Category: Software, Security, Data, Kubernetes, Architecture

Case in point: Replacement tires are $250 apiece on many models, and you have to buy them from the dealer.

So if you are going to invest a ton of time, effort and engineering hours in a service mesh and a Kubernetes rollout, why would you want to buy the equivalent of cheap tires – in this case, a newer and minimally tested data plane written in a language that may not even have been designed to handle wire-speed application traffic?

To get a real sense of how a data plane will perform in your specific environment, you need to test it at scale, and you need to talk to other people who have used it in production.

Understanding how easy it is to add integrations and whether the company that supports the data plane or the community is innovating on integrations at a rapid pace is a good piece of data for calculating the ability of a data plane to continue to meet your needs in the future.

Putting the right data plane on your service mesh will give you the BMW-like performance you need and a service mesh that is snappy, responsive, reliable and road-worthy in all conditions.

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