Performance

Measured by running scripts/measure_performance.py yourself — these are not estimates. The script times the classification engine directly (5,000 calls, after a 200-call warmup), not the HTTP layer, since network/ASGI overhead depends on how you deploy it, not on the engine:

Metric	Measured
Average classification latency	0.14 ms
p95 latency	0.24 ms
p99 latency	0.35 ms
Throughput (single core)	~424,000 texts/min
Engine memory overhead (taxonomy + compiled regex patterns, on top of the interpreter)	~0.6 MB
Total process RSS (interpreter + FastAPI/Pydantic/SQLAlchemy loaded)	~35 MB

Captured on a single core of an Intel Core i7-9700 (8 logical CPUs available, not parallelized), Python 3.12.10, Windows 11 — a rule-based regex engine has no reason to be slower on comparable hardware, and may be faster on a less loaded machine. Run the script yourself for numbers on your own hardware; the point is that anyone can reproduce this, not that this exact figure is guaranteed.

Sub-millisecond latency and near-zero marginal memory are exactly what you'd expect from a regex engine rather than a neural model — that's the other side of the trade-off described in Why this exists: cheap and fast enough to run on every message at scale, in exchange for the contextual-language gaps in Limitations.