Instead of using massive, expensive LLMs for every task, companies can solve the "tokenpocalypse" (runaway token costs) by pairing smaller models with high-quality retrieval systems. This allows cheap models to act like large ones, saving significant costs.

Glean's co-founder argues that most enterprise tasks don't require expensive frontier models. Open-source alternatives are now capable enough for the vast majority of use cases. The primary adoption driver has shifted from data privacy to pure cost savings, as enterprises seek to control skyrocketing AI bills.

Jev's pricing is fundamentally different, charging only for input tokens at a very low rate ($0.04/million) and not for its minimal output. This economic advantage makes it feasible to run analysis on huge, unstructured datasets—like millions of pairwise comparisons—for just a few dollars, a task previously cost-prohibitive.

Jev processes tasks up to 400x cheaper than LLMs, with costs as low as cents for thousands of complex queries. This economic shift makes it feasible to analyze entire archives (emails, ads) for deep insights, a task previously too expensive or time-consuming.

Jev processes requests in milliseconds for a fraction of a cent (e.g., 1,700 emails for 18 cents). This combination of speed and low cost makes it viable for high-volume, real-time applications like instant lead scoring or support ticket routing, which are often prohibitively expensive with large language models.

The cost to achieve a specific performance benchmark dropped from $60 per million tokens with GPT-3 in 2021 to just $0.06 with Llama 3.2-3b in 2024. This dramatic cost reduction makes sophisticated AI economically viable for a wider range of enterprise applications, shifting the focus to on-premise solutions.

Judgment models like JEV make traditional ML techniques like classification and regression more accessible. Companies that currently use expensive LLMs for these tasks can now use a simpler, API-driven approach that is better suited for the job, without needing to build and host complex custom models from scratch.

While frontier models like Claude excel at analyzing a few complex documents, they are impractical for processing millions. Smaller, specialized, fine-tuned models offer orders of magnitude better cost and throughput, making them the superior choice for large-scale, repetitive extraction tasks.

YipitData had data on millions of companies but could only afford to process it for a few hundred public tickers due to high manual cleaning costs. AI and LLMs have now made it economically viable to tag and structure this messy, long-tail data at scale, creating massive new product opportunities.