Serverless allows developers to build and run applications and services without thinking about the servers actually running the code. Serverless services, or FaaS (Functions-as-a-Service) providers, instrument this concept by allowing developers to upload the code while taking care of deploying running and scaling it.AWS Lambda was the first one in the market to offer this kind.
Serverless can help create an environment that allows DevOps teams to focus on improving code, processes, and upgrade procedures, instead of on provisioning, scaling and maintaining servers.
Popular cloud providers that supports Function As A Service (FaaS) as follows:
We will compare these cloud provider’s support for Serverless in the next section but first, let’s check how it works & its advantages/disadvantages.
Quick Snapshot
Serverless services or FaaS lets you run code without provisioning or managing servers (but still servers are needed). You pay only for the compute time you consume there is no charge when your code is not running. You can run code for virtually any type of application or backend service all with zero administration. Just upload your code and FaaS provider would take care of everything required to run and scale your code with high availability. You can set up your code to automatically trigger from other services or call it directly from any web or mobile app.
Serverless computing is more cost-effective than renting or purchasing a fixed quantity of servers, which generally involves significant periods of under utilization or idle time. It can even be more cost-efficient than provisioning an autoscaling group, due to more efficient utilization of the underlying machine resources.
Serverless architecture means that developers and operators do not need to spend time setting up and tuning autoscaling policies or systems; the cloud provider is responsible for ensuring that the capacity always meets the demand.
Units of code exposed to the outside world are simple functions. This means that typically, the programmer does not have to worry about multithreading or directly handling HTTP requests in their code, simplifying the task of back-end software development.
Infrequently-used serverless code may suffer from greater response latency than code that is continuously running on a dedicated server, virtual machine, or container. This is because, unlike with autoscaling, the cloud provider typically “spins down” the serverless code completely when not in use. This means that if the runtime (for example, the Java runtime) requires a significant amount of time to start up, it will create additional latency.
Serverless computing is not suited to some computing workloads, such as high-performance computing, because of the resource limits imposed by cloud providers, and also because it would likely be cheaper to bulk-provision the number of servers believed to be required at any given point in time.
Diagnosing performance or excessive resource usage problems with serverless code may be more difficult than with traditional server code, because although entire functions can be timed, there is typically no ability to dig into more detail by attaching profilers, debuggers or APM tools. Furthermore, the environment in which the code runs is typically not open source, so its performance characteristics cannot be precisely replicated in a local environment.
Serverless is sometimes mistakenly considered as more secure than traditional architectures. While this is true to some extent because OS vulnerabilities are taken care of by the cloud provider, the total attack surface is significantly larger as there are many more components to the application compared to traditional architectures and each component is an entry point to the serverless application.
AWS Lambda | Google Cloud Functions | Azure Functions | |
Production Readiness | Production Ready | Production Ready | Production Ready |
Scalability & Availability | Automatic scaling | Automatic scaling | Manual or metered scaling (App Service Plan), or sub-second automatic scaling (Consumption Plan) |
Max # of functions | Unlimited functions | 1000 functions per project | Unlimited functions |
Concurrent executions | 1000 parallel executions per account, per region (soft limit) | No limit | No limit |
Max execution time per request | 900 secs | Default 60 secs | Default 300 secs |
Supported languages | Node.js (JavaScript), Java, C#, PHP, Go, Python and more | Node.js (JavaScript) & Python Only | C#, JavaScript, F#, Java, Python, Batch, PHP, PowerShell |
Dependencies | Deployment Package per language | npm package.json | Functions written in the dedicated editor do not require dependencies |
Deployment | ZIP uploads, Serverless Framework,In-line code edits | CLI, ZIP upload,In-line code editor, Cloud Storage or Cloud Source Repositories | Web Editor Visual Studio Team Services, OneDrive, Local Git repository, GitHub, Bitbucket, Dropbox, External repository |
Event-driven | S3, SNS, SES, DynamoDB, Kinesis, CloudWatch, Cognito, API Gateway, CodeCommit, etc. | Cloud Pub/Sub or Cloud Storage Object Change Notifications | Blob, EventHub, Generic WebHook, GitHub WebHook, Queue, Http, ServiceBus Queue, Service Bus Topic, Timer triggers |
Trigger types | Wide range of Amazon services + API Gateway | Cloud Pub/Sub and Cloud Storage + HTTP trigger | Microsoft cloud services + HTTP trigger |
Orchestration | AWS Step Functions | Not yet | Azure Logic Apps + Durable Functions |
Logging | CloudWatch Logs | Stackdriver Logging | App Services monitoring |
Monitoring | CloudWatch & X-Ray | Stackdriver Monitoring | Application Insights |
In-browser code editor | Yes | Cloud Source Repositories only | Functions environment, App Service editor |
Pricing | 1M free requests per month and 400,000 GB-seconds of compute time per month for free, then $0.20/1M invocations | 2M requests for free, then $0.40/2M invocations | 1 million requests for free, then $0.20/1M invocations, plus $0.000016/GB-s |
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