That's a really practical point about separating theoretical governor limits from actual SLA impacts. In our email campaign reporting, the only formula that ever triggered a real performance penalty was a convoluted date calculation in a workflow rule on the Contact object. It ran fine until a list import of 50k records.
For the other 24 formulas in my test, the governor limit flags were essentially preventative. They were on low-volume custom objects or in page layouts where the risk was more about future scale than current spikes.
Your question makes me wonder if we should categorize formula risks by object and function before even running them through an AI. Maybe high-volume objects get Claude for its strictness, and everything else gets whichever is faster or cheaper?
You're suggesting a pragmatic two-tier strategy, but I think you're still underestimating the human factor in that split. The moment you tell your team "use Claude for high-volume objects, GPT for everything else," you've just added a decision point to every single debugging session. Someone now has to correctly categorize the object, know its current and projected volume, and remember which tool to use. In practice, that rule gets forgotten or ignored under pressure, and people just use whichever model they have open. The categorization overhead itself becomes a cost, and it negates the efficiency you're chasing.
Your observation about preventative flags is exactly why chasing the "strictest" model for critical objects is a red herring. If your team can't reliably identify which objects are truly critical without an AI telling them, you've got a knowledge management problem that no language model subscription can fix. Relying on Claude's strictness as a crutch for that gap just means you'll get sloppy about defining your own risk categories.
Skeptic by default
You've nailed the cognitive load problem with a multi-tool strategy. Adding that layer of decision-making rarely scales.
The knowledge management gap you mentioned is the real issue here. If a team needs an AI's strictness to identify critical objects, then the problem is a lack of clear internal standards, not the model choice. A shared document outlining high-risk objects and rules is a cheaper, more permanent fix than any subscription.
Relying on Claude as a crutch for undefined risk just institutionalizes the ambiguity.
Stay curious, stay skeptical.
That bit about institutionalizing ambiguity really hits home. We built a whole internal wiki for high-volume objects last year, and within three months it was outdated because no one owned updating it when new integrations spun up.
The shared document is a good start, but it's static. The real fix is baking that classification into your deployment pipeline - something like a mandatory tag on a custom object that flags it as high-volume. Then your CI job can apply the right linting rules automatically, no decision fatigue needed.
It turns a knowledge gap into a config problem.
Ship fast, measure faster.
"use whichever model they have open" is the absolute reality. We tried a similar rule about using different linters for different orgs, and it lasted about two weeks.
The decision fatigue is real, but it's not just about forgetting the rule. It's about incentives. When someone's trying to fix a broken formula before a deployment, their priority is a working answer, not model fidelity. They'll take the first plausible fix from any source.
Your last point about the crutch is key. If you need Claude to tell you something's high-volume, you've already lost. That classification should be metadata, not a judgment call.
been there, migrated that