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

Knative vs Kubeless

OverviewComparisonAlternatives

Overview

Kubeless
Kubeless
Stacks39
Followers195
Votes0
Knative
Knative
Stacks86
Followers342
Votes21
GitHub Stars5.9K
Forks1.2K

Knative vs Kubeless: What are the differences?

Introduction

Knative and Kubeless are both serverless frameworks for running scalable workloads on Kubernetes clusters. While both provide solutions for deploying and managing functions as services, there are several key differences between the two.

  1. Containerization vs. Function Invocation: Knative focuses on the containerization of workloads, enabling developers to package their functions as Docker containers and deploy them on a Kubernetes cluster. On the other hand, Kubeless abstracts away the containerization aspect and allows developers to directly invoke functions without the need for explicit containerization.

  2. Event-driven vs. Request-driven: Knative is primarily designed for event-driven workloads, supporting automatic scaling based on incoming events and enabling seamless integration with event sources. Kubeless, on the other hand, is more request-driven, where functions are invoked through HTTP requests or other transport mechanisms.

  3. Language Support: Knative provides a more extensive language support, allowing developers to write functions in multiple languages such as Java, Node.js, Go, and more. Kubeless also supports multiple languages but has a more limited language support compared to Knative.

  4. Serverless Workflow: Knative provides a serverless workflow layer on top of Kubernetes, allowing developers to define sophisticated workflows and orchestrate the execution of functions. Kubeless focuses more on the function operation itself rather than workflow orchestration.

  5. Community and Maturity: Knative is an open-source project with a vibrant community and is backed by major organizations like Google, Pivotal, and Red Hat. It has gained more traction and maturity compared to Kubeless, which has a smaller community and ecosystem.

  6. Integration with Kubernetes Native Ecosystem: Knative is tightly integrated with the Kubernetes ecosystem and leverages Kubernetes primitives for managing workloads, scaling, and routing. Kubeless also runs on Kubernetes but provides additional abstractions to simplify function deployment and management.

In summary, Knative focuses on containerization, event-driven workloads, supports multiple languages, provides a serverless workflow layer, has a larger community and is tightly integrated with Kubernetes. Kubeless, on the other hand, is more request-driven, has a more limited language support, focuses on function operation rather than workflow orchestration, has a smaller community, and provides additional abstractions for function deployment and management.

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Detailed Comparison

Kubeless
Kubeless
Knative
Knative

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.

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

-
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
Statistics
GitHub Stars
-
GitHub Stars
5.9K
GitHub Forks
-
GitHub Forks
1.2K
Stacks
39
Stacks
86
Followers
195
Followers
342
Votes
0
Votes
21
Pros & Cons
No community feedback yet
Pros
  • 5
    Portability
  • 4
    Autoscaling
  • 3
    On top of Kubernetes
  • 3
    Open source
  • 3
    Eventing
Integrations
Docker
Docker
Kafka
Kafka
Zookeeper
Zookeeper
Serverless
Serverless
Kubernetes
Kubernetes
Google Kubernetes Engine
Google Kubernetes Engine

What are some alternatives to Kubeless, Knative?

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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