Optimizing Python for high-load services can achieve performance comparable to Go, as shown in a project handling 100,000 requests per second. Using tools like Granian and strategies such as caching and load testing, Python maintained scalability and cost efficiency, allowing the client to stay within their existing stack while avoiding more expensive languages like Go or Rust.
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