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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:
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.
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.
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.
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.
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.
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.
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.
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
Pros of Knative
- Portability5
- Autoscaling4
- Open source3
- Eventing3
- Secure Eventing3
- On top of Kubernetes3
Pros of Kubeless
Pros of Serverless
- API integration14
- Supports cloud functions for Google, Azure, and IBM7
- Lower cost3
- Auto scale1
- Openwhisk1