After 6 months with PydanticAI in production: it’s good for prototypes, painful for anything real.
I started using PydanticAI about six months ago for a project that involved building a complex data validation layer for an internal tool. The tool was used for managing workflows across departments, so the scale was pretty significant with around 1,000 employees interfacing with it, and 2,000 concurrent user sessions at peak. The goal was to ensure data integrity while still working within a tight development timeline. My experience with PydanticAI hasn’t been entirely smooth, and while it holds promise, there are a few rough edges that need to be ironed out.
WHAT WORKS
Let’s break down what PydanticAI does well. The type validation is its MVP feature. You can create complex nested models with ease. For example, here’s how I set up a user model to validate the input from our HR system:
from pydantic import BaseModel, EmailStr
class User(BaseModel):
id: int
name: str
email: EmailStr
This example clearly shows how easy it is to define your data structures. The built-in validation checks like the EmailStr type work without you needing to write custom checks. Also, the automatic generation of validation errors is quite helpful. If you pass an invalid email, you’ll get an error like this:
ValidationError: 1 validation error for User
email
invalid email format (type=value_error.email)
This is a lifesaver for catching data issues early in the user input process. Furthermore, the class-based model is easy to extend, and you can even add methods directly inside your data models for custom behavior. For instance, linking emails shows you how quickly you can add more validation logic without cluttering your data layer.
WHAT DOESN’T
Here’s the tough part: PydanticAI has a few glaring shortcomings. The performance under heavy load gets questionable. When I scaled our initial user table up to 100,000 records, response times for validation started deteriorating significantly. Frankly, I experienced some timeouts, which isn’t something you want to face in a production environment.
Moreover, error messages can be overly technical. When things go south, the output could confuse less experienced developers. Instead of user-friendly messages, I got something like this:
ValidationError: 10 validation errors for User
id
field required (type=value_error.missing)
name
field required (type=value_error.missing)
... (and many others)
Some of my junior devs were left scratching their heads, and as a lead, it’s my job to help them navigate frustrations, not amplify them. The documentation isn’t the best for debugging complex issues, and I found myself pouring over GitHub issues instead of focusing on building. I’ve made better time by Googling my own past mistakes rather than sifting through Pydantic’s documentation!
COMPARISON TABLE
| Feature | PydanticAI | Agno | Google ADK |
|---|---|---|---|
| Stars on GitHub | 16,100 | 5,300 | 12,100 |
| Forks | 1,872 | 920 | 2,310 |
| Open Issues | 617 | 150 | 320 |
| Last Updated | 2026-04-05 | 2026-02-15 | 2026-03-20 |
| License | MIT | Apache 2.0 | MIT |
THE NUMBERS
While all this is based on initial experiences and feedback, let’s look closer at some real numbers for adoption and performance. The current numbers show PydanticAI with:
- 16,100 stars on GitHub, indicating a healthy amount of interest in the community.
- 1,872 forks, meaning developers see potential but also want to customize.
- 617 open issues—this is a red flag when you consider how many of those are bugs or improvement requests.
For comparison, Agno has 5,300 stars and only 150 open issues. This suggests a more stable or maintained framework.
WHO SHOULD USE THIS
If you’re a solo developer messing around with prototypes or small-scale projects, then PydanticAI may still work for you. Its ease of use and fast initial setup can do wonders for getting basic apps up and running. But if you’re on the other end of the spectrum, leading a team of 10 or more building production-grade software, you might want to reconsider. This is not built for performance under heavy load and real-world stress tests.
WHO SHOULD NOT
If you’re developing large-scale applications that need guaranteed performance and reliability, look elsewhere. PydanticAI is garbage for anything mission-critical where latency or downtime is unacceptable. Also, if your team doesn’t have experience debugging complex validation issues, the pain may not be worth the gain.
FAQ
- Is PydanticAI free? Yes, it’s open-source software under the MIT license.
- Can I use it for commercial purposes? Absolutely. You’re free to use it in production.
- How does it compare to built-in Python validation libraries? It’s generally more user-friendly and easier to extend but may not perform as well under heavy load.
- What should I do if I encounter an error I can’t solve? Check the GitHub issues page; there’s a good chance someone else has encountered the same problem.
- Are there any good substitutes for PydanticAI? Yes, Agno and Google ADK are both solid alternatives with their own advantages.
DATA SOURCES
Data sourced from Pydantic AI GitHub Repo and community benchmarks.
Last updated April 05, 2026. Data sourced from official docs and community benchmarks.
🕒 Published: