If you've ever heard the terms concurrency and parallelism used interchangeably, you're not alone.

They are closely related, but they describe two different ideas.

Understanding the difference becomes especially important when you're building APIs, processing background jobs, handling large workloads, or trying to improve application performance.

Before we go further, it helps to understand the building blocks behind these concepts: processes and threads.

If those concepts are still unclear, check out our previous guide:

Processes and Threads Explained: A Practical Guide for Software Engineers

Now let's answer the real question:

What is the difference between concurrency and parallelism?

Concurrency vs Parallelism in Simple Terms

Let's start with the simplest definitions.

Concurrency

Concurrency is the ability to make progress on multiple tasks during overlapping periods of time.

The tasks don't necessarily execute at exactly the same moment.

Parallelism

Parallelism is the execution of multiple tasks at the same time.

The key difference is:

Concurrency is about dealing with multiple tasks.

Parallelism is about executing multiple tasks simultaneously.

That sounds simple, but there's a much better way to understand it.

Let's go back to our restaurant.

The Restaurant Analogy

Imagine you own a restaurant.

From our previous article:

Restaurant = Process

Worker = Thread

The restaurant provides the environment and resources, while the workers perform the actual work.

Now imagine three customers arrive:

Customer A → Order
Customer B → Order
Customer C → Order

How you handle these orders determines whether we're talking about sequential execution, concurrency, or parallelism.

Sequential Execution: One Thing at a Time

Imagine you have only one worker.

The worker handles each order completely before moving to the next:

Worker
  │
  ├── Order A
  │      ↓
  │    Finish
  │
  ├── Order B
  │      ↓
  │    Finish
  │
  └── Order C
         ↓
       Finish

Nothing overlaps.

Order A must finish before Order B starts.

Order B must finish before Order C starts.

This is sequential execution.

Concurrency: Switching Between Tasks

Now imagine the same worker has three orders to handle.

But instead of waiting for one order to completely finish, the worker can switch between tasks.

For example:

Order A → Start
             ↓
Order B → Start
             ↓
Order A → Continue
             ↓
Order C → Start
             ↓
Order B → Continue
             ↓
Order A → Finish

The worker is switching between tasks.

The tasks are making progress during overlapping periods.

That's concurrency.

Notice something important:

We still have only one worker.

The worker cannot physically perform three completely different actions at the exact same instant.

Instead, the work is interleaved.

Time →
 
Worker
│
├── A
├── B
├── A
├── C
├── B
└── A

That's concurrency.

Parallelism: Multiple Workers

Now let's hire three workers.

Restaurant
│
├── 👨‍🍳 Worker 1 → Order A
├── 👩‍🍳 Worker 2 → Order B
└── 👨‍🍳 Worker 3 → Order C

Now the workers can actually perform different tasks at the same time.

Worker 1 → Order A
Worker 2 → Order B
Worker 3 → Order C

That's parallelism.

We're no longer just switching between tasks.

Multiple workers are performing work simultaneously.

The CPU Makes This Even Clearer

Now replace our restaurant with a computer.

The workers become threads, and the kitchen's processing capacity becomes the CPU.

Imagine your computer has one CPU core:

CPU
└── Core 1

But your application has three threads:

Thread 1
Thread 2
Thread 3

The operating system can schedule these threads by switching between them:

Core 1
 
Thread 1
   ↓
Thread 2
   ↓
Thread 3
   ↓
Thread 1
   ↓
Thread 2

This allows multiple tasks to make progress.

That's concurrency.

What If We Have Multiple CPU Cores?

Now imagine your machine has four cores:

CPU
├── Core 1
├── Core 2
├── Core 3
└── Core 4

Now multiple threads can potentially execute at the same time:

Core 1 → Thread 1
Core 2 → Thread 2
Core 3 → Thread 3
Core 4 → Thread 4

This is parallelism.

Multiple execution resources are working simultaneously.

So our restaurant analogy becomes:

One worker
    ↓
Concurrency through task switching
 
Multiple workers
    ↓
Parallelism through simultaneous work

Concurrency Does Not Require Multiple CPU Cores

This is one of the most important points to understand.

