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Table of Contents
Why Golang Uses Less Memory
What Makes Python Use More Memory
Realistic Comparison Setup
When Python’s Higher Footprint Might Not Matter
Home Backend Development Golang Memory Footprint Comparison: Running Identical Web Service Workloads in Golang and Python

Memory Footprint Comparison: Running Identical Web Service Workloads in Golang and Python

Jul 03, 2025 am 02:32 AM
python golang

Go uses significantly less memory than Python when running web services due to language design and concurrency model differences.1. Go's goroutines are lightweight with minimal stack overhead, allowing efficient handling of thousands of connections.2. Its garbage collector is optimized for low latency, reducing memory management overhead.3. Static binaries eliminate interpreter layers, further minimizing memory usage. In contrast, Python's threads or coroutines carry more overhead, dynamic typing increases object memory footprint, and the GIL often requires multiple processes, multiplying memory consumption. Under high concurrency, a Go service might use 30–50MB compared to Python’s 100–200MB. However, Python’s productivity benefits may outweigh its higher memory usage for smaller-scale applications or I/O-bound workloads where developer efficiency is critical.

Memory Footprint Comparison: Running Identical Web Service Workloads in Golang and Python

When you're choosing between Golang and Python for running a web service, one of the more practical concerns is memory usage — or "memory footprint" as it's often called. If you're deploying identical workloads in both languages, the differences can be pretty stark.

Memory Footprint Comparison: Running Identical Web Service Workloads in Golang and Python

Here’s what you’ll typically see: Go tends to use significantly less memory than Python when serving the same kind of workload. The reasons are rooted in language design, runtime behavior, and how each handles concurrency.

Memory Footprint Comparison: Running Identical Web Service Workloads in Golang and Python

Why Golang Uses Less Memory

Go was designed with systems programming in mind, and that shows up clearly in its performance characteristics. When you run a web service in Go:

  • Goroutines are lightweight: Each goroutine starts with a small stack (2KB by default) and grows as needed. Thousands of concurrent connections don’t eat up memory the way threads or processes do in other languages.
  • The garbage collector is optimized for low latency and efficiency: It doesn’t clean up as aggressively as Python’s reference counting system, which helps reduce overhead.
  • Static binaries and minimal runtime dependencies mean there's no interpreter layer sitting on top of your process.

All of this adds up to a much leaner process when you're running a Go-based web server compared to an equivalent Python service.

Memory Footprint Comparison: Running Identical Web Service Workloads in Golang and Python

What Makes Python Use More Memory

Python’s simplicity and flexibility come at a cost when it comes to memory. Running the same logic in Python usually results in higher memory consumption because:

  • Each thread or async worker carries more overhead: Whether you're using Gunicorn with gevent or asyncio, each connection or coroutine still needs more memory than a goroutine.
  • Dynamic typing and object model are memory-heavy: Even basic objects like integers or strings take up more space in Python than their counterparts in compiled languages.
  • Interpreter overhead per process: Python’s Global Interpreter Lock (GIL) also means you often need to spawn multiple processes to scale, which multiplies memory usage.

If you’ve ever deployed a Flask or Django app next to a Go microservice doing the same thing, you may have noticed the Python version uses 10x or even 100x more RAM under similar load — especially when concurrency is high.


Realistic Comparison Setup

To get a fair comparison, people often test:

  • A simple HTTP endpoint that does minimal processing (like returning JSON).
  • Both services handling the same number of requests per second.
  • Load testing tools like wrk or ab to simulate traffic.
  • Measuring RSS (resident set size) or VIRT memory via top or htop.

For example:

  • A Go service using the standard net/http package might sit around 5–10MB of memory before any load.
  • A Python service using Flask Werkzeug could start around 20–40MB just from importing the framework.

Under load, the gap widens. With 100 concurrent requests:

  • Go might peak around 30–50MB.
  • Python might hit 100–200MB or more, depending on how many workers are running.

When Python’s Higher Footprint Might Not Matter

That said, Python’s memory overhead isn’t always a dealbreaker. For internal tools, prototypes, or apps with modest traffic, the extra RAM usage is negligible compared to developer productivity gains.

Also:

  • If your service spends most of its time waiting on I/O (like databases or external APIs), Python's memory usage won't be a bottleneck.
  • You can optimize Python apps with tools like uvicorn (with ASGI) or gunicorn meinheld to cut down on memory and improve throughput.

But if you're building something that needs to scale horizontally — like a high-throughput API or microservice — Go’s lower memory footprint becomes a real advantage.


So yeah, Go wins hands-down in raw memory efficiency when you’re running comparable web services. Python makes up for it in ease of use and ecosystem, but if you care about keeping your server costs low or squeezing more out of limited resources, Go’s lean runtime gives it a clear edge.

And that’s basically it.

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