Future of Pydantic and FastAPI

Episode Deep Dive

Guests Introduction and Background

Samuel Colvin is the creator of Pydantic, the popular data validation and settings management library for Python. His work on Pydantic has led to a brand-new company and a major update, Pydantic v2, which includes Rust-based performance optimizations.

Sebastián Ramírez is the creator of FastAPI, a high-performance Python web framework that uses type hints to deliver automatic validation, serialization, and documentation. Together, they share a passion for harnessing Python’s modern language features and exploring interesting performance optimizations.

What to Know If You're New to Python

If you’re just starting your Python journey, a basic understanding of data structures (like lists, dictionaries, and sets) and how Python’s type hints work will help you follow along. Many of the discussions revolve around validating data and leveraging Python’s runtime type information. If you’re unfamiliar with these topics, just know that Python's typing system helps you (and your tools) catch mistakes early. The topics in this episode will be clearer once you’ve used Python in data validation or basic web projects.

Key Points and Takeaways

1. Massive Performance Gains with Pydantic 2 The new version of Pydantic brings a significant speedup—often cited as up to a 20x improvement in certain validation scenarios. This boost comes from major refactoring in both Python and Rust, drastically reducing overhead for large or complex data structures. Faster validation translates to lower latency in web services and less resource usage overall. Many real-world applications, including those using FastAPI or data pipelines handling large JSON structures, benefit from these performance improvements. Even if your project doesn’t need the absolute best speed, having more efficient operations can open up possibilities or allow you to process bigger workloads with fewer resources.
   • Links / Tools:
     • Pydantic GitHub
     • Rust (general) at rust-lang.org
2. Refactoring into Pydantic Core and Pydantic (Python Layer) Pydantic v2 divides the library into a high-performance core (written in Rust) and a Python layer for developer-facing features. This refactoring simplifies code maintenance and allows each layer to focus on its strengths: Rust for speed and Python for readability. The separation also makes it easier to introduce new features without risking major regressions to core functionality. Because Pydantic Core is where the heavy lifting happens, bug fixes and optimizations apply broadly, while the Python layer can evolve around better developer ergonomics. This structure sets the stage for more ambitious updates down the road.
3. Partial Rewrite in Rust for Efficiency Rust was chosen for speed, memory safety, and the ability to integrate efficiently with Python. By shifting critical logic into Rust, Pydantic can handle massive data validation tasks more efficiently than in pure Python. The Rust code also uses Serde for JSON serialization, making it possible to parse data directly from bytes in a very controlled manner. This approach benefits not only CPU-bound operations but can also reduce overall resource usage, since Rust helps avoid performance pitfalls like the Python GIL in certain parts of the workload. While developers need not write Rust themselves, they benefit from the low-level optimizations in Pydantic.
   • Links / Tools:
     • Rust Language
     • Serde (Rust)
4. Tight Integration with FastAPI FastAPI is one of Pydantic’s largest “customers,” using it deeply for data validation and serialization. Pydantic 2 will allow FastAPI to drop a few internal hacks and fully rely on the Rust-based core for speed. Sebastian Ramírez notes that FastAPI users will often see a speed boost, especially for response serialization, without changing a single line of application code. Additionally, the improved control over how data is parsed and serialized will support more flexible workflows, like returning partial objects without exposing sensitive attributes. This synergy underlines why both projects share a close development relationship.
   • Links / Tools:
     • FastAPI documentation
5. Alpha Release and Testing Pydantic v2 An alpha of Pydantic 2.0 has been made available, and early adopters can try it in non-critical environments. The Pydantic team welcomes feedback and bug reports to help finalize compatibility layers and fix any unforeseen edge cases. By experimenting with the alpha, projects can identify subtle validation changes and decide if they need migration paths or code tweaks. The earlier people test, the more likely the final 2.0 release will address real-world issues. This community-driven approach ensures the Pydantic ecosystem remains stable during a major transition.
   • Links / Tools:
     • Pydantic Release Notes
6. New Company and VC Backing for Pydantic Samuel Colvin announced that he secured venture capital funding from notable firms like Sequoia, which allowed him to hire a dedicated team. This is a rare scenario where a Python open-source project receives major VC support to evolve beyond a single maintainer. The company’s immediate goal is finishing the Pydantic v2 rollout, but over time, they plan to explore commercial services built around Pydantic. While these plans remain flexible, the ability to raise capital demonstrates both the library’s success and the broader trust in open-source Python solutions.
   • Links / Tools:
     • Sequoia Capital
     • Samuel Colvin on Twitter
7. Implications for Other Libraries Relying on Pydantic Many popular libraries, such as Beanie (MongoDB ODM) and Django Ninja, use Pydantic under the hood for data validation. Improvements in Pydantic v2 mean these libraries can often remove specialized code previously needed for edge-case handling or performance workarounds. As a result, the entire ecosystem benefits—users of these libraries could see response times drop or gain access to new features for data serialization. Engaging with alpha releases helps library maintainers ensure a smooth migration. Ultimately, Pydantic’s enhancements cascade to a wide range of Python web and data solutions.
8. Adoption of Annotated and Next-Generation Typing Features Both Samuel and Sebastián highlighted Python’s typing evolution, especially the Annotated type from typing which simplifies how metadata can be stored with type hints. In Pydantic v2, annotated metadata is used to define constraints like greater_than=0 or to specify more advanced validation rules. Meanwhile, FastAPI uses Annotated to differentiate default values from parameter metadata. This approach benefits other tools like Hypothesis for property-based testing, which can automatically generate valid inputs based on type and metadata constraints. The alignment of typed frameworks is ushering in a new era of clearer, more maintainable code.
   • Links / Tools:
     • Python Annotated Type (PEP 593)
9. Upgrading from Pydantic v1 to v2 The majority of v1 code should “just work,” but some edge cases exist, like certain coercions that are now stricter. Projects that override __init__ or use advanced features like root validators may need to adapt to the new validator system. Overall, the maintainers plan to offer clear migration documentation and possibly tooling to automate code modifications. Users are encouraged to test v2 in staging or sandbox environments to catch any changes in data parsing behavior. By the time v2 is fully stable, the combination of speed and clearer validators should outweigh migration overhead for most teams.
10. PyCon 2023 and Community Energy The live interview setting at PyCon 2023 brought about an energetic discussion of success stories and hallway-track conversations. Both guests described the excitement of meeting other open-source contributors face to face for the first time. PyCon fosters these real-world collaborations, leading to better synergy and faster iteration on new features. Many library maintainers gather at PyCon to share direct feedback, which feeds right back into the roadmap. Getting first-hand developer stories continues to play a vital role in guiding Python’s next wave of libraries and frameworks.