You can have:

1 CPU Core
+
Multiple Threads
=
Concurrency

The operating system can switch between the threads.

For example:

Time →
 
Core 1
│
├── Thread A
├── Thread B
├── Thread A
├── Thread C
├── Thread B
└── Thread A

The CPU is still executing one thread at a time on that core.

But the application can still be handling multiple tasks concurrently.

Parallelism Requires Multiple Execution Resources

For actual simultaneous execution, you need multiple execution resources.

For example:

Core 1 → Task A
Core 2 → Task B
Core 3 → Task C

Now the tasks can genuinely execute at the same time.

This is why the number of CPU cores matters when discussing CPU-bound workloads.

Where I/O Changes Everything

Now let's move from restaurants to something closer to the applications we build.

Imagine your API needs to perform three operations:

Task A → Query PostgreSQL
Task B → Call an external API
Task C → Read a file

These operations involve waiting.

For example:

Task A
  │
  └── Send database query
          │
          └── WAIT
 
 
Task B
  │
  └── Send HTTP request
          │
          └── WAIT
 
 
Task C
  │
  └── Read file
          │
          └── WAIT

The CPU doesn't necessarily need to be actively computing during all of those waiting periods.

This is where concurrency becomes extremely useful.

Instead of doing:

Task A → WAIT → Finish
Task B → WAIT → Finish
Task C → WAIT → Finish

you can have overlapping progress:

Task A → WAIT ───────────────→ Finish
Task B    → WAIT ────────────→ Finish
Task C       → WAIT ─────────→ Finish

While Task A is waiting for PostgreSQL, the system can potentially make progress on Task B.

While Task B is waiting for a network response, it can potentially make progress on Task C.

The result can be much better utilization of available resources.

This Is Why I/O-Bound Work Matters

I/O-bound work is work that spends significant time waiting for something outside the CPU.

Examples include:

• Database queries
• HTTP requests
• Reading files
• Writing files
• Network communication
• Calling external services

Concurrency is particularly useful for workloads like these.

Python's official documentation describes asyncio as a framework for writing concurrent code and notes that it is often a good fit for I/O-bound and high-level network code.

What About CPU-Bound Work?

Now imagine a completely different task.

Suppose you're processing a huge dataset:

Calculate millions of values

Or you're performing:

• Image processing
• Video encoding
• Large mathematical calculations
• Scientific simulations
• Machine learning computations

Here, the CPU is doing actual work.

There isn't necessarily a long period of waiting for a database or network response.

This is a CPU-bound workload.

And this is where parallelism becomes particularly useful.

Imagine:

Core 1 → Process data chunk A
Core 2 → Process data chunk B
Core 3 → Process data chunk C
Core 4 → Process data chunk D

Instead of one core doing everything:

Core 1
  ↓
A → B → C → D

multiple cores can work on different portions simultaneously.

Concurrency vs Parallelism

At this point, we can summarize the difference:

The easiest way to remember it is:

Concurrency is about structure and coordination.

Parallelism is about simultaneous execution.

Context Switching

Concurrency on a single CPU core often involves context switching.

Suppose the CPU is executing Thread A:

Thread A
   ↓
CPU

The operating system can pause Thread A and switch to Thread B:

Thread A
   ↓
Context Switch
   ↓
Thread B

Later:

Thread B
   ↓
Context Switch
   ↓
Thread A

The operating system saves the state of one thread and restores the state of another.

This allows multiple threads to share CPU time.

But context switching has a cost.

If you create far more threads than your workload needs, the system can spend significant resources managing and switching between them.

More concurrency does not automatically mean better performance.

Concurrency Is Not the Same as Speed

This is another common misconception.

Suppose we have two tasks:

Task A = 10 seconds
Task B = 10 seconds

Sequential:

A: 0 ───────── 10
B:             10 ───────── 20
 
Total ≈ 20 seconds

If both tasks can make progress concurrently:

A: 0 ───────── 10
B: 0 ───────── 10
 
Total ≈ 10 seconds

That looks much faster.

But the improvement depends on what the tasks are doing and what resources they are waiting for.

Concurrency is not magic.

If two tasks are competing for the same limited resource, adding concurrency may provide little improvement—or even make things worse.

