When NOT to Use Pydantic (ossa-ma.github.io)

A recent discussion highlighted a common misuse of Pydantic—defaulting to its BaseModel without considering performance implications. While Pydantic excels at developer experience, it incurs a latency overhead of 10-50 microseconds per model, potentially reaching over 100 microseconds with complex validation. An effective framework is proposed through the “Latency Ladder,” categorizing use cases: Pydantic is suitable for scenarios with latency budgets over 10ms and throughput under 1,000 requests/sec, but alternatives should be sought for high-performance applications with lower latency and higher request rates. One such alternative is msgspec, which delivers 5-30 times faster performance by directly parsing JSON into Python objects without creating intermediate dictionaries, a common bottleneck in Pydantic. Benchmark tests showed that for both simple and complex data structures, msgspec drastically outperformed Pydantic, particularly emphasizing its efficiency with nested structures. Despite this speed advantage, Pydantic remains valuable for its comprehensive feature set, such as custom validation and ecosystem support, making it essential for many applications. The author hints at releasing further insights on Pydantic’s startup time and memory usage in future posts.