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  1. Stackups
  2. Application & Data
  3. Serverless
  4. Serverless Task Processing
  5. Laravel Vapor vs OpenFaaS

Laravel Vapor vs OpenFaaS

OverviewComparisonAlternatives

Overview

OpenFaaS
OpenFaaS
Stacks54
Followers234
Votes17
GitHub Stars26.0K
Forks2.0K
Laravel Vapor
Laravel Vapor
Stacks45
Followers48
Votes0

Laravel Vapor vs OpenFaaS: What are the differences?

  1. Scalability: Laravel Vapor is a serverless platform that automatically scales based on the demands of the application, while OpenFaaS allows users to scale their functions on-demand but does not offer automatic scaling capabilities.

  2. Integration with Laravel: Laravel Vapor is specifically built for Laravel applications, providing seamless integration with Laravel features, whereas OpenFaaS is a more general-purpose serverless platform that can be used with various programming languages and frameworks.

  3. Maintenance: Laravel Vapor handles all the server management, maintenance, and upgrades automatically, reducing the overall workload for developers, whereas with OpenFaaS, developers are responsible for managing and maintaining the server infrastructure.

  4. Deployment Process: Laravel Vapor simplifies the deployment process through a streamlined interface and a focus on Laravel-specific features, making it easier for developers to deploy and manage their applications, in contrast, OpenFaaS offers a more customizable deployment process that allows for more control over the environment.

  5. Cost Structure: Laravel Vapor operates on a pay-as-you-go pricing model, where users only pay for the resources they consume, while OpenFaaS can be deployed on any infrastructure, allowing for more flexibility in terms of cost management.

  6. Community Support: Laravel Vapor benefits from the Laravel community's extensive support and resources, providing users with a wealth of knowledge and tools to leverage, whereas OpenFaaS has a dedicated community but may not offer the same level of resources and support as Laravel Vapor due to its niche focus.

In Summary, Laravel Vapor offers automatic scalability, seamless integration with Laravel, simplified maintenance, deployment process, pay-as-you-go pricing, and extensive community support, while OpenFaaS provides more customization options, broader language support, and flexibility in deployment infrastructure.

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Detailed Comparison

OpenFaaS
OpenFaaS
Laravel Vapor
Laravel Vapor

Serverless Functions Made Simple for Docker and Kubernetes

It is an auto-scaling, serverless deployment platform for Laravel, powered by AWS Lambda. Manage your Laravel infrastructure on Vapor and fall in love with the scalability and simplicity of serverless.

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Auto-scaling web / queue infrastructure fine tuned for Laravel; Zero-downtime deployments and rollbacks; Environment variable / secret management; Database management, including point-in-time restores and scaling; Redis Cache management, including cluster scaling; Database and cache tunnels, allowing for easy local inspection; Automatic uploading of assets to Cloudfront CDN during deployment; Unique, Vapor assigned vanity URLs for each environment, allowing immediate inspection; Custom application domains; DNS management; Certificate management and renewal; Application, database, and cache metrics; CI friendly
Statistics
GitHub Stars
26.0K
GitHub Stars
-
GitHub Forks
2.0K
GitHub Forks
-
Stacks
54
Stacks
45
Followers
234
Followers
48
Votes
17
Votes
0
Pros & Cons
Pros
  • 5
    Open source
  • 4
    Ease
  • 3
    Autoscaling
  • 2
    Documentation
  • 2
    Community
No community feedback yet
Integrations
Kubernetes
Kubernetes
Docker
Docker
AWS Lambda
AWS Lambda
Laravel
Laravel

What are some alternatives to OpenFaaS, Laravel Vapor?

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.

Serverless

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.

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

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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