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  1. Stackups
  2. Application & Data
  3. Serverless
  4. Serverless Task Processing
  5. Architect vs Serverless

Architect vs Serverless

OverviewDecisionsComparisonAlternatives

Overview

Serverless
Serverless
Stacks2.2K
Followers1.2K
Votes28
GitHub Stars46.9K
Forks5.7K
Architect
Architect
Stacks36
Followers87
Votes0

Architect vs Serverless: What are the differences?

Key Differences between Architect and Serverless

Architect and Serverless are both frameworks that are used to build applications on the cloud. However, they have some key differences that set them apart.

  1. Architecture: The first major difference between Architect and Serverless is their architectural approach. Architect follows a monolithic architecture, where the application is divided into components or modules, whereas Serverless follows a microservices architecture, where the application is divided into smaller, independent services.

  2. Deployment: Another key difference is in the deployment process. Architect uses a traditional deployment approach, where the application is deployed as a whole, while Serverless uses a serverless deployment approach, where each service is deployed individually, allowing for faster and more efficient updates and scaling.

  3. Scaling: Scalability is an important aspect of cloud applications. Architect provides a more granular approach to scaling, allowing for scaling at different levels, such as module or component level. On the other hand, Serverless offers automatic scaling, where services can scale based on demand without the need for manual intervention.

  4. Vendor Lock-in: Vendor lock-in is a concern for many developers. Architect is a framework that is provider-agnostic, meaning it can be used with different cloud providers, while Serverless is tightly integrated with specific cloud providers, such as AWS Lambda or Azure Functions.

  5. Cost: Cost is another differentiating factor. Architect follows a pay-as-you-go model, where you only pay for the resources you use, while Serverless also follows a pay-as-you-go model, but with more fine-grained billing, as you are billed for individual service invocations rather than the entire application.

  6. Community and Support: Lastly, the community and support around these frameworks differ. Architect has a smaller community compared to Serverless, but it has a dedicated team maintaining and supporting the framework. Serverless, on the other hand, has a larger and more active community, as well as official support from cloud providers.

In summary, Architect and Serverless differ in their architectural approach, deployment process, scaling capabilities, vendor lock-in, cost model, and community support.

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Advice on Serverless, Architect

Tim
Tim

CTO at Checkly Inc.

Sep 18, 2019

Needs adviceonHerokuHerokuAWS LambdaAWS Lambda

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

357k views357k
Comments

Detailed Comparison

Serverless
Serverless
Architect
Architect

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.

Create, deploy, and maintain next-generation AWS cloud function-based serverless infrastructure with full local, offline workflows, and more.

-
Version control your architecture and create cloud infra in minutes from an .arc manifest; Deploy in seconds with first class support for staging and production; Work locally while completely offline with a speedy in-memory database; Primitives not Frameworks; define app architecture agnostic of vendor arcana
Statistics
GitHub Stars
46.9K
GitHub Stars
-
GitHub Forks
5.7K
GitHub Forks
-
Stacks
2.2K
Stacks
36
Followers
1.2K
Followers
87
Votes
28
Votes
0
Pros & Cons
Pros
  • 14
    API integration
  • 7
    Supports cloud functions for Google, Azure, and IBM
  • 3
    Lower cost
  • 1
    Auto scale
  • 1
    Openwhisk
No community feedback yet
Integrations
Azure Functions
Azure Functions
AWS Lambda
AWS Lambda
Amazon API Gateway
Amazon API Gateway
Amazon DynamoDB
Amazon DynamoDB
Amazon SQS
Amazon SQS
Amazon CloudWatch
Amazon CloudWatch
AWS Lambda
AWS Lambda
Amazon Route 53
Amazon Route 53
Amazon S3
Amazon S3
Amazon API Gateway
Amazon API Gateway
Amazon SNS
Amazon SNS

What are some alternatives to Serverless, Architect?

AWS Lambda

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.

Azure Functions

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.

Google Cloud Run

Google Cloud Run

A managed compute platform that enables you to run stateless containers that are invocable via HTTP requests. It's serverless by abstracting away all infrastructure management.

Google Cloud Functions

Google Cloud Functions

Construct applications from bite-sized business logic billed to the nearest 100 milliseconds, only while your code is running

Knative

Knative

Knative provides a set of middleware components that are essential to build modern, source-centric, and container-based applications that can run anywhere: on premises, in the cloud, or even in a third-party data center

OpenFaaS

OpenFaaS

Serverless Functions Made Simple for Docker and Kubernetes

Nuclio

Nuclio

nuclio is portable across IoT devices, laptops, on-premises datacenters and cloud deployments, eliminating cloud lock-ins and enabling hybrid solutions.

Apache OpenWhisk

Apache OpenWhisk

OpenWhisk is an open source serverless platform. It is enterprise grade and accessible to all developers thanks to its superior programming model and tooling. It powers IBM Cloud Functions, Adobe I/O Runtime, Naver, Nimbella among others.

Cloud Functions for Firebase

Cloud Functions for Firebase

Cloud Functions for Firebase lets you create functions that are triggered by Firebase products, such as changes to data in the Realtime Database, uploads to Cloud Storage, new user sign ups via Authentication, and conversion events in Analytics.

AWS Batch

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.

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