Google Cloud Run vs Knative

Overview

Knative

Stacks86

Followers342

Votes21

GitHub Stars5.9K

Forks1.2K

Google Cloud Run

Stacks290

Followers243

Votes62

Google Cloud Run vs Knative: What are the differences?

Introduction: Google Cloud Run and Knative are both popular serverless platforms that allow developers to run containerized workloads on a cloud infrastructure. While both platforms offer similar functionalities, there are key differences that set them apart.

Deployer integration: One of the key differences between Google Cloud Run and Knative is the level of integration with the deployment tools. Google Cloud Run is tightly integrated with Google Cloud Build, providing a streamlined deployment process. On the other hand, Knative is more flexible and can integrate with various deployment tools, allowing developers to choose the most suitable one for their workflow.
Vendor lock-in: Another difference between Google Cloud Run and Knative is the level of vendor lock-in. Google Cloud Run is a fully managed service provided by Google Cloud, which means that developers may be tied to the Google Cloud ecosystem. Knative, on the other hand, is an open-source project that can be run on any Kubernetes cluster, offering more freedom and flexibility for developers who prefer to avoid vendor lock-in.
Auto-scaling mechanism: Google Cloud Run and Knative also differ in their auto-scaling mechanisms. Google Cloud Run uses a request-based scaling mechanism, meaning it scales based on the number of incoming requests. Knative, on the other hand, offers more advanced auto-scaling features, such as pod-based scaling, which allows it to scale based on the number of pods running concurrently. This can be particularly useful for workloads with long-lived connections or high resource utilization.
Eventing capabilities: Knative offers built-in eventing capabilities, allowing developers to easily build event-driven applications. It provides event sources, brokers, and triggers to simplify the eventing workflow. Google Cloud Run, on the other hand, does not offer native eventing capabilities but can be integrated with other services, such as Cloud Pub/Sub, to enable event-driven architectures.
Pricing structure: The pricing structure for Google Cloud Run and Knative also differs. Google Cloud Run follows a pricing model based on the number of invocations and the amount of resources used. Knative, being an open-source project, does not have its own pricing structure but may incur costs based on the underlying cloud provider's pricing for Kubernetes cluster usage.
Managed service vs self-managed: Lastly, Google Cloud Run is a fully managed serverless platform, meaning that the infrastructure and underlying resources are managed by Google. Knative, on the other hand, is an open-source project that requires self-management and deployment on a Kubernetes cluster. This allows for more customization and control but also adds the responsibility of managing the infrastructure.

In summary, Google Cloud Run offers a tightly integrated and fully managed serverless platform with a request-based scaling mechanism, while Knative provides a more flexible, open-source solution with pod-based scaling and built-in eventing capabilities. The choice between the two depends on factors such as vendor lock-in preference, deployment tool integration, and the need for advanced eventing features.

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Advice on Knative, Google Cloud Run

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.

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Detailed Comparison

Knative	Google Cloud Run
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	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.
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	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
GitHub Stars 5.9K	GitHub Stars -
GitHub Forks 1.2K	GitHub Forks -
Stacks 86	Stacks 290
Followers 342	Followers 243
Votes 21	Votes 62
Pros & Cons
Pros 5 Portability 4 Autoscaling 3 Eventing 3 Open source 3 On top of Kubernetes	Pros 11 HTTPS endpoints 10 Fully managed 10 Pay per use 7 Deploy containers 7 Concurrency: multiple requests sent to each container
Integrations
Google Kubernetes Engine	Google Kubernetes Engine Google Cloud Build Docker

What are some alternatives to Knative, Google Cloud Run?

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 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

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

Construct applications from bite-sized business logic billed to the nearest 100 milliseconds, only while your code is running

OpenFaaS

Serverless Functions Made Simple for Docker and Kubernetes

Nuclio

nuclio is portable across IoT devices, laptops, on-premises datacenters and cloud deployments, eliminating cloud lock-ins and enabling hybrid solutions.

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 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

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

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.

Related Comparisons

Google Cloud Run vs Knative: What are the differences?

Deployer integration: One of the key differences between Google Cloud Run and Knative is the level of integration with the deployment tools. Google Cloud Run is tightly integrated with Google Cloud Build, providing a streamlined deployment process. On the other hand, Knative is more flexible and can integrate with various deployment tools, allowing developers to choose the most suitable one for their workflow.
Vendor lock-in: Another difference between Google Cloud Run and Knative is the level of vendor lock-in. Google Cloud Run is a fully managed service provided by Google Cloud, which means that developers may be tied to the Google Cloud ecosystem. Knative, on the other hand, is an open-source project that can be run on any Kubernetes cluster, offering more freedom and flexibility for developers who prefer to avoid vendor lock-in.
Auto-scaling mechanism: Google Cloud Run and Knative also differ in their auto-scaling mechanisms. Google Cloud Run uses a request-based scaling mechanism, meaning it scales based on the number of incoming requests. Knative, on the other hand, offers more advanced auto-scaling features, such as pod-based scaling, which allows it to scale based on the number of pods running concurrently. This can be particularly useful for workloads with long-lived connections or high resource utilization.
Eventing capabilities: Knative offers built-in eventing capabilities, allowing developers to easily build event-driven applications. It provides event sources, brokers, and triggers to simplify the eventing workflow. Google Cloud Run, on the other hand, does not offer native eventing capabilities but can be integrated with other services, such as Cloud Pub/Sub, to enable event-driven architectures.
Pricing structure: The pricing structure for Google Cloud Run and Knative also differs. Google Cloud Run follows a pricing model based on the number of invocations and the amount of resources used. Knative, being an open-source project, does not have its own pricing structure but may incur costs based on the underlying cloud provider's pricing for Kubernetes cluster usage.
Managed service vs self-managed: Lastly, Google Cloud Run is a fully managed serverless platform, meaning that the infrastructure and underlying resources are managed by Google. Knative, on the other hand, is an open-source project that requires self-management and deployment on a Kubernetes cluster. This allows for more customization and control but also adds the responsibility of managing the infrastructure.

Google Cloud Run vs Knative

Overview