9 karma · joined March 23, 2018
These are all becoming tablestakes, imo for any AI SRE. The real moat is to build the data layer in an efficient manner so that the investigation (and mitigation) is fast and cost-efficient. The models are making it easier for any company at the same time.
I also strongly believe that severless is an overloaded term and everyone understands something different from it. Some people consider this only as a FaaS but I think that it's any cloud service, that can auto-scale to infinity, scales to zero, pay-per-use and managed. I frankly believe Serverless should be regarded as a paradigm rather than a technology advancement. here's a very nice blog about it: https://ben11kehoe.medium.com/serverless-is-a-state-of-mind-...
You can watch it here: https://www.youtube.com/watch?v=bmAw24uquK0
1) Automatic instrumentation is what creates no code change. Once you add Thundra to your environment variables, you can change the monitoring settings with annotation or additional environment variables with no code change for Java. For Node.js and Go, You simply wrap your functions with our agents. With manual instrumentation on the other hand, you can also add code blocks to inspect your variables.
You can check our codes here: https://github.com/thundra-io. We are open to share more, if you want any further questions.
2) Zero overhead is one of other strong points of us. You need to switch to async. monitoring for this. This means you need to add "our" lambda to your environment variables (Please check for it: https://github.com/thundra-io/serverless-plugin-thundra-moni...). Then this lambda sends logs of your function to us. Overhead of sending and retrying for failed logs will not happen. Only overhead can occur because of make your code to gather more logs for our lambda to read. But this is truly negligible.