Space complexity calculator

Paste your code and get an AI-assisted explanation of memory growth, auxiliary storage, and recursion stack usage.

Useful for queries like space complexity calc, auxiliary space calculator, and memory complexity analyzer.

The Lab

your_code

def bubble_sort(arr):
    n = len(arr)
    for i in range(n):
        for j in range(0, n-i-1):
            if arr[j] > arr[j+1]:
                arr[j], arr[j+1] = arr[j+1], arr[j]
    return arr

def bubble_sort(arr):
    n = len(arr)
    for i in range(n):
        for j in range(0, n-i-1):
            if arr[j] > arr[j+1]:
                arr[j], arr[j+1] = arr[j+1], arr[j]
    return arr

Push it. I dare you.

The Story

"Your code is a blank page. Let's write the story."

How This Space Complexity Calculator Works

Estimate auxiliary memory growth, recursion stack usage, and temporary storage from real code.

Paste Code

Drop in Python, JavaScript, Java, C, or C++ code that allocates arrays, maps, stacks, or recursive calls.

Inspect Memory Growth

The analyzer looks for auxiliary arrays, recursion depth, and data structures that grow with input size.

Read The Tradeoff

Get a clearer view of how time and space interact so you can compare implementations more confidently.

PythonJavaScriptJavaCC++

This page is built for space complexity queries, but it also works best when paired with the time complexity calculator so you can compare speed against memory usage.

How To Calculate Time Complexity Read The Big O Guide

Common Complexity Classes

These patterns help you compare how memory grows, especially when recursion, auxiliary arrays, and data structures are involved.

O(1)

Constant

Array access, hash lookup

O(log n)

Logarithmic

Binary searching

O(n)

Linear

Single loop, linear search

O(n log n)

Linearithmic

Merge sort, quick sort

O(n²)

Quadratic

Nested loops, bubble sort

O(2ⁿ)

Exponential

Recursive Fibonacci

The Free Space Complexity Calculator

Estimate memory growth, auxiliary storage, and recursion stack usage without signing up.

Time Complexity Analyzer

Instantly analyze O(N), O(log N), O(N²) and more patterns in your code. Get Big O notation with AI explanations.

Space Complexity Calculator

Calculate auxiliary space and memory usage of your algorithms. Analyze O notation with AI-powered insights.

AI-Powered Insights

Get natural language explanations of complexity patterns. Understand algorithm efficiency in plain English.

Master Big O Notation

Master algorithm complexity with 16+ interactive tutorials. From linear search to dynamic programming.

Why Choose TimeComplexityAI?

Feature	TimeComplexityAI	Others
AI-Powered Explanations		---
Space Complexity Labs		---
16+ Deep-Dive Tutorials		---
Step-by-Step Breakdowns		---
Free & Clean UI		---

Learn The Patterns

Go beyond the calculator. These deep-dive guides connect theory to the lines of code you write every day.

Tutorials

Crawlable guides on binary search, merge sort, graphs, and dynamic programming.

Browse Guides

Math Lab

Use the step-by-step analyzer to understand where each complexity term comes from.

Enter Lab

The Blog

Read focused explainers on Big O notation, Bubble Sort, and Merge Sort theory.

Read Articles

Space Complexity Calculator FAQ

Answers to common questions about memory growth, auxiliary space, and how to use this page.

What does this space complexity calculator measure?

It estimates auxiliary memory growth, including temporary arrays, maps, recursion stack usage, and other input-dependent storage.

Does it include recursion stack and helper data structures?

Yes. The goal is to explain both explicit allocations and implicit memory growth from recursion depth and support structures.

Can I compare time and space complexity together?

Yes. The calculator and linked guides are meant to help you compare runtime efficiency against memory tradeoffs.