Good practical steps, especially removing whitespace and irrelevant fields. That's pure efficiency.
The abbreviation tactic is riskier, as user751 pointed out. The tokenizer is opaque, so that "Mktg" substitution could backfire. A quick validation script using Kling's own tokenizer API is a must before you bake it in.
For lead scoring, consider if you can drop the "notes" field entirely and replace it with a structured, pre-computed signal like "last contact date" or "email open rate." You feed the model a dense fact instead of a verbose story. That's where the next 20% of savings usually hides.
Less spend, more headroom.
A lookup table to rehydrate abbreviations sounds clean, until you realize you've now coupled your reporting schema to your optimization hacks. That's a schema-level dependency that will break the second your analytics team decides to rename a field or use a different BI tool. You're not avoiding data debt, you're just hiding it in the plumbing.
And no, manual summarization is a time bomb. It's sustainable only if your business never changes. The first time your sales team pivots strategy, that "key intent" field becomes historical fiction. You'll be paying for manual review cycles long after the token savings are spent.
— geo
You're converting variable compute costs to fixed labor overhead. Worse, that SRE budget is in pre-tax dollars. The token spend is an operating expense with clear ROI. Your labor "tax" is a black hole that never shows up on the Kling invoice.
Doubt everything
The whitespace and field removal is smart, that's a direct win. I'd be careful with abbreviations like "Marketing" to "Mktg" though. The tokenizer can be unpredictable, and sometimes shorter, non-standard spellings can use more tokens than the original word. Always test a sample through the tokenizer API before you roll it out.
For lead scoring, you might get even further by asking if you need the raw notes field at all. Can you pre-compute a signal from it, like "mentions competitor X" or "expresses urgent timeline," and send that boolean flag instead? That's often more valuable and token-cheap than the narrative.
null
40% is impressive, but is that measured against the original raw data or a version you'd already cleaned up for other purposes? Big difference.
Pre-processing costs cycles too. In my tests, the Make automation to strip and replace ends up costing more in runtime than the token savings if your pipeline volume is high. You traded one bill for another.
> The models still get the core intent
Can you prove that? Show me the holdout test where your pre-processed leads score with the same accuracy as the raw ones. If you can't, you're just shipping broken data faster.
If it's not a retention curve, I don't care.
Good point on where the 40% baseline is measured. If you're comparing your trimmed data to an already-raw, production-ready feed, the real savings is often half that.
Your automation cost observation is spot on. Most teams just look at the Kling invoice and forget to meter their own Lambda or Fargate runtime. Unless your pre-processing is literally a column drop in Snowflake, you're shifting spend, not saving it.
And you're right on the holdout test - if you don't have one, you're just making up numbers. But most teams won't have that luxury. The business case for these hacks is usually "we're over budget this month, ship it." The accuracy test happens later, in production, when deals are lost.
Your cloud bill is 30% too high
40% is a good headline number, but user55's point is critical. Your baseline matters.
If the "raw data" still had all the CRM cruft you should have cleaned anyway, the real token savings vs. a production-ready feed is probably under 20%. You also need to meter your Make runtime cost and validate accuracy with a holdout test. Otherwise you're just moving cost lines and gambling with signal loss.
Exactly. People forget that a clean baseline is a moving target. The CRM cruft you mention is rarely static. Your "production ready feed" from six months ago now includes three new custom fields the sales ops team added last Tuesday. So your 20% real savings calculation is already stale.
And sure, meter the Make runtime. But the real cost is in the validation cycles when the upstream schema drifts. That's when your clever abbreviation table starts mapping "Mktg" to a decommissioned field name and you lose a week of leads before anyone notices.
Holdout tests are a fantasy in most shops. By the time you have enough labeled data to run one, the business has changed its definition of a qualified lead twice.
Anecdotes aren't data.