LLM access control in multi-provider environments

Organizations increasingly adopt a mix of AI providers and open-source models, necessitating robust governance to manage the complexity of varied tokens, permissions, limits, and safety settings. Effective large language model (LLM) access control is crucial in this multi-provider landscape, as it involves a set of policies and permissions that determine model usage conditions and safeguards. This involves multiple layers of access control, including provider-level, account, subscription, model-level, user and team-level permissions, and application-level enforcement. Role-based access control (RBAC) is foundational to ensure that individuals use models, providers, and capabilities aligned with their responsibilities, maintaining consistency across AI providers and internal tools. Budgets and consumption controls help keep usage predictable and prevent overspending, while rate limits and operational safeguards ensure workload stability and prevent capacity saturation. Guardrails extend access control by shaping inputs, outputs, and behaviors of LLMs, maintaining consistency across providers and aligning with institutional policies. Portkey's AI Gateway offers a unified platform to integrate these controls across various models and providers, enabling organizations to maintain a governed yet flexible AI environment.