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Why use Pydantic?

Today, Pydantic is downloaded many times a month and used by some of the largest and most recognisable organisations in the world.

It's hard to know why so many people have adopted Pydantic since its inception six years ago, but here are a few guesses.

Type hints powering schema validation

The schema that Pydantic validates against is generally defined by Python type hints.

Type hints are great for this since, if you're writing modern Python, you already know how to use them. Using type hints also means that Pydantic integrates well with static typing tools like mypy and pyright and IDEs like pycharm and vscode.

Example - just type hints

(This example requires Python 3.9+)

from typing import Annotated, Dict, List, Literal, Tuple

from annotated_types import Gt

from pydantic import BaseModel

class Fruit(BaseModel):
    name: str  # (1)!
    color: Literal['red', 'green']  # (2)!
    weight: Annotated[float, Gt(0)]  # (3)!
    bazam: Dict[str, List[Tuple[int, bool, float]]]  # (4)!

        bazam={'foobar': [(1, True, 0.1)]},
#> name='Apple' color='red' weight=4.2 bazam={'foobar': [(1, True, 0.1)]}

  1. The name field is simply annotated with str - any string is allowed.
  2. The Literal type is used to enforce that color is either 'red' or 'green'.
  3. Even when we want to apply constraints not encapsulated in python types, we can use Annotated and annotated-types to enforce constraints without breaking type hints.
  4. I'm not claiming "bazam" is really an attribute of fruit, but rather to show that arbitrarily complex types can easily be validated.

Learn more

See the documentation on supported types.


Pydantic's core validation logic is implemented in a separate package pydantic-core, where validation for most types is implemented in Rust.

As a result, Pydantic is among the fastest data validation libraries for Python.

Performance Example - Pydantic vs. dedicated code

In general, dedicated code should be much faster than a general-purpose validator, but in this example Pydantic is >300% faster than dedicated code when parsing JSON and validating URLs.

Performance Example
import json
import timeit
from urllib.parse import urlparse

import requests

from pydantic import HttpUrl, TypeAdapter

reps = 7
number = 100
r = requests.get('')
emojis_json = r.content

def emojis_pure_python(raw_data):
    data = json.loads(raw_data)
    output = {}
    for key, value in data.items():
        assert isinstance(key, str)
        url = urlparse(value)
        assert url.scheme in ('https', 'http')
        output[key] = url

emojis_pure_python_times = timeit.repeat(
        'emojis_pure_python': emojis_pure_python,
        'emojis_json': emojis_json,
print(f'pure python: {min(emojis_pure_python_times) / number * 1000:0.2f}ms')
#> pure python: 5.32ms

type_adapter = TypeAdapter(dict[str, HttpUrl])
emojis_pydantic_times = timeit.repeat(
        'type_adapter': type_adapter,
        'HttpUrl': HttpUrl,
        'emojis_json': emojis_json,
print(f'pydantic: {min(emojis_pydantic_times) / number * 1000:0.2f}ms')
#> pydantic: 1.54ms

    f'Pydantic {min(emojis_pure_python_times) / min(emojis_pydantic_times):0.2f}x faster'
#> Pydantic 3.45x faster

Unlike other performance-centric libraries written in compiled languages, Pydantic also has excellent support for customizing validation via functional validators.

Learn more

Samuel Colvin's talk at PyCon 2023 explains how pydantic-core works and how it integrates with Pydantic.


Pydantic provides functionality to serialize model in three ways:

  1. To a Python dict made up of the associated Python objects
  2. To a Python dict made up only of "jsonable" types
  3. To a JSON string

In all three modes, the output can be customized by excluding specific fields, excluding unset fields, excluding default values, and excluding None values

Example - Serialization 3 ways
from datetime import datetime

from pydantic import BaseModel

class Meeting(BaseModel):
    when: datetime
    where: bytes
    why: str = 'No idea'

m = Meeting(when='2020-01-01T12:00', where='home')
#> {'when': datetime.datetime(2020, 1, 1, 12, 0), 'where': b'home'}
print(m.model_dump(exclude={'where'}, mode='json'))
#> {'when': '2020-01-01T12:00:00', 'why': 'No idea'}
#> {"when":"2020-01-01T12:00:00","where":"home"}

Learn more

See the documentation on serialization.

JSON Schema

JSON Schema can be generated for any Pydantic schema — allowing self-documenting APIs and integration with a wide variety of tools which support JSON Schema.

