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Parallel Works has announced new AI governance and budget management capabilities for its Activate AI platform, enabling enterprises and government organizations to centrally manage, govern, and control AI usage across commercial and privately hosted large language models (LLMs) through a single unified gateway. The Activate AI Gateway addresses the challenge of uncontrolled token consumption by applying proven governance principles used for compute and storage. Designed for large enterprises, government/defense organizations, and HPC/research environments, the platform helps manage escalating AI usage costs. “Organizations are discovering that the future of AI will be defined as much by governance and economics as by the model itself,” said CEO Matthew Shaxted. “Token consumption is becoming fragmented and difficult to manage. Enterprises need centralized visibility, accountability, and financial controls.” The platform combines hybrid compute orchestration, GPU governance, Kubernetes management, and AI consumption governance—including token budgeting and chargebacks—within a single system. It connects commercial AI services and self-hosted LLMs via a vendor-neutral API gateway, supporting OpenAI-compatible providers, Anthropic, Azure OpenAI, AWS Bedrock, and private models, avoiding vendor lock-in. Key capabilities include: a unified virtual API gateway for public/private LLM access; real-time token usage, budget allocation, and reporting; organization-level governance tracking; AI resource chargeback and cost accounting; and single-pane-of-glass management integrated into existing governance. Chris Coker, VP of major accounts at FutureTech, noted: “Our customers demand stronger AI governance to deploy AI securely and at scale. The combination of token budgeting, usage visibility, and chargeback gives our clients the controls they need.” The Activate AI Gateway governance capabilities are currently deployed at FutureTech, supporting thousands of users and managing token consumption across complex AI workloads. “Enterprises want to expand AI access, but without governance, costs and risks spiral,” said Michael McQuade, director of engineering at Parallel Works. The new capabilities are available now for large enterprises, government/defense, HPC environments, and research institutions deploying private GPU infrastructure or consuming commercial AI APIs at scale.