A valid, yet normal pattern of writing decision logic typically would default to if/else statements:
if company == "anthropic":
print("Model: Opus 5")
elif company == "openai":
print("Model: GPT-5.6 Sol")
elif company = "google":
print("Model: Gemini 3.5")
else:
print("Not a valid company")Inherently, there isn’t anything wrong with this: you’re looking to make a decision based upon some kind of criteria. This is a situation where you’d use a if/else statement!
However, when scaling to multiple conditions, this can become cumbersome and tough to maintain - what if we wanted to include models such as Qwen, DeepSeek, or models that are developed locally/in-house?
Sure, you could expand upon what you have, but this becomes a problem because of maintenance.
The Registry Pattern
This is where the registry pattern comes into play. You can think of a registry as a single "place” where you store all of your if/else conditions and outputs. This “place” is normally a dictionary within your code.
So, instead of maintaining a bunch of if/else statements, you’d maintain this dictionary instead:
registry = {
"anthropic" : "Opus 5",
"openai" : "GPT-5.6 Sol",
"google" : "Gemini 3.5",
# ... add other companies/models here
}
# sample variable
company = "anthropic"
model = company.get("registry")
if not model:
print(f"{company} not in the registry")
else:
print(f"Model: {model}")Notice that instead of a bunch of if/else statements we only have one? This is because we’re checking for the existence of a company in a dictionary instead of checking against a variable.
This pattern has a number of advantages, not only from a maintenance perspective, but also from a scalability perspective. For instance, if our dictionary grows too large, we can refactor this to a JSON file and read it in during run-time.
For those who are interested in time complexity, using a registry is linear (O(1)), as dictionary lookup, adding keys, and removing is linear. On the converse, using if/else statements is at best linear (O(1)), but average case is linear (O(n)).
Thus, as the number of companies grows when using the if/else statements, the slower the code becomes.
Happy coding!
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