captures their metrics, logs, traces, and user experience data, and analyzes them in the context of their dependencies among other services and infrastructure. Customizable, no-code dashboards in Dynatrace give you direct insight into every service without scanning through the countless logs generated across your applications.

Dynatrace combines information from more than 500 applications on AWS, Azure, and the Google Cloud Platform. Extending real-time observability beyond the tools offered by your services provider helps monitor your entire hybrid cloud environment with no blind spots, stay on top of the dynamics of your enterprise, and avoid performance issues and cost overruns.

Powerful artificial intelligence automatically consolidates meaningful data to flag slowdowns and pinpoint root causes for quick remediation. Dynatrace connects directly to serverless platforms through a single no-configuration agent, OneAgent. A layer on AWS Lambda and agents for many other tools and platforms lets applications push information directly to Dynatrace to provide 360-degree observability of your serverless architecture. OneAgent supports nearly all major programming languages and tools.

Serverless architecture increases developer efficiency, decreases time to production, and reduces costs for maintaining on-premises infrastructure or unused VMs. Today, vendors offer dozens of technologies to help you cut hardware, but often at the cost of observability.

With support for hundreds of applications, tools, and languages, Dynatrace can help you understand all your serverless applications and how they interact without the added time and effort of creating, tuning, and maintaining limited-visibility monitoring solutions. Join us for a Dynatrace Power Demo: AWS Observability with Serverless, to see how Dynatrace overcomes the observability challenges that come with serverless computing.

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