TriggerMesh clears way into the multi-cloud future

TriggerMesh clears way into the multi-cloud future

Serverless management platform provider TriggerMesh just released its Knative Lambda Runtimes (KLR) into the Kubernetes ecosystem, aiming at making AWS Lambda users just a tad more flexible.

KLR, or clear as they like to pronounce it, are templates built with Knative, which are supposed to let users run an AWS Lambda function in a Kubernetes cluster installed with Knative without the need of any changes. The project takes inspiration from serverless continuous integration project LambCI, which includes a replicate of the AWS Lambda environment. It also makes use of the Lambda runtime API AWS offers and of course the Knative project Google introduced in July 2018 along with partners Pivotal, Red Hat, IBM, and SAP.

Similarities to other serverless projects in the Kubernetes ecosystem – OpenFaaS, Fission and kubeless to name a few prominent examples – can’t be denied. Its makers however wanted to make a distinction by aiming Knative at companies that could use the infrastructure project to create products that need ways to build, deploy, and manage modern serverless workloads – not mere end-users – with the ultimate goal of more portable workloads for all.

Since vendor lock-in is a topic anyone looking into switching to serverless has to consider, the option of porting Lambda functions that KLR provides seems quite interesting. But not just that, with growing numbers of developers making use of several providers in projects any step into the direction of better communication between cloud offerings should be looked into.

However KLR is – like TriggerMesh itself – only at the beginning. In the upcoming months the execution environment is supposed to be supplemented by triggers for event routing, so that communication with functions is possible. We’ll see how that all works out.


If you’d like to know more about Knative, here’s a recording from the inaugeral Serverless Computing London conference back in November 2018: