Go as a Backend for AI Applications: A Performance Case Study

When developing AI applications in Python, leveraging Go for model serving in the backend can offer significant performance advantages. While Python excels in machine learning model development with its vast ecosystem, Go’s efficiency makes it an ideal choice for serving those models in production.

We conducted a benchmark comparison using hey to demonstrate Go’s superiority in high-performance backend operations. By deploying identical machine learning models using both Python and Go, we measured their response times and throughput.

Why Python for AI Development?

Python is the dominant language for AI development due to its rich libraries, like TensorFlow and scikit-learn, which simplify model training and experimentation. Its extensive ecosystem and simple syntax make rapid development easy, especially for data scientists who need flexibility and easy-to-read code.

However, Python’s interpreted nature results in slower execution and higher memory consumption compared to Go. This becomes especially noticeable when scaling an application to handle many requests, such as in a model-serving scenario.

Why Go for Model Serving?

Go’s strengths lie in its speed, concurrency, and memory efficiency. It’s compiled and optimized for handling many requests at once, making it a powerful choice for production-level model serving. For applications that need to serve ML predictions to thousands of users, Go is an excellent fit due to its lower latency and better scalability.

Simple Example: Serving AI Models

Let’s take a simple machine learning example where we train a model using Python and serve it using both Python and Go. We’ll then compare their performance.

Python API (Flask)

from flask import Flask
app = Flask(__name__)

@app.route('/predict')
def predict():
    # Simple prediction simulation
    return "Prediction: Iris-Setosa"

if __name__ == '__main__':
    app.run(port=5000)

Go API

package main

import (
    "fmt"
    "log"
    "net/http"
)

func predictHandler(w http.ResponseWriter, r *http.Request) {
    fmt.Fprintf(w, "Prediction: Iris-Setosa")
}

func main() {
    http.HandleFunc("/predict", predictHandler)
    log.Fatal(http.ListenAndServe(":8080", nil))
}

Benchmarking with Hey

To compare both APIs, use hey for benchmarking. Here’s how you can run the tests:

# For Python API
hey -n 1000 -c 10 http://localhost:5000/predict
# For Go API
hey -n 1000 -c 10 http://localhost:8080/predict

Benchmarking: Go vs. Python for Model Serving

In our tests, Go handled more requests per second (34,172 vs. Python’s 24,795) and had lower average response times. These results clearly show Go’s efficiency in handling concurrent requests, making it ideal for production model serving in AI applications.

• Python

  • Requests per second: 24,795
  • Average response time: 0.0004 secs
  • Slowest response: 0.0107 secs

• Go

  • Requests per second: 34,172
  • Average response time: 0.0003 secs
  • Slowest response: 0.0075 secs

As these numbers show, Go outperforms Python in terms of both requests per second and response times. In high-concurrency environments, the ability to handle thousands of requests with minimal latency is critical, and Go’s concurrent processing model makes it a clear winner for the backend. The improvement in throughput (over 30% more requests per second) highlights Go’s advantage when scaling applications.

Conclusion

While Python remains the best language for AI development due to its powerful libraries, Go outperforms it in production environments for serving machine learning models. Using Go for model serving can lead to faster response times and higher scalability, making it a compelling choice for backend operations in AI-powered applications.