A Real Software Example

Imagine you're building an API endpoint:

GET /dashboard

The dashboard needs:

1. User information
2. Recent orders
3. Notifications
4. Analytics

A sequential implementation might conceptually do:

User
 ↓
Orders
 ↓
Notifications
 ↓
Analytics
 ↓
Response

If these operations are independent, concurrency can allow multiple operations to make progress during overlapping periods:

             Dashboard
                 │
       ┌─────────┼─────────┐
       ▼         ▼         ▼
     Users     Orders   Notifications
       │         │         │
       └─────────┼─────────┘
                 ▼
             Analytics
                 │
                 ▼
              Response

The exact implementation could involve asynchronous I/O, threads, or another concurrency mechanism depending on the application and runtime.

Where asyncio Fits In

This leads naturally to asynchronous programming.

Python's asyncio provides an event loop that runs asynchronous tasks and handles operations such as network I/O.

A simplified mental model is:

                 Event Loop
                     │
          ┌──────────┼──────────┐
          ▼          ▼          ▼
        Task A     Task B     Task C
          │          │          │
       Waiting    Running     Waiting
          │          │          │
          └──────────┼──────────┘
                     ▼
                Continue work

When an asynchronous task reaches an await and needs to wait for I/O, the event loop can suspend that task and work on another task.

Python's documentation explicitly describes this behavior: while one task is suspended at an await, the event loop can execute another task in the same thread.

Notice something interesting:

You can achieve concurrency without creating a thread for every task.

That's one of the reasons asynchronous programming is so useful for high-concurrency I/O workloads.

Threads, Processes, and Async: How Do They Relate?

Now we can connect everything we've learned.

                 CONCURRENCY
                     │
          ┌──────────┼──────────┐
          ▼          ▼          ▼
       Threads     Async     Processes
          │          │          │
          │          │          │
       Multiple    Event     Multiple
       execution   loop      processes
       paths

These are different mechanisms and models for handling multiple tasks.

You shouldn't choose one simply because:

"Concurrency is good."

Instead ask:

What type of workload am I dealing with?

A Practical Decision Framework

When you're building a system, start with the workload.

Is it mostly waiting for I/O?

For example:

Database
HTTP
Files
Network
External APIs

Consider concurrency mechanisms such as:

Async I/O
Threads

depending on your language, framework, libraries, and workload.

Is it CPU-heavy?

For example:

Image processing
Video encoding
Large computations
Data processing

Think about:

Parallel execution
Multiple processes
Multiple CPU cores

Again, the exact choice depends on the language/runtime and workload.

Bringing It Back to the Restaurant

Let's finish where we started.

Sequential

One worker handles one order at a time:

👨‍🍳
A → B → C

Concurrent

One worker switches between multiple orders:

👨‍🍳
A → B → A → C → B → A

Parallel

Multiple workers handle multiple orders simultaneously:

👨‍🍳 → A
 
👩‍🍳 → B
 
👨‍🍳 → C

That's the entire concept.

The Mental Model to Keep

Don't memorize:

"Concurrency means threads."

And don't memorize:

"Parallelism means processes."

Those are oversimplifications.

Instead remember:

CONCURRENCY
    ↓
Multiple tasks can make progress
during overlapping periods.
 
PARALLELISM
    ↓
Multiple tasks execute
simultaneously.

Then ask:

What is my workload?
       │
       ├── Waiting / I/O
       │       ↓
       │   Concurrency
       │
       └── Heavy computation
               ↓
           Parallelism

And finally:

Concurrency is about how we structure and manage multiple tasks.

Parallelism is about using multiple execution resources to perform multiple tasks at the same time.

Once this distinction becomes clear, concepts such as async/await, event loops, thread pools, process pools, multiprocessing, Celery workers, web-server workers, and horizontal scaling become much easier to understand.

And that is where these operating-system concepts stop being theory and start becoming practical tools for designing production software.

Further Reading

• Processes and Threads Explained: A Practical Guide for Software Engineers — Kodane
• Python Documentation — asyncio
• Python Documentation — Event Loop
• Python Documentation — Developing with asyncio