StackShareStackShare
Follow on
StackShare

Discover and share technology stacks from companies around the world.

Follow on

© 2025 StackShare. All rights reserved.

Product

  • Stacks
  • Tools
  • Feed

Company

  • About
  • Contact

Legal

  • Privacy Policy
  • Terms of Service
  1. Stackups
  2. Application & Data
  3. Serverless
  4. Serverless Task Processing
  5. Google Cloud Run vs Kubeless

Google Cloud Run vs Kubeless

OverviewDecisionsComparisonAlternatives

Overview

Kubeless
Kubeless
Stacks39
Followers195
Votes0
Google Cloud Run
Google Cloud Run
Stacks290
Followers243
Votes62

Google Cloud Run vs Kubeless: What are the differences?

# Introduction
In this comparison, we will highlight the key differences between Google Cloud Run and Kubeless.

1. **Execution Environment**: Google Cloud Run utilizes container images to run applications, while Kubeless allows users to run serverless functions.
2. **Scalability**: Cloud Run automatically scales the number of containers based on incoming traffic, whereas Kubeless requires manual configuration for scaling.
3. **Billing Model**: Google Cloud Run bills per second with a minimum usage of 100 milliseconds, while Kubeless allows for precise billing based on function execution time.
4. **Infrastructure Management**: With Google Cloud Run, Google manages the infrastructure, while Kubeless requires the management of Kubernetes clusters.
5. **Supported Languages**: Kubeless supports multiple languages like Python, Node.js, etc., while Google Cloud Run has wider language support due to containerization.
6. **Maintenance**: Google Cloud Run abstracts away infrastructure maintenance tasks, providing a more serverless experience, whereas Kubeless requires more direct management due to its Kubernetes nature.

In Summary, Google Cloud Run and Kubeless differ in terms of their execution environment, scalability, billing model, infrastructure management, supported languages, and maintenance requirements, offering users varied options for deploying their applications and functions.

Share your Stack

Help developers discover the tools you use. Get visibility for your team's tech choices and contribute to the community's knowledge.

View Docs
CLI (Node.js)
or
Manual

Advice on Kubeless, Google Cloud Run

Clifford
Clifford

Software Engineer at Bidvest Advisory Services

Mar 28, 2020

Decided

Run cloud service containers instead of cloud-native services

  • Running containers means that your microservices are not "cooked" into a cloud provider's architecture.
  • Moving from one cloud to the next means that you simply spin up new instances of your containers in the new cloud using that cloud's container service.
  • Start redirecting your traffic to the new resources.
  • Turn off the containers in the cloud you migrated from.
71.3k views71.3k
Comments

Detailed Comparison

Kubeless
Kubeless
Google Cloud Run
Google Cloud Run

Kubeless is a Kubernetes native serverless Framework. Kubeless supports both HTTP and event based functions triggers. It has a serverless plugin, a graphical user interface and multiple runtimes, including Python and Node.js.

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.

-
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
Statistics
Stacks
39
Stacks
290
Followers
195
Followers
243
Votes
0
Votes
62
Pros & Cons
No community feedback yet
Pros
  • 11
    HTTPS endpoints
  • 10
    Pay per use
  • 10
    Fully managed
  • 7
    Concurrency: multiple requests sent to each container
  • 7
    Serverless
Integrations
Docker
Docker
Kafka
Kafka
Zookeeper
Zookeeper
Serverless
Serverless
Kubernetes
Kubernetes
Google Kubernetes Engine
Google Kubernetes Engine
Google Cloud Build
Google Cloud Build
Docker
Docker
Knative
Knative

What are some alternatives to Kubeless, Google Cloud Run?

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.

Related Comparisons

Bootstrap
Materialize

Bootstrap vs Materialize

Laravel
Django

Django vs Laravel vs Node.js

Bootstrap
Foundation

Bootstrap vs Foundation vs Material UI

Node.js
Spring Boot

Node.js vs Spring-Boot

Liquibase
Flyway

Flyway vs Liquibase