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

Knative vs OpenFaaS

OverviewComparisonAlternatives

Overview

Knative
Knative
Stacks86
Followers342
Votes21
GitHub Stars5.9K
Forks1.2K
OpenFaaS
OpenFaaS
Stacks54
Followers234
Votes17
GitHub Stars26.0K
Forks2.0K

Knative vs OpenFaaS: What are the differences?

Knative and OpenFaaS are two popular frameworks in the serverless computing space. Let's explore the key differences between them.

  1. Programming Language Support: Knative supports multiple programming languages, including Java, Node.js, Go, PHP, and Ruby. On the other hand, OpenFaaS primarily focuses on supporting Docker containers, allowing developers to use any programming language or framework.

  2. Runtime Environment: Knative is built on Kubernetes and leverages its orchestration capabilities to manage scalable serverless workloads. OpenFaaS can also be deployed on Kubernetes but also offers support for alternative runtimes like Docker Swarm, Nomad, and even standalone mode without any orchestration layer.

  3. Scaling Mechanism: Knative provides auto-scaling capabilities out-of-the-box, automatically scaling up or down based on incoming requests or defined metrics. OpenFaaS, on the other hand, relies on the underlying container orchestrator's scaling mechanisms, requiring manual configuration for scaling.

  4. Ecosystem and Community: Knative has gained significant adoption and is backed by major industry players like Google, IBM, and Red Hat. It benefits from a larger ecosystem and a supportive community, with a growing number of contributors and integrations. OpenFaaS also has a strong community following and contributions, but it may have a smaller ecosystem compared to Knative.

  5. Event-Driven Capabilities: Knative provides native support for event-driven architectures, allowing developers to easily build and run event-driven applications. OpenFaaS, while capable of handling events, does not have the same level of native support for event-driven architectures.

  6. Resource Utilization: Knative optimizes resource utilization by scaling to zero when there are no incoming requests, reducing costs and improving efficiency. OpenFaaS, by default, keeps replicas running even during idle periods, which may result in higher resource consumption and costs.

In summary, Knative offers broader programming language support, leverages Kubernetes for orchestration, provides extensive auto-scaling and event-driven capabilities, and enjoys a larger ecosystem and community support. OpenFaaS, on the other hand, has a flexible runtime environment, supports any programming language through Docker containers, and may be a better fit for existing container orchestration setups.

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Detailed Comparison

Knative
Knative
OpenFaaS
OpenFaaS

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

Serverless Functions Made Simple for Docker and Kubernetes

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
5.9K
GitHub Stars
26.0K
GitHub Forks
1.2K
GitHub Forks
2.0K
Stacks
86
Stacks
54
Followers
342
Followers
234
Votes
21
Votes
17
Pros & Cons
Pros
  • 5
    Portability
  • 4
    Autoscaling
  • 3
    Secure Eventing
  • 3
    Open source
  • 3
    Eventing
Pros
  • 5
    Open source
  • 4
    Ease
  • 3
    Autoscaling
  • 2
    Documentation
  • 2
    Community
Integrations
Google Kubernetes Engine
Google Kubernetes Engine
Kubernetes
Kubernetes
Docker
Docker

What are some alternatives to Knative, OpenFaaS?

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

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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