Comparing two time series data to find deviations between them This is a use case that I have and I am trying to automate this. Any pointers would be helpful.

Use Case:

When we deploy any new version of a web service, we keep monitoring it (while deploying to live) to make sure it is not introducing any new errors. For this, we just visually compare with last weeks trend of errors (within the same time frame) and if they look similar, we approve the new build, or if the number of errors seems to increase, we decide to rollback.

What I am looking for is to automate this decision making. Basically, compare the error data during the push with last weeks (or any other time frame) and decide if the two error trends are similar and to what degree they are similar.

The data that I have is
x axis -> Time stamp
y axis -> # of errors at that time stamp.

I also have details such as number of requests at that timestamp, latency, etc
Your use-case looks solvable using dynamic time warping. DTW is an algorithm for comparing similarity between two temporal sequences. 

Implementation of DTW are available in standard data-science programming language via libraries like this R implementation.