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AWS Lambda vs Serverless: What are the differences?

AWS Lambda vs Serverless

AWS Lambda and Serverless are two cloud computing technologies that provide a serverless architecture for building and deploying applications. While both are commonly used for serverless application development, there are several key differences between them:

  1. Execution Model: AWS Lambda is a compute service that allows developers to run code without provisioning or managing servers. It executes the code in response to events, such as changes to data in an Amazon S3 bucket or a table in an Amazon DynamoDB database. On the other hand, Serverless, also known as Serverless Framework or Serverless Framework for AWS, is a framework that simplifies the deployment of serverless applications. It is not a compute service like Lambda, but rather a tool for managing and deploying serverless applications on AWS Lambda.

  2. Tooling and Ecosystem: AWS Lambda is a part of the larger Amazon Web Services ecosystem, which provides various services like storage, databases, messaging, etc. It has a comprehensive set of tools and services that work seamlessly with Lambda, making it easier to build and deploy applications using other AWS services. Serverless, on the other hand, is a framework that is designed to work specifically with AWS Lambda. It provides a set of tools and APIs to simplify the deployment and management of serverless applications, regardless of the specific AWS services being used.

  3. Language Support: AWS Lambda supports multiple programming languages, including Node.js, Java, C#, PowerShell, and Python. It allows developers to write functions in their preferred language and provides the necessary runtime environment to execute them. Serverless, on the other hand, is language agnostic and supports any programming language that can be executed in a Lambda function.

  4. Configuration and Management: AWS Lambda provides a web console and command-line interface (CLI) for configuring and managing Lambda functions. It allows developers to define various aspects of the Lambda function, such as the runtime, memory allocation, timeout, and triggers. Serverless, on the other hand, provides a declarative configuration file, usually written in YAML, for defining the infrastructure and resources required for a serverless application. It also provides a command-line interface (CLI) for deploying and managing serverless applications.

  5. Event Sources and Triggers: AWS Lambda supports a wide range of event sources and triggers that can invoke a Lambda function, such as API Gateway, S3, DynamoDB, CloudWatch Events, etc. It allows developers to create event-driven architectures by specifying the events that should trigger the function. Serverless, on the other hand, provides a simplified way of defining and configuring event sources and triggers using its framework. It abstracts the underlying AWS services and provides a consistent interface for defining events and triggers.

  6. Vendor Lock-in: AWS Lambda is a proprietary service provided by Amazon Web Services and is tightly integrated with its ecosystem of services. While it allows developers to build serverless applications that can run on other cloud platforms, it may require substantial modifications to make them compatible. Serverless, on the other hand, is an open-source framework and can be used with any cloud provider that supports AWS Lambda-like compute services. It provides a consistent deployment and management experience across different cloud platforms.

In summary, AWS Lambda is a compute service for running code in a serverless manner, while Serverless is a framework for managing and deploying serverless applications on AWS Lambda. AWS Lambda is part of the broader AWS ecosystem, supports multiple programming languages, and provides tools and services for configuring and managing Lambda functions. Serverless, on the other hand, is language agnostic, provides its own set of tools and APIs, and offers a simplified way of defining and managing serverless applications.

Advice on AWS Lambda and Serverless

Need advice on what platform, systems and tools to use.

Evaluating whether to start a new digital business for which we will need to build a website that handles all traffic. Website only right now. May add smartphone apps later. No desktop app will ever be added. Website to serve various countries and languages. B2B and B2C type customers. Need to handle heavy traffic, be low cost, and scale well.

We are open to either build it on AWS or on Microsoft Azure.

Apologies if I'm leaving out some info. My first post. :) Thanks in advance!

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Replies (2)
Anis Zehani

I recommend this : -Spring reactive for back end : the fact it's reactive (async) it consumes half of the resources that a sync platform needs (so less CPU -> less money). -Angular : Web Front end ; it's gives you the possibility to use PWA which is a cheap replacement for a mobile app (but more less popular). -Docker images. -Kubernetes to orchestrate all the containers. -I Use Jenkins / blueocean, ansible for my CI/CD (with Github of course) -AWS of course : u can run a K8S cluster there, make it multi AZ (availability zones) to be highly available, use a load balancer and an auto scaler and ur good to go. -You can store data by taking any managed DB or u can deploy ur own (cheap but risky).

You pay less money, but u need some technical 2 - 3 guys to make that done.

Good luck

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My advice will be Front end: React Backend: Language: Java, Kotlin. Database: SQL: Postgres, MySQL, Aurora NOSQL: Mongo db. Caching: Redis. Public : Spring Webflux for async public facing operation. Admin api: Spring boot, Hibrernate, Rest API. Build Container image. Kuberenetes: AWS EKS, AWS ECS, Google GKE. Use Jenkins for CI/CD pipeline. Buddy works is good for AWS. Static content: Host on AWS S3 bucket, Use Cloudfront or Cloudflare as CDN.

