Serverless computing, simplified.

“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).

Your Serverless Workloads Explained

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.

Instrumentation increases observability

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.

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.

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  • $0.30 per GB as you grow
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