Example - JSON Schema
from datetime import datetime

from pydantic import BaseModel

class Address(BaseModel):
    street: str
    city: str
    zipcode: str

class Meeting(BaseModel):
    when: datetime
    where: Address
    why: str = 'No idea'

    '$defs': {
        'Address': {
            'properties': {
                'street': {'title': 'Street', 'type': 'string'},
                'city': {'title': 'City', 'type': 'string'},
                'zipcode': {'title': 'Zipcode', 'type': 'string'},
            'required': ['street', 'city', 'zipcode'],
            'title': 'Address',
            'type': 'object',
    'properties': {
        'when': {'format': 'date-time', 'title': 'When', 'type': 'string'},
        'where': {'$ref': '#/$defs/Address'},
        'why': {'default': 'No idea', 'title': 'Why', 'type': 'string'},
    'required': ['when', 'where'],
    'title': 'Meeting',
    'type': 'object',

Pydantic generates JSON Schema version 2020-12, the latest version of the standard which is compatible with OpenAPI 3.1.

Learn more

See the documentation on JSON Schema.

Strict mode and data coercion

By default, Pydantic is tolerant to common incorrect types and coerces data to the right type — e.g. a numeric string passed to an int field will be parsed as an int.

Pydantic also has strict=True mode — also known as "Strict mode" — where types are not coerced and a validation error is raised unless the input data exactly matches the schema or type hint.

But strict mode would be pretty useless when validating JSON data since JSON doesn't have types matching many common python types like datetime, UUID or bytes.

To solve this, Pydantic can parse and validate JSON in one step. This allows sensible data conversion like RFC3339 (aka ISO8601) strings to datetime objects. Since the JSON parsing is implemented in Rust, it's also very performant.

Example - Strict mode that's actually useful
from datetime import datetime

from pydantic import BaseModel, ValidationError

class Meeting(BaseModel):
    when: datetime
    where: bytes

m = Meeting.model_validate({'when': '2020-01-01T12:00', 'where': 'home'})
#> when=datetime.datetime(2020, 1, 1, 12, 0) where=b'home'
    m = Meeting.model_validate(
        {'when': '2020-01-01T12:00', 'where': 'home'}, strict=True
except ValidationError as e:
    2 validation errors for Meeting
      Input should be a valid datetime [type=datetime_type, input_value='2020-01-01T12:00', input_type=str]
      Input should be a valid bytes [type=bytes_type, input_value='home', input_type=str]

m_json = Meeting.model_validate_json(
    '{"when": "2020-01-01T12:00", "where": "home"}'
#> when=datetime.datetime(2020, 1, 1, 12, 0) where=b'home'

Learn more

See the documentation on strict mode.

Dataclasses, TypedDicts, and more

Pydantic provides four ways to create schemas and perform validation and serialization:

  1. BaseModel — Pydantic's own super class with many common utilities available via instance methods.
  2. pydantic.dataclasses.dataclass — a wrapper around standard dataclasses which performs validation when a dataclass is initialized.
  3. TypeAdapter — a general way to adapt any type for validation and serialization. This allows types like TypedDict and NamedTuple to be validated as well as simple scalar values like int or timedeltaall types supported can be used with TypeAdapter.
  4. validate_call — a decorator to perform validation when calling a function.
Example - schema based on TypedDict
from datetime import datetime

from typing_extensions import NotRequired, TypedDict

from pydantic import TypeAdapter

class Meeting(TypedDict):
    when: datetime
    where: bytes
    why: NotRequired[str]

meeting_adapter = TypeAdapter(Meeting)
m = meeting_adapter.validate_python(  # (1)!
    {'when': '2020-01-01T12:00', 'where': 'home'}
#> {'when': datetime.datetime(2020, 1, 1, 12, 0), 'where': b'home'}
meeting_adapter.dump_python(m, exclude={'where'})  # (2)!

print(meeting_adapter.json_schema())  # (3)!
    'properties': {
        'when': {'format': 'date-time', 'title': 'When', 'type': 'string'},
        'where': {'format': 'binary', 'title': 'Where', 'type': 'string'},
        'why': {'title': 'Why', 'type': 'string'},
    'required': ['when', 'where'],
    'title': 'Meeting',
    'type': 'object',
  1. TypeAdapter for a TypedDict performing validation, it can also validate JSON data directly with validate_json
  2. dump_python to serialise a TypedDict to a python object, it can also serialise to JSON with dump_json
  3. TypeAdapter can also generate JSON Schema


Functional validators and serializers, as well as a powerful protocol for custom types, means the way Pydantic operates can be customized on a per-field or per-type basis.

