This platform “sits between an organization’s people, applications, and agents, and every AI model the organization uses, giving enterprises one place to govern, secure, operate, and optimize AI,” the fresh-from-stealth company said in a Monday (Aug. 3) news release.
Actualyze integrates with any OpenAI-compatible models, “the ecosystem of AI client tools,” and third-party platforms, and is designed to ensure every request follows a “governed path” to let enterprises adopt and scale AI, the release added.
“It’s clear that AI has become a new layer of the enterprise stack,” said Rafi Khardalian, CEO and co-founder of Actualyze AI. “A model call looks like any other API request, a key, an SDK, an invoice at month’s end, but the resemblance is the trap.”
He added that the systems governing the remainder of an organization’s request traffic can authenticate a model call and count it, though it can’t read the prompt inside it, determine if data is leaking or the best place to route it, or charge the call to the team behind it.
“So anyone with a key can call a model, and all that spend pools into one bucket with no visibility into who spent it or accountability for it,” Khardalian said. “Agents raise the stakes, fanning a single task into dozens of autonomous calls. That’s the problem we built Actualyze to solve.”
The launch of this platform comes as enterprises are becoming much more diligent about their AI spending, as PYMNTS reported last week. This trend comes in the wake of a long period of “tokenmaxxing,” in which companies pushed workers toward the biggest AI models and the heaviest usage, as if consumption denoted progress.
That era “is ending after two years of unchecked growth,” the report said. “That approach worked while AI spending was small enough to absorb without much scrutiny. It no longer is.”
Enterprise software once used annual licenses and seat-based pricing that finance teams could predict with reasonable accuracy. AI, priced in tokens, compute cycles and application programming interface (API) calls, has shaken that model, with new tools being developed to address the issue.
For example, Ramp last month intrdouced AI Token Spend Management, giving finance teams a single dashboard to track, allocate and control AI spending across providers including OpenAI, Anthropic, Gemini and Cursor. The company noted in its announcement that AI token spend across its customer base increased 20.7x since June 2025.
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