OpenAI Slashes Model Prices, Targets Niche Industries to Accelerate Enterprise Growth

OpenAI is implementing significant price cuts and a refined strategy focused on specialized industries to accelerate its growth in the enterprise market, according to Chief Financial Officer Sarah Friar. Speaking Monday at Goldman Sachs’ Communacopia + Technology Conference, Friar outlined the company's push to compete with cheaper open-source models and address corporate demand for a clearer return on AI investments.

A Sharpened Enterprise Strategy

To counter the rise of lower-cost, open-weight AI systems, OpenAI has aggressively reduced the cost of its services. Friar noted that the company slashed pricing on its budget-tier Luna model by 80%, a move that she said helped increase its usage roughly tenfold.

OpenAI is also targeting specific high-value industries, including chip design, life sciences, and financial services, where businesses are seeking AI tools for specialized tasks rather than general-purpose applications. In a significant shift, Friar said the company is testing outcome-based pricing models, moving away from traditional usage-based fees to better align with corporate clients' desire to see a tangible payoff from their AI spending.

Accelerating Corporate Adoption

The strategy appears to be yielding results, with enterprise revenue outpacing the company's overall growth. According to Friar, enterprise revenue climbed 32% between June and July, compared to a 20% increase in overall annualized revenue during the same period. This growth has brought enterprise and consumer revenue to a roughly balanced state, achieving a year-end goal ahead of schedule.

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Friar highlighted the practical value of OpenAI's technology by citing the company's own success in designing its "Jalapeno" chip. Using its proprietary models, the chip design was completed and sent for manufacturing—a stage known as tape-out—in just nine months. She also noted that Codex, OpenAI's coding assistant, has grown to 25 million users.

The Competitive Landscape

OpenAI's strategic shift comes amid mounting pressure from rivals like Anthropic and a proliferation of open-source models, particularly from China, which are often perceived as more cost-effective. Friar directly addressed this, arguing that OpenAI's models can be cheaper to deploy at scale.

"If you’re deploying Luna and compare that to (Z.ai’s) GLM 5.3, for example, on a cloud layer, we are cheaper," she stated. This positions OpenAI to compete not just on performance but also on total cost of ownership for enterprise clients scrutinizing their technology budgets.