Serverless Solution: Api gateway Lambda, Serveless Aurora (SQL). AWS S3 bucket.

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Decisions about AWS Lambda and Serverless
Cory Bell

Netlfiy Functions uses AWS Lambda under the hood, but Netlify adds some nice sugar. The biggest advantage is the local development experience with netlify-cli. This allows you to run your functions locally with local configuration or pull configs from the Netlify dashboard. I built a health-check endpoint in about 2 minutes, and my send-email function in less than an hour.

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When adding a new feature to Checkly rearchitecting some older piece, I tend to pick Heroku for rolling it out. But not always, because sometimes I pick AWS Lambda . The short story:

  • Developer Experience trumps everything.
  • AWS Lambda is cheap. Up to a limit though. This impact not only your wallet.
  • If you need geographic spread, AWS is lonely at the top.
The setup

Recently, I was doing a brainstorm at a startup here in Berlin on the future of their infrastructure. They were ready to move on from their initial, almost 100% Ec2 + Chef based setup. Everything was on the table. But we crossed out a lot quite quickly:

  • Pure, uncut, self hosted Kubernetes — way too much complexity
  • Managed Kubernetes in various flavors — still too much complexity
  • Zeit — Maybe, but no Docker support
  • Elastic Beanstalk — Maybe, bit old but does the job
  • Heroku
  • Lambda

It became clear a mix of PaaS and FaaS was the way to go. What a surprise! That is exactly what I use for Checkly! But when do you pick which model?

I chopped that question up into the following categories:

  • Developer Experience / DX 🤓
  • Ops Experience / OX 🐂 (?)
  • Cost 💵
  • Lock in 🔐

Read the full post linked below for all details

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Pros of AWS Lambda
Pros of Serverless
  • 129
    No infrastructure
  • 83
  • 70
  • 59
  • 47
    No deploy, no server, great sleep
  • 12
    AWS Lambda went down taking many sites with it
  • 6
    Event Driven Governance
  • 6
    Extensive API
  • 6
    Auto scale and cost effective
  • 6
    Easy to deploy
  • 5
    VPC Support
  • 3
    Integrated with various AWS services
  • 14
    API integration
  • 7
    Supports cloud functions for Google, Azure, and IBM
  • 3
    Lower cost
  • 1
    Auto scale
  • 1

Sign up to add or upvote prosMake informed product decisions

Cons of AWS Lambda
Cons of Serverless
  • 7
    Cant execute ruby or go
  • 3
    Compute time limited
  • 1
    Can't execute PHP w/o significant effort
    Be the first to leave a con

    Sign up to add or upvote consMake informed product decisions

    - No public GitHub repository available -

    What is AWS Lambda?

    AWS Lambda is a compute service that runs your code in response to events and automatically manages the underlying compute resources for you. You can use AWS Lambda to extend other AWS services with custom logic, or create your own back-end services that operate at AWS scale, performance, and security.

    What is Serverless?

    Build applications comprised of microservices that run in response to events, auto-scale for you, and only charge you when they run. This lowers the total cost of maintaining your apps, enabling you to build more logic, faster. The Framework uses new event-driven compute services, like AWS Lambda, Google CloudFunctions, and more.

    Need advice about which tool to choose?Ask the StackShare community!

    What companies use AWS Lambda?
    What companies use Serverless?
    See which teams inside your own company are using AWS Lambda or Serverless.
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    What tools integrate with AWS Lambda?
    What tools integrate with Serverless?

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    What are some alternatives to AWS Lambda and Serverless?
    Azure Functions
    Azure Functions is an event driven, compute-on-demand experience that extends the existing Azure application platform with capabilities to implement code triggered by events occurring in virtually any Azure or 3rd party service as well as on-premises systems.
    AWS Elastic Beanstalk
    Once you upload your application, Elastic Beanstalk automatically handles the deployment details of capacity provisioning, load balancing, auto-scaling, and application health monitoring.
    AWS Step Functions
    AWS Step Functions makes it easy to coordinate the components of distributed applications and microservices using visual workflows. Building applications from individual components that each perform a discrete function lets you scale and change applications quickly.
    Google App Engine
    Google has a reputation for highly reliable, high performance infrastructure. With App Engine you can take advantage of the 10 years of knowledge Google has in running massively scalable, performance driven systems. App Engine applications are easy to build, easy to maintain, and easy to scale as your traffic and data storage needs grow.
    AWS Batch
    It enables developers, scientists, and engineers to easily and efficiently run hundreds of thousands of batch computing jobs on AWS. It dynamically provisions the optimal quantity and type of compute resources (e.g., CPU or memory optimized instances) based on the volume and specific resource requirements of the batch jobs submitted.
    See all alternatives