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  1. Stackups
  2. Application & Data
  3. Serverless
  4. Serverless Task Processing
  5. Cakework vs Google Cloud Run

Cakework vs Google Cloud Run

OverviewComparisonAlternatives

Overview

Google Cloud Run
Google Cloud Run
Stacks290
Followers243
Votes62
Cakework
Cakework
Stacks0
Followers0
Votes0
GitHub Stars116
Forks1

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Detailed Comparison

Google Cloud Run
Google Cloud Run
Cakework
Cakework

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.

It is a set of simple APIs for building serverless directly into your developer product. You describe how you want to package and run your users' code, and use Cakework to build images from Github, CLI, or even a browser.

Simple developer experience; Fast autoscaling; Managed; Any language, any library, any binary; Leverage container workflows and standards; Redundancy; Integrated logging and monitoring; Built on Knative; Custom domains
APIs for building serverless directly into your developer product; You use APIs to run code securely and show logs and metrics back to your users
Statistics
GitHub Stars
-
GitHub Stars
116
GitHub Forks
-
GitHub Forks
1
Stacks
290
Stacks
0
Followers
243
Followers
0
Votes
62
Votes
0
Pros & Cons
Pros
  • 11
    HTTPS endpoints
  • 10
    Pay per use
  • 10
    Fully managed
  • 7
    Concurrency: multiple requests sent to each container
  • 7
    Serverless
No community feedback yet
Integrations
Google Kubernetes Engine
Google Kubernetes Engine
Google Cloud Build
Google Cloud Build
Docker
Docker
Knative
Knative
GitHub
GitHub
Fern
Fern
TypeScript
TypeScript
JSON
JSON
Docker
Docker

What are some alternatives to Google Cloud Run, Cakework?

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.

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

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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