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  1. Stackups
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  3. Serverless
  4. Serverless Task Processing
  5. Knative vs Kubeless vs Serverless

Knative vs Kubeless vs Serverless

OverviewDecisionsComparisonAlternatives

Overview

Serverless
Serverless
Stacks2.2K
Followers1.2K
Votes28
GitHub Stars46.9K
Forks5.7K
Kubeless
Kubeless
Stacks39
Followers195
Votes0
Knative
Knative
Stacks86
Followers342
Votes21
GitHub Stars5.9K
Forks1.2K

Knative vs Kubeless vs Serverless: What are the differences?

Key differences between Knative, Kubeless, and Serverless

Knative, Kubeless, and Serverless are popular tools in the domain of serverless computing. Despite some similarities, they have key differences. Here are six essential differences between these platforms:

  1. Deployment: Knative allows developers to deploy both containerized and non-containerized workloads, offering more deployment flexibility. Kubeless, on the other hand, is specifically designed for deploying functions to Kubernetes clusters. Serverless frameworks, like AWS Lambda or Azure Functions, are typically suited for hosting functions as a service in cloud environments.

  2. Scaling: Knative offers a more advanced scaling mechanism compared to Kubeless and Serverless frameworks. It automatically scales containers based on incoming request traffic and utilizes Kubernetes' Horizontal Pod Autoscaler. Kubeless has built-in scaling capabilities for function-based workloads, while traditional Serverless frameworks rely on the provider's scaling infrastructure.

  3. Eventing: Knative provides built-in eventing support, allowing developers to easily create and consume events within their applications. Kubeless has limited support for event-driven architectures, usually relying on Kubernetes events or additional eventbus integrations. Serverless frameworks provide event triggers to execute functions in response to specific events, making them suitable for event-driven scenarios.

  4. Language Support: Knative has broader language support, allowing developers to write functions in various languages and runtimes. Kubeless also supports multiple languages, but the available runtimes may be more limited. Serverless frameworks often have a vast array of supported languages, depending on the provider's offering.

  5. Monitoring and Observability: Knative offers comprehensive monitoring and observability features, including integration with popular observability tools like Prometheus and Grafana. Kubeless provides basic monitoring capabilities, while Serverless frameworks usually have their own monitoring and logging solutions, like AWS CloudWatch or Azure Monitor.

  6. Portability: Knative, being built on top of Kubernetes, offers better portability across different environments. Kubeless is tightly integrated with Kubernetes, which provides portability within Kubernetes clusters. Serverless frameworks are tied to specific cloud providers, making it harder to switch between platforms.

In summary, Knative provides more deployment flexibility, advanced scaling mechanisms, built-in eventing, broader language support, comprehensive monitoring, and better portability compared to Kubeless and traditional Serverless frameworks.

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Advice on Serverless, Kubeless, Knative

Tim
Tim

CTO at Checkly Inc.

Sep 18, 2019

Needs adviceonHerokuHerokuAWS LambdaAWS Lambda

When adding a new feature to Checkly rearchitecting some older piece, I tend to pick Heroku for rolling it out. But not always, because sometimes I pick AWS Lambda . The short story:

  • Developer Experience trumps everything.
  • AWS Lambda is cheap. Up to a limit though. This impact not only your wallet.
  • If you need geographic spread, AWS is lonely at the top.

The setup

Recently, I was doing a brainstorm at a startup here in Berlin on the future of their infrastructure. They were ready to move on from their initial, almost 100% Ec2 + Chef based setup. Everything was on the table. But we crossed out a lot quite quickly:

  • Pure, uncut, self hosted Kubernetes — way too much complexity
  • Managed Kubernetes in various flavors — still too much complexity
  • Zeit — Maybe, but no Docker support
  • Elastic Beanstalk — Maybe, bit old but does the job
  • Heroku
  • Lambda

It became clear a mix of PaaS and FaaS was the way to go. What a surprise! That is exactly what I use for Checkly! But when do you pick which model?

I chopped that question up into the following categories:

  • Developer Experience / DX 🤓
  • Ops Experience / OX 🐂 (?)
  • Cost 💵
  • Lock in 🔐

Read the full post linked below for all details

357k views357k
Comments

Detailed Comparison

Serverless
Serverless
Kubeless
Kubeless
Knative
Knative

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.

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
46.9K
GitHub Stars
-
GitHub Stars
5.9K
GitHub Forks
5.7K
GitHub Forks
-
GitHub Forks
1.2K
Stacks
2.2K
Stacks
39
Stacks
86
Followers
1.2K
Followers
195
Followers
342
Votes
28
Votes
0
Votes
21
Pros & Cons
Pros
  • 14
    API integration
  • 7
    Supports cloud functions for Google, Azure, and IBM
  • 3
    Lower cost
  • 1
    3. Simplified Management for developers to focus on cod
  • 1
    Openwhisk
No community feedback yet
Pros
  • 5
    Portability
  • 4
    Autoscaling
  • 3
    Open source
  • 3
    Eventing
  • 3
    On top of Kubernetes
Integrations
Azure Functions
Azure Functions
AWS Lambda
AWS Lambda
Amazon API Gateway
Amazon API Gateway
Docker
Docker
Kafka
Kafka
Zookeeper
Zookeeper
Kubernetes
Kubernetes
Google Kubernetes Engine
Google Kubernetes Engine

What are some alternatives to Serverless, 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.

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.

Fission

Fission

Write short-lived functions in any language, and map them to HTTP requests (or other event triggers). Deploy functions instantly with one command. There are no containers to build, and no Docker registries to manage.

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