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Devops Serverless

In traditional IT monitoring, you were responsible for managing and maintaining your own resources—whether they’re on-prem servers or databases, caches or load balancers. But in today’s dynamic software environments, with managed cloud services and functions, the number of resources you’re responsible for operating has been reduced—and “serverless” computing is just another extension of this new world.

What is serverless computing?

“Serverless” isn't truly serverless. The same VMs and containers you traditionally managed are still there—but instead of you having to patch, secure, and scale those servers, the responsibility is now in the hands of the cloud providers. Another way to think of serverless is “Compute as a Service” (CaaS) or “Functions as a Service” (FaaS).

Examples include: AWS Lambda, AWS S3, AWS DynamoDB, AWS SQS, AWS API Gateway, AWS SES, Azure Cloud Functions, Google Cloud Functions, Google ML Engine

What is serverless?

Understand all your serverless workloads

When you instrument your serverless environment, you’ll know exactly what happens in the code when it responds to a request. Good instrumentation measures these transactions, increases the observability of your systems, and emits useful metrics, logs, and traces.

Understand all your serverless workloads

Instrumentation increases observability

  • Logs: “The database won’t start after the update.”
  • Metrics: “Our application is 35% slower than last week after this configuration change.”
  • Traces: “What are the dependencies for this service?”


Every component should be instrumented

  • Mobile app / browser
  • Cloud compute services (AWS, Azure, GCP)
  • Application & application microservices
  • Server OS (cloud, on-prem, or virtual)
  • Managed Services


Instrumentation increases observability

Instrumentation should be painless

Our cloud-based platform does for your monitoring what serverless is doing for your development and operations processes. It’s easy to instrument all your resources and services in minutes, trace transactions from end to end, then start querying, analyzing, and receiving intelligent alerts for what’s happening throughout your environment.

Requirements for effective cloud-native workload monitoring

Code-level instrumentation

By instrumenting everything in your dynamic environment, you can measure (and optimize) the amount of work your workloads are doing.

Distributed tracing

Understand and correlate every interaction on a request’s journey through the code and dependent services, so you can quickly locate, identify, and debug bottlenecks.

Analytics, applied intelligence, and alerting

With intelligent capabilities, you can explore the data, have correlations automatically surfaced, and reduce alert noise.

Know your workloads with New Relic serverless monitoring

From frontend applications to backend infrastructure, understand what’s happening in your serverless architecture—so you can boost stability, allocate resources intelligently, and optimize your cloud spend.

Know your workloads