Interesting Quotes and Stories

“The best thing about it is I didn’t think about how long it was going to take, so I tried to do everything.” – Samuel Colvin on rewriting Pydantic in Rust.

“One of the coolest parts is that people won’t have to change their code to get that performance benefit—just bump the version.” – Sebastián Ramírez on FastAPI’s upcoming speedups via Pydantic v2.

“I gave a talk in the Typing Summit. Now, there’s an understanding that runtime use of type hints is a legitimate thing.” – Samuel Colvin acknowledging how far Python typing has come.

Key Definitions and Terms

• Pydantic: A Python library for data validation and settings management. It uses type hints to parse and validate input data, returning Python objects with minimal fuss.
• FastAPI: A high-performance Python web framework that uses Pydantic for data handling and validation, offering automatic documentation and easy asynchronous code support.
• Annotated (PEP 593): A typing construct that allows attaching metadata to Python types, helping frameworks like Pydantic and FastAPI do more advanced validation or documentation.
• Serialization: The process of converting complex Python objects or models into simpler data formats (e.g. JSON) for storage or transmission.

Learning Resources

• Modern APIs with FastAPI and Python: Dive deeper into building APIs using FastAPI’s type-centric approach.
• Rock Solid Python with Python Typing: Develop a solid understanding of Python’s type hints and how projects like Pydantic leverage them.
• MongoDB with Async Python: See Pydantic in action with Beanie and FastAPI, handling async data persistence with MongoDB.

Overall Takeaway

Pydantic v2 and FastAPI’s next iteration represent a major leap in both speed and developer experience for Python web apps. By harnessing Rust under the hood, Pydantic delivers large-scale performance gains that percolate throughout the Python ecosystem—especially for data-intensive services. Thanks to close collaboration between Samuel Colvin and Sebastián Ramírez, FastAPI stands ready to provide a seamless upgrade path with fewer hacks and more elegant validation. Their combined efforts also highlight Python’s growing support for advanced typing features and the convergence of runtime and static type-checking benefits. Ultimately, these improvements underscore how vibrant and innovative the Python community can be when tackling real-world challenges together.