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Used by AI teams and early‑stage founders managing $10K–$500K+ monthly AI spend to cut waste and protect runway.
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Most small AI teams are overpaying for every inference, every training run, and every experiment—usually by 2–10x. Not because the tech is bad, but be
Most startups run one large, expensive model for everything. That’s the fastest way to light money on fire. The fix: map each use case to the *smalles
Token bloat is a silent budget killer: oversized prompts, unnecessary history, and verbose responses multiply your costs every day. The fix: aggressiv
You are likely recomputing the same or similar work thousands of times—especially for read-heavy workloads like search, recommendations, and common qu
Even if your models are efficient, your deployment setup can quietly burn thousands per month through idle capacity and suboptimal hardware. The fix:
Unbounded experimentation and retraining are silent budget drains. A few long-running jobs or poorly scoped sweeps can burn more in a week than your p
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