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

Knative vs Napkin

OverviewComparisonAlternatives

Overview

Knative
Knative
Stacks86
Followers342
Votes21
GitHub Stars5.9K
Forks1.2K
Napkin
Napkin
Stacks3
Followers9
Votes0

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Detailed Comparison

Knative
Knative
Napkin
Napkin

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

It is the next-generation production-ready backend development tool in your browser. Build production-ready API endpoints. No infra. No boilerplate. Just code.

Serving - Scale to zero, request-driven compute model; Build - Cloud-native source to container orchestration; Events - Universal subscription, delivery and management of events; Serverless add-on on GKE - Enable GCP managed serverless stack on Kubernetes
Zero configuration required; Automatic HTTPS. Private by default; Your backend is guaranteed to grow without interruption; Run and view your code response immediately; Test, lint, and view code errors right in the browser; Your code is automatically saved and backed up in the cloud
Statistics
GitHub Stars
5.9K
GitHub Stars
-
GitHub Forks
1.2K
GitHub Forks
-
Stacks
86
Stacks
3
Followers
342
Followers
9
Votes
21
Votes
0
Pros & Cons
Pros
  • 5
    Portability
  • 4
    Autoscaling
  • 3
    On top of Kubernetes
  • 3
    Open source
  • 3
    Eventing
No community feedback yet
Integrations
Google Kubernetes Engine
Google Kubernetes Engine
Python
Python
JavaScript
JavaScript

What are some alternatives to Knative, Napkin?

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

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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