Customisation Example - wrap validators

"wrap validators" are new in Pydantic V2 and are one of the most powerful ways to customize Pydantic validation.

from datetime import datetime, timezone

from pydantic import BaseModel, field_validator

class Meeting(BaseModel):
    when: datetime

    @field_validator('when', mode='wrap')
    def when_now(cls, input_value, handler):
        if input_value == 'now':
        when = handler(input_value)
        # in this specific application we know tz naive datetimes are in UTC
        if when.tzinfo is None:
            when = when.replace(tzinfo=timezone.utc)
        return when

#> when=datetime.datetime(2020, 1, 1, 12, 0, tzinfo=TzInfo(+01:00))
#> when=datetime.datetime(2032, 1, 2, 3, 4, 5, 6)
#> when=datetime.datetime(2020, 1, 1, 12, 0, tzinfo=datetime.timezone.utc)

Learn more

See the documentation on validators, custom serializers, and custom types.


At the time of writing there are 214,100 repositories on GitHub and 8,119 packages on PyPI that depend on Pydantic.

Some notable libraries that depend on Pydantic:

More libraries using Pydantic can be found at Kludex/awesome-pydantic.

Organisations using Pydantic

Some notable companies and organisations using Pydantic together with comments on why/how we know they're using Pydantic.

The organisations below are included because they match one or more of the following criteria:

  • Using pydantic as a dependency in a public repository
  • Referring traffic to the pydantic documentation site from an organization-internal domain - specific referrers are not included since they're generally not in the public domain
  • Direct communication between the Pydantic team and engineers employed by the organization about usage of Pydantic within the organization

We've included some extra detail where appropriate and already in the public domain.


adobe/dy-sql uses Pydantic.

Amazon and AWS


anthropics/anthropic-sdk-python uses Pydantic.


(Based on the criteria described above)


(Based on the criteria described above)


Multiple repos in the AstraZeneca GitHub org depend on Pydantic.

Cisco Systems


(Based on the criteria described above)


  • Extensive use of Pydantic in DataDog/integrations-core and other repos
  • Communication with engineers from Datadog about how they use Pydantic.


Multiple repos in the facebookresearch GitHub org depend on Pydantic.


GitHub sponsored Pydantic $750 in 2022


Extensive use of Pydantic in google/turbinia and other repos.


(Based on the criteria described above)


Multiple repos in the IBM GitHub org depend on Pydantic.


(Based on the criteria described above)


(Based on the criteria described above)

Intergovernmental Panel on Climate Change

Tweet explaining how the IPCC use Pydantic.


(Based on the criteria described above)


  • The developers of the Jupyter notebook are using Pydantic for subprojects
  • Through the FastAPI-based Jupyter server Jupyverse
  • FPS's configuration management.


  • DeepSpeed deep learning optimisation library uses Pydantic extensively
  • Multiple repos in the microsoft GitHub org depend on Pydantic, in particular their
  • Pydantic is also used in the Azure GitHub org
  • Comments on GitHub show Microsoft engineers using Pydantic as part of Windows and Office

Molecular Science Software Institute

Multiple repos in the MolSSI GitHub org depend on Pydantic.


Multiple repos in the NASA GitHub org depend on Pydantic.

NASA are also using Pydantic via FastAPI in their JWST project to process images from the James Webb Space Telescope, see this tweet.


Multiple repos in the Netflix GitHub org depend on Pydantic.


The nsacyber/WALKOFF repo depends on Pydantic.


Mupltiple repos in the NVIDIA GitHub org depend on Pydantic.

Their "Omniverse Services" depends on Pydantic according to their documentation.


OpenAI use Pydantic for their ChatCompletions API, as per this discussion on GitHub.

Anecdotally, OpenAI use Pydantic extensively for their internal services.


(Based on the criteria described above)


(Based on the criteria described above)


(Based on the criteria described above)

Red Hat

(Based on the criteria described above)


Anecdotally, all internal services at Revolut are built with FastAPI and therefore Pydantic.


The robusta-dev/robusta repo depends on Pydantic.


Salesforce sponsored Samuel Colvin $10,000 to work on Pydantic in 2022.


(Based on the criteria described above)

Texas Instruments

(Based on the criteria described above)


(Based on the criteria described above)


Twitter's the-algorithm repo where they open sourced their recommendation engine uses Pydantic.

UK Home Office

(Based on the criteria described above)