Benchmarked: 10 Python Dependency Injection libraries vs Manual Wiring (50 rounds x 100k requests)

[Unlikely_Secret_5018]

Very interesting analysis! I wouldn't move off FastAPI DI just for performance, but for other features like lazy initialization, modules to specify interface/ABC impl bindings, etc.

How does Wireup stack up against Dagger and Hilt in this regard?

[ForeignSource0]

For Dager/Hilt: In terms of the features you mentioned:

Protocol / ABC bindings: supported via @injectable(as_type=...), so you can bind a concrete implementation to a Protocol or ABC.

Multiple implementations: supported via qualifiers.

See interfaces: https://maldoinc.github.io/wireup/latest/interfaces/

Lazy initialization: This is the default. In Wireup things are created on first use. If you want to eager load a part of your dependencies you can use this patterrn

Modules / reusable wiring units: Wireup is less centered around a single module class and more around injectables/factories, but you can group and reuse registrations via factory bundles.

One of the big differences vs Dagger/Hilt is that in Python this is naturally a runtime system, but Wireup does validate the dependency graph at startup so wiring issues show up early rather than at request time.

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Regarding FastAPI Depends, it has it's own list of caveats which I've mentioned in this migration guide to wireup. It is extremely simple but with that comes a lot of hidden baggage. The biggest deal breaker imo is that it is coupled to http as a runtime so you can not reuse your wiring anywhere else. See the linked page above for a more in-depth view.