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Drawbacks of Msgspec Compared to Pydantic: A Deep Dive with Examples

· 3 min read
Serhii Hrekov
Serhii Hrekov
software engineer, creator, artist, programmer, projects founder

msgspec is gaining attention in the Python ecosystem due to its incredible speed and minimalist design. It's written in Rust, supports JSON and MsgPack, and uses type hints for validation. But like every tool, it’s not perfect — and when compared to the battle-tested and feature-rich Pydantic, there are several key trade-offs to be aware of.

In this article, we’ll explore what msgspec lacks compared to Pydantic, illustrated with code examples and practical reasoning.

🧪 1. Validation Flexibility and Custom Error Handling​

✅ Pydantic Example​

from pydantic import BaseModel, validator

class User(BaseModel):
    username: str
    age: int

    @validator('age')
    def age_must_be_adult(cls, v):
        if v < 18:
            raise ValueError('User must be at least 18 years old')
        return v

❌ Msgspec Limitation​

Msgspec has no native @validator feature. You must manually override __init__ or wrap deserialization in another function:

import msgspec

class User(msgspec.Struct):
    username: str
    age: int

def parse_user(json_bytes):
    obj = msgspec.json.decode(json_bytes, type=User)
    if obj.age < 18:
        raise ValueError("User must be at least 18")
    return obj

🧠 Why it matters: You lose the declarative style of field-level validation. Code becomes procedural and harder to reuse.

📋 2. Error Reporting and Debuggability​

✅ Pydantic​

from pydantic import ValidationError

try:
    User(username="Alice", age="not_an_int")
except ValidationError as e:
    print(e.json())

Output:

[
  {
    "loc": ["age"],
    "msg": "value is not a valid integer",
    "type": "type_error.integer"
  }
]

❌ Msgspec​

import msgspec

class User(msgspec.Struct):
    username: str
    age: int

msgspec.json.decode(b'{"username": "Alice", "age": "not_an_int"}', type=User)

Raises:

msgspec.DecodeError: Expected `int`, got `str`

🧠 Why it matters: Msgspec errors are fast and simple but lack the context and depth that make debugging easier.

🌐 3. Ecosystem Integration​

✅ Pydantic​

  • Works with FastAPI
  • Compatible with OpenAPI
  • Supported in ORMs, CLI tools, doc generators

❌ Msgspec​

  • No first-class FastAPI integration
  • Limited schema export support
  • Requires additional tooling

🧠 Why it matters: Msgspec is not plug-and-play in real-world API stacks.

🔄 4. Nested Models and Complex Structures​

✅ Pydantic​

from typing import List
from pydantic import BaseModel

class Pet(BaseModel):
    name: str

class Owner(BaseModel):
    pets: List[Pet]

❌ Msgspec​

import msgspec

class Pet(msgspec.Struct):
    name: str

class Owner(msgspec.Struct):
    pets: list[Pet]  # works, but no nested validation

🧠 Why it matters: Pydantic performs nested type checking and can enforce structure deep in your objects.

🔧 5. Decorators and Utilities​

  • .dict(), .json(), .copy() are standard with Pydantic
  • Msgspec only supports .to_builtins() and .to_json()

🧠 Why it matters: Pydantic has a richer set of convenience tools that improve developer experience.

🧾 Final Thoughts​

FeaturePydanticMsgspec
Custom validation✅ Yes❌ No
Nested model support✅ Yes⚠️ Limited
Ecosystem integration✅ Strong❌ Weak
Developer tools✅ Rich⚠️ Basic
JSON Schema generation✅ Yes⚠️ Partial
Performance❌ Slower✅ Fast

Msgspec is incredibly fast, but it's not a full replacement for Pydantic — especially in cases where developer productivity, error visibility, and integration matter.

Use Pydantic if you're building APIs, admin panels, or want flexibility.
Use Msgspec if you're optimizing raw throughput and can accept reduced ergonomics.

🔗 Resources​